Tablestore:Aggregation

The aggregation method that can be used to return the minimum value of a field. This method can be used in a similar manner as the SQL MIN function.

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  field	The name of the field that is used to perform the aggregation operation. Only the LONG, DOUBLE, and DATE types are supported.
  missing	The default value for the field that is used to perform the aggregation operation on a row when the field value is empty. If you do not specify a value for missing, the row is ignored. If you specify a value for missing, the value of this parameter is used as the field value of the row.

• Example

  The following code provides an example on how to use aggregation to obtain the lowest score from the scores of people aged 18:

  query = TermQuery('age', 18)
  agg = Min('score', name = 'min')
  
  search_response = client.search(table_name, index_name,
                                  SearchQuery(query, next_token = None, limit=0, aggs=[agg]),
                                  columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for agg_result in search_response.agg_results:
      print('{\n"name":"%s",\n"value":%s\n}\n' % (agg_result.name, str(agg_result.value)))

The aggregation method that can be used to return the maximum value of a field. This method can be used in a similar manner as the SQL MAX function.

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  field	The name of the field that is used to perform the aggregation operation. Only the LONG, DOUBLE, and DATE types are supported.
  missing	The default value for the field that is used to perform the aggregation operation on a row when the field value is empty. If you do not specify a value for missing, the row is ignored. If you specify a value for missing, the value of this parameter is used as the field value of the row.

• Example

  The following code provides an example on how to use aggregation to obtain the highest score from the scores of people aged 18. By default, if a person has no score, a value of 0 is used as the score for aggregation.

  query = TermQuery('age', 18)
  agg = Max('score', missing_value = 0, name = 'max')
  
  search_response = client.search(table_name, index_name,
                                  SearchQuery(query, next_token = None, limit=0, aggs=[agg]),
                                  columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for agg_result in search_response.agg_results:
      print('{\n"name":"%s",\n"value":%s\n}\n' % (agg_result.name, str(agg_result.value)))

The aggregation method that can be used to return the sum of all values for a numeric field. This method can be used in a similar manner as the SQL SUM function.

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  field	The name of the field that is used to perform the aggregation operation. Only the LONG and DOUBLE types are supported.
  missing	The default value for the field that is used to perform the aggregation operation on a row when the field value is empty. If you do not specify a value for missing, the row is ignored. If you specify a value for missing, the value of this parameter is used as the field value of the row.

• Example

  The following code provides an example on how to use aggregation to obtain the sum of the scores of people aged 18:

  query = TermQuery('age', 18)
  agg = Sum('score', name = 'sum')
  
  search_response = client.search(table_name, index_name,
                                  SearchQuery(query, next_token = None, limit=2, aggs=[agg]),
                                  columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for agg_result in search_response.agg_results:
      print('{\n"name":"%s",\n"value":%s\n}\n' % (agg_result.name, str(agg_result.value)))

The aggregation method that can be used to return the average of all values for a numeric field. This method is used in a similar manner as the SQL AVG function.

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  field	The name of the field that is used to perform the aggregation operation. Only the LONG, DOUBLE, and DATE types are supported.
  missing	The default value for the field that is used to perform the aggregation operation on a row when the field value is empty. If you do not specify a value for missing, the row is ignored. If you specify a value for missing, the value of this parameter is used as the field value of the row.

• Example

  The following code provides an example on how to use aggregation to obtain the average score of people aged 18:

  query = TermQuery('age', 18)
  agg = Avg('score', name = 'avg')
  
  search_response = client.search(table_name, index_name,
                                  SearchQuery(query, next_token = None, limit=2, aggs=[agg]),
                                  columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for agg_result in search_response.agg_results:
      print('{\n"name":"%s",\n"value":%s\n}\n' % (agg_result.name, str(agg_result.value)))

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  field	The name of the field that is used to perform the aggregation operation. Only the LONG, DOUBLE, BOOLEAN, KEYWORD, GEO_POINT, and DATE types are supported.

• Example

  The following code provides an example on how to use aggregation to obtain the number of people aged 18 who have scores:

  
  query = TermQuery('age', 18)
  agg = Count('score', name = 'count')
  
  search_response = client.search(table_name, index_name,
                                  SearchQuery(query, next_token = None, limit=2, aggs=[agg]),
                                  columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for agg_result in search_response.agg_results:
      print('{\n"name":"%s",\n"value":%s\n}\n' % (agg_result.name, str(agg_result.value)))

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  field	The name of the field that is used to perform the aggregation operation. Only the LONG, DOUBLE, BOOLEAN, KEYWORD, GEO_POINT, and DATE types are supported.
  missing	The default value for the field that is used to perform the aggregation operation on a row when the field value is empty. If you do not specify a value for missing, the row is ignored. If you specify a value for missing, the value of this parameter is used as the field value of the row.

• Example

  The following code provides an example on how to use aggregation to obtain the number of unique names of people aged 18:

  query = TermQuery('age', 18)
  agg = DistinctCount('name', name = 'distinct_name')
  
  search_response = client.search(table_name, index_name,
                                  SearchQuery(query, next_token = None, limit=2, aggs=[agg]),
                                  columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for agg_result in search_response.agg_results:
      print('{\n"name":"%s",\n"value":%s\n}\n' % (agg_result.name, str(agg_result.value)))

A percentile value indicates the relative position of a value in a dataset. For example, when you collect statistics for the response time of each request during the routine O&M of your system, you must analyze the response time distribution by using percentiles such as p25, p50, p90, and p99.

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  field	The name of the field that is used to perform the aggregation operation. Only the LONG, DOUBLE, and DATE types are supported.
  percentiles	The percentiles such as p50, p90, and p99. You can specify one or more percentiles.
  missing_value	The default value for the field that is used to perform the aggregation operation on a row when the field value is empty. If you do not specify a value for missing_value, the row is ignored. If you specify a value for missing_value, the value of this parameter is used as the field value of the row.

• Example

  query = TermQuery('product', '10010')
  agg = Percentiles('latency', percentiles_list = [50, 90, 95])
  
  search_response = client.search(table_name, index_name,
                                  SearchQuery(query, next_token = None, limit=2, aggs=[agg]),
                                  columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for agg_result in search_response.agg_results:
      print('percentiles:%s' % agg_result.name)
      for item in agg_result.value:
          print('%s:%s' % (str(item.key), str(item.value)))

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  field	The name of the field that is used to perform the aggregation operation. Only the LONG, DOUBLE, BOOLEAN, KEYWORD, and DATE types are supported.
  size	The number of groups that you want to return. Default value: 10. Maximum value: 2000. If the number of groups exceeds 2,000, only the first 2,000 groups are returned.
  group_by_sort	The sorting rules for groups. By default, groups are sorted based on the number of items in the groups in descending order. If you configure multiple sorting rules, the groups are sorted based on the order in which the rules are configured. Supported parameters: Sort by value in alphabetical order. Sort by value in reverse alphabetical order. Sort by row count in ascending order. Sort by row count in descending order. Sort by the values that are obtained from sub-aggregation results in ascending order. Sort by the values that are obtained from sub-aggregation results in descending order.
  sub_aggs and sub_group_bys	The sub-aggregation operation. You can perform the sub-aggregation operation based on the grouping results. Scenario Query the number of products in each category, and the maximum and minimum product prices in each category. Method Group query results by product category to obtain the number of products in each category. Then, perform two sub-aggregation operations to obtain the maximum and minimum product prices in each category. Sample result Fruits: 5. The maximum price is CNY 15. The minimum price is CNY 3. Toiletries: 10. The maximum price is CNY 98. The minimum price is CNY 1. Electronic devices: 3. The maximum price is CNY 8,699. The minimum price is CNY 2,300. Other products: 15. The maximum price is CNY 1,000. The minimum price is CNY 80.

• Example 1

  The following code provides an example on how to group people aged 18 by score and obtain the top 10 common scores and the number of people in each group:

  query = TermQuery('age', 18)
  group_by = GroupByField('score', size = 10)
  
  search_response = client.search(table_name, index_name,
                                  SearchQuery(query, next_token = None, limit=20, group_bys = [group_by]),
                                  columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for group_by in search_response.group_by_results:
      print("name:%s" % group_by.name)
      print("groups:")
      for item in group_by.items:
          print("key:%s, count:%d" % (item.key, item.row_count))

• Example 2

  The following code provides an example on how to group people aged 18 by score and obtain the two least common scores and the number of people in each group:

  group_by = GroupByField('score', size = 2, group_by_sort = [RowCountSort(sort_order=SortOrder.ASC)])
  search_response = client.search(table_name, index_name,
                                  SearchQuery(TermQuery('age', 18), limit=100, get_total_count=True, group_bys = [group_by]),
                                  ColumnsToGet(return_type=ColumnReturnType.ALL_FROM_INDEX))
  
  for group_by in search_response.group_by_results:
      print("name:%s" % group_by.name)
      print("groups:")
      for item in group_by.items:
          print("key:%s, count:%d" % (item.key, item.row_count))

• Example 3

  The following code provides an example on how to group people aged 18 by score and obtain the top two common scores and the number of people in each group. Then, sort each group of people by primary key and obtain the information of the top three people in each group:

  sort = RowCountSort(sort_order = SortOrder.DESC)
  sub_agg = [TopRows(limit=3,sort=Sort([PrimaryKeySort(sort_order=SortOrder.DESC)]), name = 't1')]
  
  group_by = GroupByField('l', size = 2, group_by_sort = [sort], sub_aggs = sub_agg)
  search_response = client.search(table_name, index_name,
                                  SearchQuery(TermQuery('age', 18), limit=100, get_total_count=True, group_bys = [group_by]),
                                  ColumnsToGet(return_type=ColumnReturnType.ALL_FROM_INDEX))
  
  for group_by in search_response.group_by_results:
      print("name:%s" % group_by.name)
      print("groups:")
      for item in group_by.items:
          print("\tkey:%s, count:%d" % (item.key, item.row_count))
          for sub_agg in item.sub_aggs:
              print("\t\tname:%s:" % sub_agg.name)
              for entry in sub_agg.value:
                  print("\t\t\tvalue:%s" % str(entry))

• Example 4

  The following code provides an example on how to group people aged 18 by score and gender:

  sort = RowCountSort(sort_order = SortOrder.ASC)
  sub_group = GroupByField('sex', size = 10, group_by_sort = [sort])
  
  group_by = GroupByField('score', size = 10, group_by_sort = [sort], sub_group_bys = [sub_group])
  search_response = client.search(table_name, index_name,
                                  SearchQuery(TermQuery('age', 18), limit=100, get_total_count=True, group_bys = [group_by]),
                                  ColumnsToGet(return_type=ColumnReturnType.ALL_FROM_INDEX))
  
  for group_by in search_response.group_by_results:
      print("name:%s" % group_by.name)
      print("groups:")
      for item in group_by.items:
          print("\tkey:%s, count:%d" % (item.key, item.row_count))
          for sub_group in item.sub_group_bys:
              print("\t\tname:%s:" % sub_group.name)
              for sub_item in sub_group.items:
                  print("\t\t\tkey:%s, count:%s" % (str(sub_item.key), str(sub_item.row_count)))

The aggregation method that can be used to group query results based on the value ranges of a field. Field values that are within a specified range are grouped together. The number of values in each range is returned.

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  field	The name of the field that is used to perform the aggregation operation. Only the LONG and DOUBLE types are supported.
  range[double_from, double_to)	The value ranges for grouping. The value range can start from Double.MIN_VALUE and end with Double.MAX_VALUE.
  sub_aggs and sub_group_bys	The sub-aggregation operation. You can perform the sub-aggregation operation based on the grouping results. For example, after you group query results by sales volume and by province, you can obtain the province that has the largest proportion of sales volume in a specified range. You must specify a value for GroupByField in GroupByRange to perform this query.

• Example

  The following code provides an example on how to group people aged 18 by score range and obtain the numbers of people with scores in the [80, 90) and [90, 100) intervals:

  query = TermQuery('age', 18)
  group_by = GroupByRange(field_name = 'score', ranges = [(80, 90),(90, 100)])
  
  search_response = client.search(table_name, index_name,
                                  SearchQuery(query, next_token = None, limit=0, group_bys = [group_by]),
                                  columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for group_by in search_response.group_by_results:
      print("name:%s" % group_by.name)
      print("groups:")
      for item in group_by.items:
          print("range:%.1f~%.1f, count:%d" % (item.range_from, item.range_to, item.row_count))

The aggregation method that can be used to group query results based on geographical locations to a central point. Query results in distances that are within a specified range are grouped together. The number of items in each range is returned.

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  field	The name of the field that is used for the aggregation operation. Only the GEOPOINT type is supported.
  origin(double lat, double lon)	The longitude and latitude of the central point. lat specifies the latitude of the central point. lon specifies the longitude of the central point.
  range[double_from, double_to)	The distance ranges that are used for grouping. Unit: meters. The value range can start from Double.MIN_VALUE and end with Double.MAX_VALUE.
  sub_aggs and sub_group_bys	The sub-aggregation operation. You can perform the sub-aggregation operation based on the grouping results.

• Example

  The following code provides an example on how to group people aged 18 by the geographical distance from their school to their home and obtain the numbers of people who live within one kilometer from the school and people who live one to two kilometers away from school: The latitude and longitude of the school is (31,116).

  query = TermQuery('age', 18)
  group_by = GroupByGeoDistance(field_name = 'address', origin=GeoPoint(31, 116), ranges = [(0, 1000), (1000,2000)])
  
  search_response = client.search(table_name, index_name,
                                  SearchQuery(query, next_token = None, limit=2, group_bys = [group_by]),
                                  columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for group_by in search_response.group_by_results:
      print("name:%s" % group_by.name)
      print("groups:")
      for item in group_by.items:
          print("range:%.1f~%.1f, count:%d" % (item.range_from, item.range_to, item.row_count))

The aggregation method that can be used to filter the query results and group them together to obtain the number of results that match each filter. Results are returned in the order in which the filters are specified.

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  filter	The filters that can be used for the query. Results are returned in the order in which the filters are specified.
  sub_aggs and sub_group_bys	The sub-aggregation operation. You can perform the sub-aggregation operation based on the grouping results.

• Example

  The following code provides an example on how to group people aged 18 by filter and obtain the numbers of people who scored 100 in the math exam and who scored 100 in the Chinese exam:

  query = TermQuery('age', 18)
  filter1 = TermQuery('math', 100)
  filter2 = TermQuery('chinese', 100)
  filters = [filter1, filter2]
  group_by = GroupByFilter(filters)
  
  search_response = client.search(
      table_name, index_name,
      SearchQuery(query, next_token = None, limit=2, group_bys = [group_by]),
      columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for group_by in search_response.group_by_results:
      print("name:%s" % group_by.name)
      print("groups:")
      i = 0
      for item in group_by.items:
          print("filter:%s=%s, count:%d" % (str(filters[i].field_name), str(filters[i].column_value), item.row_count))
          i=i+1

The aggregation method that can be used to group query results based on specific data intervals. Field values that are within the same range are grouped together. The value range of each group and the number of values in each group are returned.

• Parameters

  Parameter	Description
  name	The unique name of the aggregation operation. You can query the results of a specific aggregation operation based on this name.
  field	The name of the field that is used to perform the aggregation operation. Only the LONG and DOUBLE types are supported.
  interval	The data interval that is used to obtain aggregation results.
  field_range[min,max]	The range that is used together with the interval parameter to limit the number of groups. The value that is calculated by using the formula cannot exceed 2,000.
  min_doc_count	The minimum number of rows. If the number of rows in a group is less than the minimum number of rows, the aggregation results for the group are not returned.
  missing_value	The default value for the field that is used to perform the aggregation operation on a row when the field value is empty. If you do not specify a value for missing_value, the row is ignored. If you specify a value for missing_value, the value of this parameter is used as the field value of the row.

• Example

  query = TermQuery('product', '10010')
  group_by = GroupByHistogram(field_name = 'latency', interval = 100, field_range = FieldRange(0, 10000), missing_value = 0)
  
  search_response = client.search(table_name, index_name,
                                  SearchQuery(query, next_token = None, limit=2, group_bys = [group_by]),
                                  columns_to_get = ColumnsToGet(return_type = ColumnReturnType.ALL_FROM_INDEX))
  
  for group_by in search_response.group_by_results:
      print("name:%s" % group_by.name)
      print("groups:")
      for item in group_by.items:
          print("%s:%s" % (item.key, item.value))