Mercurial > repos > shellac > guppy_basecaller
annotate env/lib/python3.7/site-packages/boto/dynamodb2/table.py @ 2:6af9afd405e9 draft
"planemo upload commit 0a63dd5f4d38a1f6944587f52a8cd79874177fc1"
author | shellac |
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date | Thu, 14 May 2020 14:56:58 -0400 |
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1 import boto |
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2 from boto.dynamodb2 import exceptions |
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3 from boto.dynamodb2.fields import (HashKey, RangeKey, |
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4 AllIndex, KeysOnlyIndex, IncludeIndex, |
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5 GlobalAllIndex, GlobalKeysOnlyIndex, |
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6 GlobalIncludeIndex) |
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7 from boto.dynamodb2.items import Item |
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8 from boto.dynamodb2.layer1 import DynamoDBConnection |
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9 from boto.dynamodb2.results import ResultSet, BatchGetResultSet |
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10 from boto.dynamodb2.types import (NonBooleanDynamizer, Dynamizer, FILTER_OPERATORS, |
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11 QUERY_OPERATORS, STRING) |
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12 from boto.exception import JSONResponseError |
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13 |
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14 |
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15 class Table(object): |
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16 """ |
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17 Interacts & models the behavior of a DynamoDB table. |
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18 |
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19 The ``Table`` object represents a set (or rough categorization) of |
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20 records within DynamoDB. The important part is that all records within the |
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21 table, while largely-schema-free, share the same schema & are essentially |
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22 namespaced for use in your application. For example, you might have a |
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23 ``users`` table or a ``forums`` table. |
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24 """ |
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25 max_batch_get = 100 |
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26 |
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27 _PROJECTION_TYPE_TO_INDEX = dict( |
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28 global_indexes=dict( |
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29 ALL=GlobalAllIndex, |
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30 KEYS_ONLY=GlobalKeysOnlyIndex, |
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31 INCLUDE=GlobalIncludeIndex, |
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32 ), local_indexes=dict( |
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33 ALL=AllIndex, |
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34 KEYS_ONLY=KeysOnlyIndex, |
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35 INCLUDE=IncludeIndex, |
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36 ) |
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37 ) |
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38 |
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39 def __init__(self, table_name, schema=None, throughput=None, indexes=None, |
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40 global_indexes=None, connection=None): |
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41 """ |
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42 Sets up a new in-memory ``Table``. |
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43 |
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44 This is useful if the table already exists within DynamoDB & you simply |
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45 want to use it for additional interactions. The only required parameter |
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46 is the ``table_name``. However, under the hood, the object will call |
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47 ``describe_table`` to determine the schema/indexes/throughput. You |
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48 can avoid this extra call by passing in ``schema`` & ``indexes``. |
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49 |
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50 **IMPORTANT** - If you're creating a new ``Table`` for the first time, |
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51 you should use the ``Table.create`` method instead, as it will |
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52 persist the table structure to DynamoDB. |
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53 |
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54 Requires a ``table_name`` parameter, which should be a simple string |
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55 of the name of the table. |
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56 |
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57 Optionally accepts a ``schema`` parameter, which should be a list of |
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58 ``BaseSchemaField`` subclasses representing the desired schema. |
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59 |
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60 Optionally accepts a ``throughput`` parameter, which should be a |
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61 dictionary. If provided, it should specify a ``read`` & ``write`` key, |
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62 both of which should have an integer value associated with them. |
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63 |
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64 Optionally accepts a ``indexes`` parameter, which should be a list of |
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65 ``BaseIndexField`` subclasses representing the desired indexes. |
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66 |
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67 Optionally accepts a ``global_indexes`` parameter, which should be a |
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68 list of ``GlobalBaseIndexField`` subclasses representing the desired |
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69 indexes. |
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70 |
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71 Optionally accepts a ``connection`` parameter, which should be a |
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72 ``DynamoDBConnection`` instance (or subclass). This is primarily useful |
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73 for specifying alternate connection parameters. |
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74 |
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75 Example:: |
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76 |
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77 # The simple, it-already-exists case. |
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78 >>> conn = Table('users') |
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79 |
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80 # The full, minimum-extra-calls case. |
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81 >>> from boto import dynamodb2 |
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82 >>> users = Table('users', schema=[ |
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83 ... HashKey('username'), |
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84 ... RangeKey('date_joined', data_type=NUMBER) |
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85 ... ], throughput={ |
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86 ... 'read':20, |
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87 ... 'write': 10, |
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88 ... }, indexes=[ |
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89 ... KeysOnlyIndex('MostRecentlyJoined', parts=[ |
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90 ... HashKey('username') |
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91 ... RangeKey('date_joined') |
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92 ... ]), |
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93 ... ], global_indexes=[ |
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94 ... GlobalAllIndex('UsersByZipcode', parts=[ |
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95 ... HashKey('zipcode'), |
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96 ... RangeKey('username'), |
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97 ... ], |
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98 ... throughput={ |
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99 ... 'read':10, |
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100 ... 'write':10, |
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101 ... }), |
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102 ... ], connection=dynamodb2.connect_to_region('us-west-2', |
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103 ... aws_access_key_id='key', |
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104 ... aws_secret_access_key='key', |
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105 ... )) |
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106 |
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107 """ |
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108 self.table_name = table_name |
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109 self.connection = connection |
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110 self.throughput = { |
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111 'read': 5, |
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112 'write': 5, |
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113 } |
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114 self.schema = schema |
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115 self.indexes = indexes |
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116 self.global_indexes = global_indexes |
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117 |
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118 if self.connection is None: |
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119 self.connection = DynamoDBConnection() |
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120 |
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121 if throughput is not None: |
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122 self.throughput = throughput |
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123 |
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124 self._dynamizer = NonBooleanDynamizer() |
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125 |
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126 def use_boolean(self): |
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127 self._dynamizer = Dynamizer() |
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128 |
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129 @classmethod |
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130 def create(cls, table_name, schema, throughput=None, indexes=None, |
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131 global_indexes=None, connection=None): |
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132 """ |
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133 Creates a new table in DynamoDB & returns an in-memory ``Table`` object. |
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134 |
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135 This will setup a brand new table within DynamoDB. The ``table_name`` |
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136 must be unique for your AWS account. The ``schema`` is also required |
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137 to define the key structure of the table. |
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138 |
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139 **IMPORTANT** - You should consider the usage pattern of your table |
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140 up-front, as the schema can **NOT** be modified once the table is |
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141 created, requiring the creation of a new table & migrating the data |
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142 should you wish to revise it. |
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143 |
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144 **IMPORTANT** - If the table already exists in DynamoDB, additional |
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145 calls to this method will result in an error. If you just need |
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146 a ``Table`` object to interact with the existing table, you should |
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147 just initialize a new ``Table`` object, which requires only the |
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148 ``table_name``. |
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149 |
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150 Requires a ``table_name`` parameter, which should be a simple string |
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151 of the name of the table. |
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152 |
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153 Requires a ``schema`` parameter, which should be a list of |
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154 ``BaseSchemaField`` subclasses representing the desired schema. |
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155 |
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156 Optionally accepts a ``throughput`` parameter, which should be a |
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157 dictionary. If provided, it should specify a ``read`` & ``write`` key, |
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158 both of which should have an integer value associated with them. |
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159 |
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160 Optionally accepts a ``indexes`` parameter, which should be a list of |
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161 ``BaseIndexField`` subclasses representing the desired indexes. |
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162 |
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163 Optionally accepts a ``global_indexes`` parameter, which should be a |
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164 list of ``GlobalBaseIndexField`` subclasses representing the desired |
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165 indexes. |
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166 |
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167 Optionally accepts a ``connection`` parameter, which should be a |
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168 ``DynamoDBConnection`` instance (or subclass). This is primarily useful |
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169 for specifying alternate connection parameters. |
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170 |
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171 Example:: |
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172 |
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173 >>> users = Table.create('users', schema=[ |
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174 ... HashKey('username'), |
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175 ... RangeKey('date_joined', data_type=NUMBER) |
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176 ... ], throughput={ |
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177 ... 'read':20, |
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178 ... 'write': 10, |
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179 ... }, indexes=[ |
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180 ... KeysOnlyIndex('MostRecentlyJoined', parts=[ |
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181 ... HashKey('username'), |
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182 ... RangeKey('date_joined'), |
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183 ... ]), global_indexes=[ |
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184 ... GlobalAllIndex('UsersByZipcode', parts=[ |
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185 ... HashKey('zipcode'), |
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186 ... RangeKey('username'), |
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187 ... ], |
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188 ... throughput={ |
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189 ... 'read':10, |
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190 ... 'write':10, |
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191 ... }), |
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192 ... ]) |
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193 |
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194 """ |
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195 table = cls(table_name=table_name, connection=connection) |
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196 table.schema = schema |
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197 |
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198 if throughput is not None: |
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199 table.throughput = throughput |
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200 |
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201 if indexes is not None: |
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202 table.indexes = indexes |
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203 |
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204 if global_indexes is not None: |
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205 table.global_indexes = global_indexes |
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206 |
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207 # Prep the schema. |
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208 raw_schema = [] |
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209 attr_defs = [] |
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210 seen_attrs = set() |
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211 |
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212 for field in table.schema: |
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213 raw_schema.append(field.schema()) |
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214 # Build the attributes off what we know. |
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215 seen_attrs.add(field.name) |
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216 attr_defs.append(field.definition()) |
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217 |
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218 raw_throughput = { |
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219 'ReadCapacityUnits': int(table.throughput['read']), |
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220 'WriteCapacityUnits': int(table.throughput['write']), |
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221 } |
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222 kwargs = {} |
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223 |
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224 kwarg_map = { |
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225 'indexes': 'local_secondary_indexes', |
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226 'global_indexes': 'global_secondary_indexes', |
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227 } |
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228 for index_attr in ('indexes', 'global_indexes'): |
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229 table_indexes = getattr(table, index_attr) |
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230 if table_indexes: |
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231 raw_indexes = [] |
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232 for index_field in table_indexes: |
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233 raw_indexes.append(index_field.schema()) |
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234 # Make sure all attributes specified in the indexes are |
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235 # added to the definition |
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236 for field in index_field.parts: |
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237 if field.name not in seen_attrs: |
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238 seen_attrs.add(field.name) |
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239 attr_defs.append(field.definition()) |
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240 |
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241 kwargs[kwarg_map[index_attr]] = raw_indexes |
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242 |
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243 table.connection.create_table( |
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244 table_name=table.table_name, |
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245 attribute_definitions=attr_defs, |
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246 key_schema=raw_schema, |
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247 provisioned_throughput=raw_throughput, |
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248 **kwargs |
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249 ) |
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250 return table |
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251 |
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252 def _introspect_schema(self, raw_schema, raw_attributes=None): |
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253 """ |
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254 Given a raw schema structure back from a DynamoDB response, parse |
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255 out & build the high-level Python objects that represent them. |
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256 """ |
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257 schema = [] |
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258 sane_attributes = {} |
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259 |
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260 if raw_attributes: |
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261 for field in raw_attributes: |
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262 sane_attributes[field['AttributeName']] = field['AttributeType'] |
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263 |
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264 for field in raw_schema: |
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265 data_type = sane_attributes.get(field['AttributeName'], STRING) |
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266 |
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267 if field['KeyType'] == 'HASH': |
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268 schema.append( |
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269 HashKey(field['AttributeName'], data_type=data_type) |
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270 ) |
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271 elif field['KeyType'] == 'RANGE': |
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272 schema.append( |
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273 RangeKey(field['AttributeName'], data_type=data_type) |
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274 ) |
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275 else: |
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276 raise exceptions.UnknownSchemaFieldError( |
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277 "%s was seen, but is unknown. Please report this at " |
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278 "https://github.com/boto/boto/issues." % field['KeyType'] |
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279 ) |
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280 |
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281 return schema |
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282 |
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283 def _introspect_all_indexes(self, raw_indexes, map_indexes_projection): |
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284 """ |
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285 Given a raw index/global index structure back from a DynamoDB response, |
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286 parse out & build the high-level Python objects that represent them. |
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287 """ |
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288 indexes = [] |
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289 |
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290 for field in raw_indexes: |
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291 index_klass = map_indexes_projection.get('ALL') |
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292 kwargs = { |
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293 'parts': [] |
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294 } |
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295 |
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296 if field['Projection']['ProjectionType'] == 'ALL': |
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297 index_klass = map_indexes_projection.get('ALL') |
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298 elif field['Projection']['ProjectionType'] == 'KEYS_ONLY': |
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299 index_klass = map_indexes_projection.get('KEYS_ONLY') |
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300 elif field['Projection']['ProjectionType'] == 'INCLUDE': |
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301 index_klass = map_indexes_projection.get('INCLUDE') |
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302 kwargs['includes'] = field['Projection']['NonKeyAttributes'] |
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303 else: |
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304 raise exceptions.UnknownIndexFieldError( |
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305 "%s was seen, but is unknown. Please report this at " |
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306 "https://github.com/boto/boto/issues." % \ |
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307 field['Projection']['ProjectionType'] |
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308 ) |
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309 |
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310 name = field['IndexName'] |
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311 kwargs['parts'] = self._introspect_schema(field['KeySchema'], None) |
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312 indexes.append(index_klass(name, **kwargs)) |
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313 |
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314 return indexes |
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315 |
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316 def _introspect_indexes(self, raw_indexes): |
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317 """ |
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318 Given a raw index structure back from a DynamoDB response, parse |
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319 out & build the high-level Python objects that represent them. |
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320 """ |
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321 return self._introspect_all_indexes( |
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322 raw_indexes, self._PROJECTION_TYPE_TO_INDEX.get('local_indexes')) |
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323 |
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324 def _introspect_global_indexes(self, raw_global_indexes): |
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325 """ |
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326 Given a raw global index structure back from a DynamoDB response, parse |
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327 out & build the high-level Python objects that represent them. |
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328 """ |
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329 return self._introspect_all_indexes( |
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330 raw_global_indexes, |
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331 self._PROJECTION_TYPE_TO_INDEX.get('global_indexes')) |
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332 |
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333 def describe(self): |
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334 """ |
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335 Describes the current structure of the table in DynamoDB. |
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336 |
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337 This information will be used to update the ``schema``, ``indexes``, |
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338 ``global_indexes`` and ``throughput`` information on the ``Table``. Some |
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339 calls, such as those involving creating keys or querying, will require |
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340 this information to be populated. |
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341 |
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342 It also returns the full raw data structure from DynamoDB, in the |
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343 event you'd like to parse out additional information (such as the |
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344 ``ItemCount`` or usage information). |
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345 |
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346 Example:: |
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347 |
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348 >>> users.describe() |
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349 { |
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350 # Lots of keys here... |
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351 } |
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352 >>> len(users.schema) |
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353 2 |
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354 |
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355 """ |
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356 result = self.connection.describe_table(self.table_name) |
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357 |
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358 # Blindly update throughput, since what's on DynamoDB's end is likely |
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359 # more correct. |
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360 raw_throughput = result['Table']['ProvisionedThroughput'] |
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361 self.throughput['read'] = int(raw_throughput['ReadCapacityUnits']) |
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362 self.throughput['write'] = int(raw_throughput['WriteCapacityUnits']) |
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363 |
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364 if not self.schema: |
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365 # Since we have the data, build the schema. |
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366 raw_schema = result['Table'].get('KeySchema', []) |
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367 raw_attributes = result['Table'].get('AttributeDefinitions', []) |
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368 self.schema = self._introspect_schema(raw_schema, raw_attributes) |
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369 |
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370 if not self.indexes: |
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371 # Build the index information as well. |
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372 raw_indexes = result['Table'].get('LocalSecondaryIndexes', []) |
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373 self.indexes = self._introspect_indexes(raw_indexes) |
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374 |
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375 # Build the global index information as well. |
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376 raw_global_indexes = result['Table'].get('GlobalSecondaryIndexes', []) |
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377 self.global_indexes = self._introspect_global_indexes(raw_global_indexes) |
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378 |
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379 # This is leaky. |
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380 return result |
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381 |
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382 def update(self, throughput=None, global_indexes=None): |
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383 """ |
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384 Updates table attributes and global indexes in DynamoDB. |
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385 |
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386 Optionally accepts a ``throughput`` parameter, which should be a |
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387 dictionary. If provided, it should specify a ``read`` & ``write`` key, |
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388 both of which should have an integer value associated with them. |
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389 |
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390 Optionally accepts a ``global_indexes`` parameter, which should be a |
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391 dictionary. If provided, it should specify the index name, which is also |
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392 a dict containing a ``read`` & ``write`` key, both of which |
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393 should have an integer value associated with them. If you are writing |
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394 new code, please use ``Table.update_global_secondary_index``. |
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395 |
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396 Returns ``True`` on success. |
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397 |
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398 Example:: |
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399 |
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400 # For a read-heavier application... |
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401 >>> users.update(throughput={ |
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402 ... 'read': 20, |
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403 ... 'write': 10, |
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404 ... }) |
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405 True |
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406 |
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407 # To also update the global index(es) throughput. |
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408 >>> users.update(throughput={ |
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409 ... 'read': 20, |
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410 ... 'write': 10, |
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411 ... }, |
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412 ... global_secondary_indexes={ |
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413 ... 'TheIndexNameHere': { |
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414 ... 'read': 15, |
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415 ... 'write': 5, |
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416 ... } |
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417 ... }) |
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418 True |
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419 """ |
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420 |
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421 data = None |
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422 |
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423 if throughput: |
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424 self.throughput = throughput |
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425 data = { |
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426 'ReadCapacityUnits': int(self.throughput['read']), |
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427 'WriteCapacityUnits': int(self.throughput['write']), |
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428 } |
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429 |
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430 gsi_data = None |
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431 |
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432 if global_indexes: |
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433 gsi_data = [] |
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434 |
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435 for gsi_name, gsi_throughput in global_indexes.items(): |
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436 gsi_data.append({ |
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437 "Update": { |
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438 "IndexName": gsi_name, |
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439 "ProvisionedThroughput": { |
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440 "ReadCapacityUnits": int(gsi_throughput['read']), |
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441 "WriteCapacityUnits": int(gsi_throughput['write']), |
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442 }, |
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443 }, |
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444 }) |
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445 |
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446 if throughput or global_indexes: |
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447 self.connection.update_table( |
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448 self.table_name, |
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449 provisioned_throughput=data, |
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450 global_secondary_index_updates=gsi_data, |
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451 ) |
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452 |
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453 return True |
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454 else: |
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455 msg = 'You need to provide either the throughput or the ' \ |
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456 'global_indexes to update method' |
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457 boto.log.error(msg) |
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458 |
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459 return False |
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460 |
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461 def create_global_secondary_index(self, global_index): |
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462 """ |
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463 Creates a global index in DynamoDB after the table has been created. |
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464 |
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465 Requires a ``global_indexes`` parameter, which should be a |
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466 ``GlobalBaseIndexField`` subclass representing the desired index. |
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467 |
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468 To update ``global_indexes`` information on the ``Table``, you'll need |
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469 to call ``Table.describe``. |
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470 |
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471 Returns ``True`` on success. |
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472 |
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473 Example:: |
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474 |
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475 # To create a global index |
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476 >>> users.create_global_secondary_index( |
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477 ... global_index=GlobalAllIndex( |
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478 ... 'TheIndexNameHere', parts=[ |
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479 ... HashKey('requiredHashkey', data_type=STRING), |
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480 ... RangeKey('optionalRangeKey', data_type=STRING) |
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481 ... ], |
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482 ... throughput={ |
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483 ... 'read': 2, |
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484 ... 'write': 1, |
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485 ... }) |
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486 ... ) |
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487 True |
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488 |
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489 """ |
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490 |
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491 if global_index: |
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492 gsi_data = [] |
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493 gsi_data_attr_def = [] |
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494 |
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495 gsi_data.append({ |
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496 "Create": global_index.schema() |
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497 }) |
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498 |
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499 for attr_def in global_index.parts: |
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500 gsi_data_attr_def.append(attr_def.definition()) |
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501 |
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502 self.connection.update_table( |
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503 self.table_name, |
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504 global_secondary_index_updates=gsi_data, |
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505 attribute_definitions=gsi_data_attr_def |
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506 ) |
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507 |
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508 return True |
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509 else: |
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510 msg = 'You need to provide the global_index to ' \ |
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511 'create_global_secondary_index method' |
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512 boto.log.error(msg) |
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513 |
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514 return False |
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515 |
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516 def delete_global_secondary_index(self, global_index_name): |
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517 """ |
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518 Deletes a global index in DynamoDB after the table has been created. |
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519 |
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520 Requires a ``global_index_name`` parameter, which should be a simple |
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521 string of the name of the global secondary index. |
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522 |
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523 To update ``global_indexes`` information on the ``Table``, you'll need |
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524 to call ``Table.describe``. |
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525 |
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526 Returns ``True`` on success. |
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527 |
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528 Example:: |
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529 |
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530 # To delete a global index |
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531 >>> users.delete_global_secondary_index('TheIndexNameHere') |
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532 True |
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533 |
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534 """ |
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535 |
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536 if global_index_name: |
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537 gsi_data = [ |
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538 { |
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539 "Delete": { |
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540 "IndexName": global_index_name |
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541 } |
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542 } |
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543 ] |
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544 |
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545 self.connection.update_table( |
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546 self.table_name, |
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547 global_secondary_index_updates=gsi_data, |
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548 ) |
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549 |
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550 return True |
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551 else: |
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552 msg = 'You need to provide the global index name to ' \ |
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553 'delete_global_secondary_index method' |
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554 boto.log.error(msg) |
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555 |
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556 return False |
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557 |
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558 def update_global_secondary_index(self, global_indexes): |
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559 """ |
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560 Updates a global index(es) in DynamoDB after the table has been created. |
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561 |
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562 Requires a ``global_indexes`` parameter, which should be a |
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563 dictionary. If provided, it should specify the index name, which is also |
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564 a dict containing a ``read`` & ``write`` key, both of which |
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565 should have an integer value associated with them. |
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566 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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567 To update ``global_indexes`` information on the ``Table``, you'll need |
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568 to call ``Table.describe``. |
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569 |
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570 Returns ``True`` on success. |
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571 |
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572 Example:: |
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573 |
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574 # To update a global index |
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575 >>> users.update_global_secondary_index(global_indexes={ |
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576 ... 'TheIndexNameHere': { |
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577 ... 'read': 15, |
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578 ... 'write': 5, |
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579 ... } |
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580 ... }) |
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581 True |
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582 |
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583 """ |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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584 |
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585 if global_indexes: |
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586 gsi_data = [] |
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587 |
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588 for gsi_name, gsi_throughput in global_indexes.items(): |
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589 gsi_data.append({ |
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590 "Update": { |
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591 "IndexName": gsi_name, |
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592 "ProvisionedThroughput": { |
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593 "ReadCapacityUnits": int(gsi_throughput['read']), |
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594 "WriteCapacityUnits": int(gsi_throughput['write']), |
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595 }, |
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596 }, |
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597 }) |
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598 |
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599 self.connection.update_table( |
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600 self.table_name, |
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601 global_secondary_index_updates=gsi_data, |
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602 ) |
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603 return True |
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604 else: |
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605 msg = 'You need to provide the global indexes to ' \ |
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606 'update_global_secondary_index method' |
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607 boto.log.error(msg) |
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608 |
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609 return False |
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610 |
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611 def delete(self): |
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612 """ |
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613 Deletes a table in DynamoDB. |
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614 |
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615 **IMPORTANT** - Be careful when using this method, there is no undo. |
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616 |
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617 Returns ``True`` on success. |
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618 |
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619 Example:: |
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620 |
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621 >>> users.delete() |
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622 True |
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623 |
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624 """ |
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625 self.connection.delete_table(self.table_name) |
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626 return True |
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627 |
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628 def _encode_keys(self, keys): |
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629 """ |
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630 Given a flat Python dictionary of keys/values, converts it into the |
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631 nested dictionary DynamoDB expects. |
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632 |
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633 Converts:: |
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634 |
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635 { |
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636 'username': 'john', |
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637 'tags': [1, 2, 5], |
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638 } |
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639 |
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640 ...to...:: |
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641 |
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642 { |
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643 'username': {'S': 'john'}, |
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644 'tags': {'NS': ['1', '2', '5']}, |
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645 } |
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646 |
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647 """ |
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648 raw_key = {} |
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649 |
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650 for key, value in keys.items(): |
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651 raw_key[key] = self._dynamizer.encode(value) |
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652 |
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653 return raw_key |
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654 |
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655 def get_item(self, consistent=False, attributes=None, **kwargs): |
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656 """ |
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657 Fetches an item (record) from a table in DynamoDB. |
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658 |
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659 To specify the key of the item you'd like to get, you can specify the |
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660 key attributes as kwargs. |
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661 |
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662 Optionally accepts a ``consistent`` parameter, which should be a |
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663 boolean. If you provide ``True``, it will perform |
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664 a consistent (but more expensive) read from DynamoDB. |
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665 (Default: ``False``) |
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666 |
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667 Optionally accepts an ``attributes`` parameter, which should be a |
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668 list of fieldname to fetch. (Default: ``None``, which means all fields |
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669 should be fetched) |
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670 |
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671 Returns an ``Item`` instance containing all the data for that record. |
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672 |
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673 Raises an ``ItemNotFound`` exception if the item is not found. |
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674 |
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675 Example:: |
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676 |
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677 # A simple hash key. |
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678 >>> john = users.get_item(username='johndoe') |
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679 >>> john['first_name'] |
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680 'John' |
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681 |
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682 # A complex hash+range key. |
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683 >>> john = users.get_item(username='johndoe', last_name='Doe') |
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684 >>> john['first_name'] |
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685 'John' |
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686 |
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687 # A consistent read (assuming the data might have just changed). |
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688 >>> john = users.get_item(username='johndoe', consistent=True) |
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689 >>> john['first_name'] |
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690 'Johann' |
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691 |
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692 # With a key that is an invalid variable name in Python. |
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693 # Also, assumes a different schema than previous examples. |
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694 >>> john = users.get_item(**{ |
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695 ... 'date-joined': 127549192, |
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696 ... }) |
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697 >>> john['first_name'] |
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698 'John' |
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699 |
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700 """ |
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701 raw_key = self._encode_keys(kwargs) |
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702 item_data = self.connection.get_item( |
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703 self.table_name, |
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704 raw_key, |
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705 attributes_to_get=attributes, |
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706 consistent_read=consistent |
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|
707 ) |
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|
708 if 'Item' not in item_data: |
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709 raise exceptions.ItemNotFound("Item %s couldn't be found." % kwargs) |
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710 item = Item(self) |
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711 item.load(item_data) |
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712 return item |
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713 |
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714 def has_item(self, **kwargs): |
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715 """ |
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|
716 Return whether an item (record) exists within a table in DynamoDB. |
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717 |
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718 To specify the key of the item you'd like to get, you can specify the |
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719 key attributes as kwargs. |
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720 |
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721 Optionally accepts a ``consistent`` parameter, which should be a |
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722 boolean. If you provide ``True``, it will perform |
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723 a consistent (but more expensive) read from DynamoDB. |
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724 (Default: ``False``) |
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725 |
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726 Optionally accepts an ``attributes`` parameter, which should be a |
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727 list of fieldnames to fetch. (Default: ``None``, which means all fields |
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728 should be fetched) |
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729 |
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730 Returns ``True`` if an ``Item`` is present, ``False`` if not. |
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731 |
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732 Example:: |
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733 |
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734 # Simple, just hash-key schema. |
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735 >>> users.has_item(username='johndoe') |
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736 True |
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737 |
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738 # Complex schema, item not present. |
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739 >>> users.has_item( |
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740 ... username='johndoe', |
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741 ... date_joined='2014-01-07' |
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742 ... ) |
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743 False |
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744 |
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745 """ |
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746 try: |
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747 self.get_item(**kwargs) |
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748 except (JSONResponseError, exceptions.ItemNotFound): |
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749 return False |
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750 |
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751 return True |
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752 |
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753 def lookup(self, *args, **kwargs): |
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754 """ |
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755 Look up an entry in DynamoDB. This is mostly backwards compatible |
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756 with boto.dynamodb. Unlike get_item, it takes hash_key and range_key first, |
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757 although you may still specify keyword arguments instead. |
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758 |
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759 Also unlike the get_item command, if the returned item has no keys |
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760 (i.e., it does not exist in DynamoDB), a None result is returned, instead |
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761 of an empty key object. |
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762 |
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763 Example:: |
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764 >>> user = users.lookup(username) |
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765 >>> user = users.lookup(username, consistent=True) |
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766 >>> app = apps.lookup('my_customer_id', 'my_app_id') |
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767 |
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768 """ |
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769 if not self.schema: |
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770 self.describe() |
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771 for x, arg in enumerate(args): |
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772 kwargs[self.schema[x].name] = arg |
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773 ret = self.get_item(**kwargs) |
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774 if not ret.keys(): |
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775 return None |
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776 return ret |
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777 |
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778 def new_item(self, *args): |
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779 """ |
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780 Returns a new, blank item |
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781 |
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782 This is mostly for consistency with boto.dynamodb |
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783 """ |
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784 if not self.schema: |
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785 self.describe() |
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786 data = {} |
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787 for x, arg in enumerate(args): |
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788 data[self.schema[x].name] = arg |
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789 return Item(self, data=data) |
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790 |
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791 def put_item(self, data, overwrite=False): |
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792 """ |
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793 Saves an entire item to DynamoDB. |
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794 |
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795 By default, if any part of the ``Item``'s original data doesn't match |
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796 what's currently in DynamoDB, this request will fail. This prevents |
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797 other processes from updating the data in between when you read the |
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798 item & when your request to update the item's data is processed, which |
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799 would typically result in some data loss. |
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800 |
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801 Requires a ``data`` parameter, which should be a dictionary of the data |
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802 you'd like to store in DynamoDB. |
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803 |
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804 Optionally accepts an ``overwrite`` parameter, which should be a |
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805 boolean. If you provide ``True``, this will tell DynamoDB to blindly |
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806 overwrite whatever data is present, if any. |
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807 |
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808 Returns ``True`` on success. |
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809 |
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810 Example:: |
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811 |
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812 >>> users.put_item(data={ |
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813 ... 'username': 'jane', |
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814 ... 'first_name': 'Jane', |
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815 ... 'last_name': 'Doe', |
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816 ... 'date_joined': 126478915, |
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817 ... }) |
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818 True |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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|
819 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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820 """ |
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|
821 item = Item(self, data=data) |
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822 return item.save(overwrite=overwrite) |
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823 |
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824 def _put_item(self, item_data, expects=None): |
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825 """ |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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826 The internal variant of ``put_item`` (full data). This is used by the |
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827 ``Item`` objects, since that operation is represented at the |
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828 table-level by the API, but conceptually maps better to telling an |
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829 individual ``Item`` to save itself. |
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830 """ |
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831 kwargs = {} |
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832 |
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833 if expects is not None: |
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834 kwargs['expected'] = expects |
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835 |
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836 self.connection.put_item(self.table_name, item_data, **kwargs) |
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837 return True |
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838 |
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839 def _update_item(self, key, item_data, expects=None): |
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840 """ |
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841 The internal variant of ``put_item`` (partial data). This is used by the |
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842 ``Item`` objects, since that operation is represented at the |
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843 table-level by the API, but conceptually maps better to telling an |
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844 individual ``Item`` to save itself. |
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845 """ |
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846 raw_key = self._encode_keys(key) |
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847 kwargs = {} |
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848 |
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849 if expects is not None: |
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850 kwargs['expected'] = expects |
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851 |
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852 self.connection.update_item(self.table_name, raw_key, item_data, **kwargs) |
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853 return True |
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854 |
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855 def delete_item(self, expected=None, conditional_operator=None, **kwargs): |
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856 """ |
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857 Deletes a single item. You can perform a conditional delete operation |
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858 that deletes the item if it exists, or if it has an expected attribute |
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859 value. |
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860 |
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861 Conditional deletes are useful for only deleting items if specific |
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862 conditions are met. If those conditions are met, DynamoDB performs |
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863 the delete. Otherwise, the item is not deleted. |
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864 |
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865 To specify the expected attribute values of the item, you can pass a |
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866 dictionary of conditions to ``expected``. Each condition should follow |
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867 the pattern ``<attributename>__<comparison_operator>=<value_to_expect>``. |
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868 |
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869 **IMPORTANT** - Be careful when using this method, there is no undo. |
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870 |
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871 To specify the key of the item you'd like to get, you can specify the |
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872 key attributes as kwargs. |
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873 |
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874 Optionally accepts an ``expected`` parameter which is a dictionary of |
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875 expected attribute value conditions. |
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876 |
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877 Optionally accepts a ``conditional_operator`` which applies to the |
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878 expected attribute value conditions: |
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879 |
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880 + `AND` - If all of the conditions evaluate to true (default) |
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881 + `OR` - True if at least one condition evaluates to true |
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882 |
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883 Returns ``True`` on success, ``False`` on failed conditional delete. |
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884 |
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885 Example:: |
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886 |
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887 # A simple hash key. |
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888 >>> users.delete_item(username='johndoe') |
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889 True |
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890 |
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891 # A complex hash+range key. |
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892 >>> users.delete_item(username='jane', last_name='Doe') |
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893 True |
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894 |
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895 # With a key that is an invalid variable name in Python. |
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896 # Also, assumes a different schema than previous examples. |
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897 >>> users.delete_item(**{ |
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898 ... 'date-joined': 127549192, |
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899 ... }) |
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900 True |
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901 |
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902 # Conditional delete |
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903 >>> users.delete_item(username='johndoe', |
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904 ... expected={'balance__eq': 0}) |
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905 True |
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906 """ |
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907 expected = self._build_filters(expected, using=FILTER_OPERATORS) |
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908 raw_key = self._encode_keys(kwargs) |
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909 |
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910 try: |
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911 self.connection.delete_item(self.table_name, raw_key, |
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912 expected=expected, |
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913 conditional_operator=conditional_operator) |
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914 except exceptions.ConditionalCheckFailedException: |
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915 return False |
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916 |
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917 return True |
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918 |
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919 def get_key_fields(self): |
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920 """ |
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921 Returns the fields necessary to make a key for a table. |
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922 |
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923 If the ``Table`` does not already have a populated ``schema``, |
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924 this will request it via a ``Table.describe`` call. |
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925 |
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926 Returns a list of fieldnames (strings). |
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927 |
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928 Example:: |
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|
929 |
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930 # A simple hash key. |
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|
931 >>> users.get_key_fields() |
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932 ['username'] |
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933 |
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|
934 # A complex hash+range key. |
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935 >>> users.get_key_fields() |
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936 ['username', 'last_name'] |
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937 |
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|
938 """ |
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939 if not self.schema: |
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940 # We don't know the structure of the table. Get a description to |
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941 # populate the schema. |
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942 self.describe() |
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943 |
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944 return [field.name for field in self.schema] |
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945 |
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946 def batch_write(self): |
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947 """ |
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948 Allows the batching of writes to DynamoDB. |
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949 |
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950 Since each write/delete call to DynamoDB has a cost associated with it, |
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951 when loading lots of data, it makes sense to batch them, creating as |
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952 few calls as possible. |
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953 |
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954 This returns a context manager that will transparently handle creating |
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955 these batches. The object you get back lightly-resembles a ``Table`` |
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956 object, sharing just the ``put_item`` & ``delete_item`` methods |
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957 (which are all that DynamoDB can batch in terms of writing data). |
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958 |
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959 DynamoDB's maximum batch size is 25 items per request. If you attempt |
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960 to put/delete more than that, the context manager will batch as many |
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961 as it can up to that number, then flush them to DynamoDB & continue |
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962 batching as more calls come in. |
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963 |
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964 Example:: |
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965 |
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966 # Assuming a table with one record... |
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967 >>> with users.batch_write() as batch: |
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968 ... batch.put_item(data={ |
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969 ... 'username': 'johndoe', |
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970 ... 'first_name': 'John', |
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971 ... 'last_name': 'Doe', |
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972 ... 'owner': 1, |
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973 ... }) |
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974 ... # Nothing across the wire yet. |
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975 ... batch.delete_item(username='bob') |
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976 ... # Still no requests sent. |
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977 ... batch.put_item(data={ |
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978 ... 'username': 'jane', |
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979 ... 'first_name': 'Jane', |
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980 ... 'last_name': 'Doe', |
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981 ... 'date_joined': 127436192, |
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982 ... }) |
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983 ... # Nothing yet, but once we leave the context, the |
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984 ... # put/deletes will be sent. |
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985 |
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986 """ |
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987 # PHENOMENAL COSMIC DOCS!!! itty-bitty code. |
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988 return BatchTable(self) |
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989 |
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990 def _build_filters(self, filter_kwargs, using=QUERY_OPERATORS): |
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991 """ |
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992 An internal method for taking query/scan-style ``**kwargs`` & turning |
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993 them into the raw structure DynamoDB expects for filtering. |
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994 """ |
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995 if filter_kwargs is None: |
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996 return |
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997 |
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998 filters = {} |
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999 |
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1000 for field_and_op, value in filter_kwargs.items(): |
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1001 field_bits = field_and_op.split('__') |
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1002 fieldname = '__'.join(field_bits[:-1]) |
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1003 |
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1004 try: |
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1005 op = using[field_bits[-1]] |
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1006 except KeyError: |
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1007 raise exceptions.UnknownFilterTypeError( |
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1008 "Operator '%s' from '%s' is not recognized." % ( |
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1009 field_bits[-1], |
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1010 field_and_op |
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1011 ) |
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1012 ) |
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1013 |
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1014 lookup = { |
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1015 'AttributeValueList': [], |
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1016 'ComparisonOperator': op, |
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1017 } |
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1018 |
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1019 # Special-case the ``NULL/NOT_NULL`` case. |
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1020 if field_bits[-1] == 'null': |
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1021 del lookup['AttributeValueList'] |
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1022 |
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1023 if value is False: |
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1024 lookup['ComparisonOperator'] = 'NOT_NULL' |
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1025 else: |
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1026 lookup['ComparisonOperator'] = 'NULL' |
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1027 # Special-case the ``BETWEEN`` case. |
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1028 elif field_bits[-1] == 'between': |
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1029 if len(value) == 2 and isinstance(value, (list, tuple)): |
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1030 lookup['AttributeValueList'].append( |
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1031 self._dynamizer.encode(value[0]) |
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1032 ) |
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1033 lookup['AttributeValueList'].append( |
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1034 self._dynamizer.encode(value[1]) |
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1035 ) |
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1036 # Special-case the ``IN`` case |
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1037 elif field_bits[-1] == 'in': |
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1038 for val in value: |
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1039 lookup['AttributeValueList'].append(self._dynamizer.encode(val)) |
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1040 else: |
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1041 # Fix up the value for encoding, because it was built to only work |
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1042 # with ``set``s. |
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1043 if isinstance(value, (list, tuple)): |
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1044 value = set(value) |
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1045 lookup['AttributeValueList'].append( |
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1046 self._dynamizer.encode(value) |
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1047 ) |
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1048 |
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1049 # Finally, insert it into the filters. |
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1050 filters[fieldname] = lookup |
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1051 |
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1052 return filters |
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1053 |
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1054 def query(self, limit=None, index=None, reverse=False, consistent=False, |
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1055 attributes=None, max_page_size=None, **filter_kwargs): |
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1056 """ |
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1057 **WARNING:** This method is provided **strictly** for |
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1058 backward-compatibility. It returns results in an incorrect order. |
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1059 |
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1060 If you are writing new code, please use ``Table.query_2``. |
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1061 """ |
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1062 reverse = not reverse |
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1063 return self.query_2(limit=limit, index=index, reverse=reverse, |
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1064 consistent=consistent, attributes=attributes, |
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1065 max_page_size=max_page_size, **filter_kwargs) |
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1066 |
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1067 def query_2(self, limit=None, index=None, reverse=False, |
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1068 consistent=False, attributes=None, max_page_size=None, |
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1069 query_filter=None, conditional_operator=None, |
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1070 **filter_kwargs): |
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1071 """ |
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1072 Queries for a set of matching items in a DynamoDB table. |
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1073 |
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1074 Queries can be performed against a hash key, a hash+range key or |
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1075 against any data stored in your local secondary indexes. Query filters |
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1076 can be used to filter on arbitrary fields. |
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1077 |
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1078 **Note** - You can not query against arbitrary fields within the data |
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1079 stored in DynamoDB unless you specify ``query_filter`` values. |
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1080 |
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1081 To specify the filters of the items you'd like to get, you can specify |
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1082 the filters as kwargs. Each filter kwarg should follow the pattern |
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1083 ``<fieldname>__<filter_operation>=<value_to_look_for>``. Query filters |
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1084 are specified in the same way. |
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1085 |
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1086 Optionally accepts a ``limit`` parameter, which should be an integer |
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1087 count of the total number of items to return. (Default: ``None`` - |
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1088 all results) |
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1089 |
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1090 Optionally accepts an ``index`` parameter, which should be a string of |
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1091 name of the local secondary index you want to query against. |
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1092 (Default: ``None``) |
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1093 |
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1094 Optionally accepts a ``reverse`` parameter, which will present the |
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1095 results in reverse order. (Default: ``False`` - normal order) |
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1096 |
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1097 Optionally accepts a ``consistent`` parameter, which should be a |
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1098 boolean. If you provide ``True``, it will force a consistent read of |
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1099 the data (more expensive). (Default: ``False`` - use eventually |
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1100 consistent reads) |
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1101 |
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1102 Optionally accepts a ``attributes`` parameter, which should be a |
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1103 tuple. If you provide any attributes only these will be fetched |
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1104 from DynamoDB. This uses the ``AttributesToGet`` and set's |
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1105 ``Select`` to ``SPECIFIC_ATTRIBUTES`` API. |
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1106 |
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1107 Optionally accepts a ``max_page_size`` parameter, which should be an |
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1108 integer count of the maximum number of items to retrieve |
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1109 **per-request**. This is useful in making faster requests & prevent |
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1110 the scan from drowning out other queries. (Default: ``None`` - |
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1111 fetch as many as DynamoDB will return) |
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1112 |
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1113 Optionally accepts a ``query_filter`` which is a dictionary of filter |
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1114 conditions against any arbitrary field in the returned data. |
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1115 |
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1116 Optionally accepts a ``conditional_operator`` which applies to the |
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1117 query filter conditions: |
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1118 |
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1119 + `AND` - True if all filter conditions evaluate to true (default) |
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1120 + `OR` - True if at least one filter condition evaluates to true |
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1121 |
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1122 Returns a ``ResultSet`` containing ``Item``s, which transparently handles the pagination of |
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1123 results you get back. |
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1124 |
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1125 Example:: |
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1126 |
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1127 # Look for last names equal to "Doe". |
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1128 >>> results = users.query(last_name__eq='Doe') |
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1129 >>> for res in results: |
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1130 ... print res['first_name'] |
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1131 'John' |
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1132 'Jane' |
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1133 |
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1134 # Look for last names beginning with "D", in reverse order, limit 3. |
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1135 >>> results = users.query( |
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1136 ... last_name__beginswith='D', |
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1137 ... reverse=True, |
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1138 ... limit=3 |
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1139 ... ) |
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1140 >>> for res in results: |
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1141 ... print res['first_name'] |
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1142 'Alice' |
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1143 'Jane' |
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1144 'John' |
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1145 |
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1146 # Use an LSI & a consistent read. |
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1147 >>> results = users.query( |
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1148 ... date_joined__gte=1236451000, |
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1149 ... owner__eq=1, |
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1150 ... index='DateJoinedIndex', |
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1151 ... consistent=True |
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1152 ... ) |
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1153 >>> for res in results: |
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1154 ... print res['first_name'] |
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1155 'Alice' |
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1156 'Bob' |
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1157 'John' |
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1158 'Fred' |
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1159 |
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1160 # Filter by non-indexed field(s) |
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1161 >>> results = users.query( |
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1162 ... last_name__eq='Doe', |
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1163 ... reverse=True, |
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1164 ... query_filter={ |
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1165 ... 'first_name__beginswith': 'A' |
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1166 ... } |
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1167 ... ) |
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1168 >>> for res in results: |
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1169 ... print res['first_name'] + ' ' + res['last_name'] |
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1170 'Alice Doe' |
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1171 |
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1172 """ |
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1173 if self.schema: |
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1174 if len(self.schema) == 1: |
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1175 if len(filter_kwargs) <= 1: |
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1176 if not self.global_indexes or not len(self.global_indexes): |
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1177 # If the schema only has one field, there's <= 1 filter |
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1178 # param & no Global Secondary Indexes, this is user |
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1179 # error. Bail early. |
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1180 raise exceptions.QueryError( |
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1181 "You must specify more than one key to filter on." |
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1182 ) |
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1183 |
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1184 if attributes is not None: |
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1185 select = 'SPECIFIC_ATTRIBUTES' |
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1186 else: |
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1187 select = None |
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1188 |
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1189 results = ResultSet( |
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1190 max_page_size=max_page_size |
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1191 ) |
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1192 kwargs = filter_kwargs.copy() |
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1193 kwargs.update({ |
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1194 'limit': limit, |
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1195 'index': index, |
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1196 'reverse': reverse, |
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1197 'consistent': consistent, |
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1198 'select': select, |
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1199 'attributes_to_get': attributes, |
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1200 'query_filter': query_filter, |
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1201 'conditional_operator': conditional_operator, |
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1202 }) |
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1203 results.to_call(self._query, **kwargs) |
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1204 return results |
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1205 |
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1206 def query_count(self, index=None, consistent=False, conditional_operator=None, |
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1207 query_filter=None, scan_index_forward=True, limit=None, |
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1208 exclusive_start_key=None, **filter_kwargs): |
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1209 """ |
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1210 Queries the exact count of matching items in a DynamoDB table. |
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1211 |
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1212 Queries can be performed against a hash key, a hash+range key or |
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1213 against any data stored in your local secondary indexes. Query filters |
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1214 can be used to filter on arbitrary fields. |
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1215 |
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1216 To specify the filters of the items you'd like to get, you can specify |
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1217 the filters as kwargs. Each filter kwarg should follow the pattern |
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1218 ``<fieldname>__<filter_operation>=<value_to_look_for>``. Query filters |
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1219 are specified in the same way. |
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1220 |
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1221 Optionally accepts an ``index`` parameter, which should be a string of |
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1222 name of the local secondary index you want to query against. |
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1223 (Default: ``None``) |
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1224 |
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1225 Optionally accepts a ``consistent`` parameter, which should be a |
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1226 boolean. If you provide ``True``, it will force a consistent read of |
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1227 the data (more expensive). (Default: ``False`` - use eventually |
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1228 consistent reads) |
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1229 |
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1230 Optionally accepts a ``query_filter`` which is a dictionary of filter |
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1231 conditions against any arbitrary field in the returned data. |
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1232 |
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1233 Optionally accepts a ``conditional_operator`` which applies to the |
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1234 query filter conditions: |
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1235 |
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1236 + `AND` - True if all filter conditions evaluate to true (default) |
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1237 + `OR` - True if at least one filter condition evaluates to true |
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1238 |
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1239 Optionally accept a ``exclusive_start_key`` which is used to get |
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1240 the remaining items when a query cannot return the complete count. |
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1241 |
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1242 Returns an integer which represents the exact amount of matched |
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1243 items. |
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1244 |
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1245 :type scan_index_forward: boolean |
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1246 :param scan_index_forward: Specifies ascending (true) or descending |
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1247 (false) traversal of the index. DynamoDB returns results reflecting |
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1248 the requested order determined by the range key. If the data type |
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1249 is Number, the results are returned in numeric order. For String, |
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1250 the results are returned in order of ASCII character code values. |
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1251 For Binary, DynamoDB treats each byte of the binary data as |
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1252 unsigned when it compares binary values. |
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1253 |
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1254 If ScanIndexForward is not specified, the results are returned in |
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1255 ascending order. |
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1256 |
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1257 :type limit: integer |
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1258 :param limit: The maximum number of items to evaluate (not necessarily |
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1259 the number of matching items). |
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1260 |
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1261 Example:: |
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1262 |
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1263 # Look for last names equal to "Doe". |
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1264 >>> users.query_count(last_name__eq='Doe') |
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1265 5 |
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1266 |
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1267 # Use an LSI & a consistent read. |
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1268 >>> users.query_count( |
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1269 ... date_joined__gte=1236451000, |
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1270 ... owner__eq=1, |
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1271 ... index='DateJoinedIndex', |
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1272 ... consistent=True |
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1273 ... ) |
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1274 2 |
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1275 |
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1276 """ |
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1277 key_conditions = self._build_filters( |
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1278 filter_kwargs, |
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1279 using=QUERY_OPERATORS |
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1280 ) |
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1281 |
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1282 built_query_filter = self._build_filters( |
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1283 query_filter, |
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1284 using=FILTER_OPERATORS |
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1285 ) |
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1286 |
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1287 count_buffer = 0 |
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1288 last_evaluated_key = exclusive_start_key |
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1289 |
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1290 while True: |
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1291 raw_results = self.connection.query( |
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1292 self.table_name, |
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1293 index_name=index, |
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1294 consistent_read=consistent, |
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1295 select='COUNT', |
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1296 key_conditions=key_conditions, |
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1297 query_filter=built_query_filter, |
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1298 conditional_operator=conditional_operator, |
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1299 limit=limit, |
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1300 scan_index_forward=scan_index_forward, |
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1301 exclusive_start_key=last_evaluated_key |
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1302 ) |
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1303 |
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1304 count_buffer += int(raw_results.get('Count', 0)) |
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1305 last_evaluated_key = raw_results.get('LastEvaluatedKey') |
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1306 if not last_evaluated_key or count_buffer < 1: |
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1307 break |
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1308 |
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1309 return count_buffer |
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1310 |
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1311 def _query(self, limit=None, index=None, reverse=False, consistent=False, |
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1312 exclusive_start_key=None, select=None, attributes_to_get=None, |
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1313 query_filter=None, conditional_operator=None, **filter_kwargs): |
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1314 """ |
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1315 The internal method that performs the actual queries. Used extensively |
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1316 by ``ResultSet`` to perform each (paginated) request. |
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1317 """ |
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1318 kwargs = { |
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1319 'limit': limit, |
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1320 'index_name': index, |
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1321 'consistent_read': consistent, |
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1322 'select': select, |
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1323 'attributes_to_get': attributes_to_get, |
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1324 'conditional_operator': conditional_operator, |
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1325 } |
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1326 |
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1327 if reverse: |
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1328 kwargs['scan_index_forward'] = False |
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1329 |
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1330 if exclusive_start_key: |
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1331 kwargs['exclusive_start_key'] = {} |
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1332 |
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1333 for key, value in exclusive_start_key.items(): |
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1334 kwargs['exclusive_start_key'][key] = \ |
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1335 self._dynamizer.encode(value) |
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1336 |
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1337 # Convert the filters into something we can actually use. |
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1338 kwargs['key_conditions'] = self._build_filters( |
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1339 filter_kwargs, |
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1340 using=QUERY_OPERATORS |
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1341 ) |
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1342 |
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1343 kwargs['query_filter'] = self._build_filters( |
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1344 query_filter, |
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1345 using=FILTER_OPERATORS |
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1346 ) |
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1347 |
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1348 raw_results = self.connection.query( |
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1349 self.table_name, |
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1350 **kwargs |
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1351 ) |
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1352 results = [] |
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1353 last_key = None |
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1354 |
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1355 for raw_item in raw_results.get('Items', []): |
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1356 item = Item(self) |
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1357 item.load({ |
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1358 'Item': raw_item, |
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1359 }) |
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1360 results.append(item) |
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1361 |
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1362 if raw_results.get('LastEvaluatedKey', None): |
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1363 last_key = {} |
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1364 |
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1365 for key, value in raw_results['LastEvaluatedKey'].items(): |
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1366 last_key[key] = self._dynamizer.decode(value) |
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1367 |
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1368 return { |
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1369 'results': results, |
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1370 'last_key': last_key, |
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1371 } |
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1372 |
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1373 def scan(self, limit=None, segment=None, total_segments=None, |
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1374 max_page_size=None, attributes=None, conditional_operator=None, |
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1375 **filter_kwargs): |
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1376 """ |
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1377 Scans across all items within a DynamoDB table. |
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1378 |
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1379 Scans can be performed against a hash key or a hash+range key. You can |
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1380 additionally filter the results after the table has been read but |
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1381 before the response is returned by using query filters. |
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1382 |
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1383 To specify the filters of the items you'd like to get, you can specify |
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1384 the filters as kwargs. Each filter kwarg should follow the pattern |
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1385 ``<fieldname>__<filter_operation>=<value_to_look_for>``. |
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1386 |
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1387 Optionally accepts a ``limit`` parameter, which should be an integer |
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1388 count of the total number of items to return. (Default: ``None`` - |
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1389 all results) |
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1390 |
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1391 Optionally accepts a ``segment`` parameter, which should be an integer |
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1392 of the segment to retrieve on. Please see the documentation about |
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1393 Parallel Scans (Default: ``None`` - no segments) |
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1394 |
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1395 Optionally accepts a ``total_segments`` parameter, which should be an |
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1396 integer count of number of segments to divide the table into. |
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1397 Please see the documentation about Parallel Scans (Default: ``None`` - |
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1398 no segments) |
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1399 |
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1400 Optionally accepts a ``max_page_size`` parameter, which should be an |
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1401 integer count of the maximum number of items to retrieve |
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1402 **per-request**. This is useful in making faster requests & prevent |
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1403 the scan from drowning out other queries. (Default: ``None`` - |
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1404 fetch as many as DynamoDB will return) |
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1405 |
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1406 Optionally accepts an ``attributes`` parameter, which should be a |
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1407 tuple. If you provide any attributes only these will be fetched |
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1408 from DynamoDB. This uses the ``AttributesToGet`` and set's |
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1409 ``Select`` to ``SPECIFIC_ATTRIBUTES`` API. |
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1410 |
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1411 Returns a ``ResultSet``, which transparently handles the pagination of |
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1412 results you get back. |
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1413 |
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1414 Example:: |
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1415 |
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1416 # All results. |
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1417 >>> everything = users.scan() |
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1418 |
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1419 # Look for last names beginning with "D". |
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1420 >>> results = users.scan(last_name__beginswith='D') |
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1421 >>> for res in results: |
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1422 ... print res['first_name'] |
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1423 'Alice' |
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1424 'John' |
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1425 'Jane' |
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1426 |
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1427 # Use an ``IN`` filter & limit. |
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1428 >>> results = users.scan( |
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1429 ... age__in=[25, 26, 27, 28, 29], |
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1430 ... limit=1 |
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1431 ... ) |
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1432 >>> for res in results: |
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1433 ... print res['first_name'] |
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1434 'Alice' |
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1435 |
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1436 """ |
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1437 results = ResultSet( |
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1438 max_page_size=max_page_size |
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1439 ) |
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1440 kwargs = filter_kwargs.copy() |
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1441 kwargs.update({ |
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1442 'limit': limit, |
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1443 'segment': segment, |
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1444 'total_segments': total_segments, |
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1445 'attributes': attributes, |
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1446 'conditional_operator': conditional_operator, |
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1447 }) |
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1448 results.to_call(self._scan, **kwargs) |
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1449 return results |
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1450 |
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1451 def _scan(self, limit=None, exclusive_start_key=None, segment=None, |
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1452 total_segments=None, attributes=None, conditional_operator=None, |
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1453 **filter_kwargs): |
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1454 """ |
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1455 The internal method that performs the actual scan. Used extensively |
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1456 by ``ResultSet`` to perform each (paginated) request. |
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1457 """ |
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1458 kwargs = { |
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1459 'limit': limit, |
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1460 'segment': segment, |
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1461 'total_segments': total_segments, |
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1462 'attributes_to_get': attributes, |
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1463 'conditional_operator': conditional_operator, |
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1464 } |
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1465 |
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1466 if exclusive_start_key: |
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1467 kwargs['exclusive_start_key'] = {} |
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1468 |
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1469 for key, value in exclusive_start_key.items(): |
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1470 kwargs['exclusive_start_key'][key] = \ |
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1471 self._dynamizer.encode(value) |
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1472 |
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1473 # Convert the filters into something we can actually use. |
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1474 kwargs['scan_filter'] = self._build_filters( |
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1475 filter_kwargs, |
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1476 using=FILTER_OPERATORS |
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1477 ) |
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1478 |
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1479 raw_results = self.connection.scan( |
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1480 self.table_name, |
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1481 **kwargs |
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1482 ) |
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1483 results = [] |
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1484 last_key = None |
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1485 |
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1486 for raw_item in raw_results.get('Items', []): |
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1487 item = Item(self) |
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1488 item.load({ |
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1489 'Item': raw_item, |
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1490 }) |
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1491 results.append(item) |
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1492 |
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1493 if raw_results.get('LastEvaluatedKey', None): |
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1494 last_key = {} |
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1495 |
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1496 for key, value in raw_results['LastEvaluatedKey'].items(): |
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1497 last_key[key] = self._dynamizer.decode(value) |
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1498 |
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1499 return { |
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1500 'results': results, |
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1501 'last_key': last_key, |
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1502 } |
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1503 |
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1504 def batch_get(self, keys, consistent=False, attributes=None): |
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1505 """ |
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1506 Fetches many specific items in batch from a table. |
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1507 |
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1508 Requires a ``keys`` parameter, which should be a list of dictionaries. |
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1509 Each dictionary should consist of the keys values to specify. |
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1510 |
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1511 Optionally accepts a ``consistent`` parameter, which should be a |
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1512 boolean. If you provide ``True``, a strongly consistent read will be |
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1513 used. (Default: False) |
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1514 |
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1515 Optionally accepts an ``attributes`` parameter, which should be a |
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1516 tuple. If you provide any attributes only these will be fetched |
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1517 from DynamoDB. |
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1518 |
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1519 Returns a ``ResultSet``, which transparently handles the pagination of |
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1520 results you get back. |
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1521 |
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1522 Example:: |
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1523 |
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1524 >>> results = users.batch_get(keys=[ |
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1525 ... { |
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1526 ... 'username': 'johndoe', |
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1527 ... }, |
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1528 ... { |
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1529 ... 'username': 'jane', |
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1530 ... }, |
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1531 ... { |
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1532 ... 'username': 'fred', |
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1533 ... }, |
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1534 ... ]) |
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1535 >>> for res in results: |
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1536 ... print res['first_name'] |
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1537 'John' |
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1538 'Jane' |
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1539 'Fred' |
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1540 |
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1541 """ |
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1542 # We pass the keys to the constructor instead, so it can maintain it's |
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1543 # own internal state as to what keys have been processed. |
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1544 results = BatchGetResultSet(keys=keys, max_batch_get=self.max_batch_get) |
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1545 results.to_call(self._batch_get, consistent=consistent, attributes=attributes) |
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1546 return results |
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1547 |
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1548 def _batch_get(self, keys, consistent=False, attributes=None): |
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1549 """ |
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1550 The internal method that performs the actual batch get. Used extensively |
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1551 by ``BatchGetResultSet`` to perform each (paginated) request. |
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1552 """ |
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1553 items = { |
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1554 self.table_name: { |
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1555 'Keys': [], |
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1556 }, |
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1557 } |
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1558 |
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1559 if consistent: |
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1560 items[self.table_name]['ConsistentRead'] = True |
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1561 |
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1562 if attributes is not None: |
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1563 items[self.table_name]['AttributesToGet'] = attributes |
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1564 |
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1565 for key_data in keys: |
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1566 raw_key = {} |
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1567 |
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1568 for key, value in key_data.items(): |
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1569 raw_key[key] = self._dynamizer.encode(value) |
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1570 |
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1571 items[self.table_name]['Keys'].append(raw_key) |
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1572 |
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1573 raw_results = self.connection.batch_get_item(request_items=items) |
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1574 results = [] |
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1575 unprocessed_keys = [] |
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1576 |
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1577 for raw_item in raw_results['Responses'].get(self.table_name, []): |
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1578 item = Item(self) |
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1579 item.load({ |
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1580 'Item': raw_item, |
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1581 }) |
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1582 results.append(item) |
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1583 |
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1584 raw_unprocessed = raw_results.get('UnprocessedKeys', {}).get(self.table_name, {}) |
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1585 |
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1586 for raw_key in raw_unprocessed.get('Keys', []): |
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1587 py_key = {} |
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1588 |
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1589 for key, value in raw_key.items(): |
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1590 py_key[key] = self._dynamizer.decode(value) |
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1591 |
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1592 unprocessed_keys.append(py_key) |
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1593 |
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1594 return { |
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1595 'results': results, |
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1596 # NEVER return a ``last_key``. Just in-case any part of |
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1597 # ``ResultSet`` peeks through, since much of the |
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1598 # original underlying implementation is based on this key. |
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1599 'last_key': None, |
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1600 'unprocessed_keys': unprocessed_keys, |
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1601 } |
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1602 |
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1603 def count(self): |
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1604 """ |
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1605 Returns a (very) eventually consistent count of the number of items |
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1606 in a table. |
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1607 |
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1608 Lag time is about 6 hours, so don't expect a high degree of accuracy. |
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1609 |
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1610 Example:: |
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1611 |
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1612 >>> users.count() |
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1613 6 |
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1614 |
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1615 """ |
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1616 info = self.describe() |
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1617 return info['Table'].get('ItemCount', 0) |
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1618 |
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1619 |
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1620 class BatchTable(object): |
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1621 """ |
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1622 Used by ``Table`` as the context manager for batch writes. |
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1623 |
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1624 You likely don't want to try to use this object directly. |
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1625 """ |
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1626 def __init__(self, table): |
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1627 self.table = table |
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1628 self._to_put = [] |
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1629 self._to_delete = [] |
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1630 self._unprocessed = [] |
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1631 |
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1632 def __enter__(self): |
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1633 return self |
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1634 |
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1635 def __exit__(self, type, value, traceback): |
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1636 if self._to_put or self._to_delete: |
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1637 # Flush anything that's left. |
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1638 self.flush() |
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1639 |
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1640 if self._unprocessed: |
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1641 # Finally, handle anything that wasn't processed. |
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1642 self.resend_unprocessed() |
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1643 |
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1644 def put_item(self, data, overwrite=False): |
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1645 self._to_put.append(data) |
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1646 |
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1647 if self.should_flush(): |
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1648 self.flush() |
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1649 |
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1650 def delete_item(self, **kwargs): |
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1651 self._to_delete.append(kwargs) |
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1652 |
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1653 if self.should_flush(): |
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1654 self.flush() |
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1655 |
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1656 def should_flush(self): |
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1657 if len(self._to_put) + len(self._to_delete) == 25: |
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1658 return True |
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1659 |
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1660 return False |
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1661 |
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1662 def flush(self): |
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1663 batch_data = { |
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1664 self.table.table_name: [ |
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1665 # We'll insert data here shortly. |
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1666 ], |
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1667 } |
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1668 |
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1669 for put in self._to_put: |
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1670 item = Item(self.table, data=put) |
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1671 batch_data[self.table.table_name].append({ |
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1672 'PutRequest': { |
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1673 'Item': item.prepare_full(), |
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1674 } |
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1675 }) |
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1676 |
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1677 for delete in self._to_delete: |
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1678 batch_data[self.table.table_name].append({ |
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1679 'DeleteRequest': { |
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1680 'Key': self.table._encode_keys(delete), |
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1681 } |
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1682 }) |
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1683 |
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1684 resp = self.table.connection.batch_write_item(batch_data) |
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1685 self.handle_unprocessed(resp) |
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1686 |
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1687 self._to_put = [] |
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1688 self._to_delete = [] |
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1689 return True |
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1690 |
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1691 def handle_unprocessed(self, resp): |
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1692 if len(resp.get('UnprocessedItems', [])): |
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1693 table_name = self.table.table_name |
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1694 unprocessed = resp['UnprocessedItems'].get(table_name, []) |
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1695 |
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1696 # Some items have not been processed. Stow them for now & |
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1697 # re-attempt processing on ``__exit__``. |
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1698 msg = "%s items were unprocessed. Storing for later." |
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1699 boto.log.info(msg % len(unprocessed)) |
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1700 self._unprocessed.extend(unprocessed) |
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1701 |
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1702 def resend_unprocessed(self): |
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1703 # If there are unprocessed records (for instance, the user was over |
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1704 # their throughput limitations), iterate over them & send until they're |
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1705 # all there. |
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1706 boto.log.info( |
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1707 "Re-sending %s unprocessed items." % len(self._unprocessed) |
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1708 ) |
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1709 |
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1710 while len(self._unprocessed): |
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1711 # Again, do 25 at a time. |
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1712 to_resend = self._unprocessed[:25] |
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1713 # Remove them from the list. |
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1714 self._unprocessed = self._unprocessed[25:] |
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1715 batch_data = { |
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1716 self.table.table_name: to_resend |
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1717 } |
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1718 boto.log.info("Sending %s items" % len(to_resend)) |
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1719 resp = self.table.connection.batch_write_item(batch_data) |
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1720 self.handle_unprocessed(resp) |
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1721 boto.log.info( |
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1722 "%s unprocessed items left" % len(self._unprocessed) |
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1723 ) |