You can call the CreateSearchIndex operation to create a search index for a data table. When you create a search index, you can add the fields that you want to query to the search index and configure advanced settings for the search index. For example, you can configure the routing key and presorting settings.
Prerequisites
An OTSClient instance is initialized. For more information, see Initialize an OTSClient instance.
A data table for which the max_version parameter is set to 1 is created. The time_to_live parameter of the data table must meet one of the following conditions. For more information, see Create a data table.
The time_to_live parameter of the data table is set to -1, which specifies that the data in the data table never expires.
The time_to_live parameter of the data table is set to a value other than -1. The Allow Updates parameter is set to No, which specifies that update operations on the data table are prohibited.
Usage notes
The data types of the fields in a search index must match the data types of the fields in the data table for which the search index is created. For more information, see Data type mappings.
To set the time_to_live parameter of a search index to a value other than -1, make sure that the UpdateRow operation is prohibited on the data table for which the search index is created. The value of the time_to_live parameter of the search index must be less than or equal to the value of the time_to_live parameter of the data table. For more information, see Specify the TTL of a search index.
Parameters
When you create a search index, you must specify the table_name, index_name, and schema parameters. In the schema parameter, configure the field_schemas, index_setting, and index_sort parameters. The following table describes the preceding parameters.
Parameter | Description |
table_name | The name of the table. |
index_name | The name of the search index. |
field_schemas | The list of field schemas. In each field schema, configure the following parameters:
|
index_setting | The settings of the search index, including the settings of the routing_fields parameter. routing_fields (optional): the custom routing fields. You can specify multiple primary key columns as routing fields. Tablestore distributes data that is written to a search index across different partitions based on the specified routing fields. The data that has the same routing field values is distributed to the same partition. |
index_sort | The presorting settings of the search index, including the settings of the sorters parameter. If you do not configure the index_sort parameter, field values are sorted by primary key. Note If you set the field_type parameter to Nested, you cannot configure the indexSort parameter. sorters (required): the presorting method of the search index. Valid values: PrimaryKeySort and FieldSort. For more information, see Sorting and paging.
|
Examples
Create a search index with an analyzer type specified
The following sample code provides an example on how to create a search index with an analyzer type specified. In this example, the search index consists of the following fields: the k field of the Keyword type, the t field of the Text type, the g field of the Geo-point type, the ka field of the array Keyword type, the la field of the array Long type, and the n field of the Nested type. The n field consists of the following subfields: the nk field of the Keyword type, the nl field of the Long type, and the nt field of the Text type.
def create_search_index(client):
# Create an index on the Keyword field and enable the aggregation feature for the field.
field_a = FieldSchema('k', FieldType.KEYWORD, index=True, enable_sort_and_agg=True, store=True)
# Create an index on the Text field and set the analyzer type to single-word tokenization.
field_b = FieldSchema('t', FieldType.TEXT, index=True, store=True, analyzer=AnalyzerType.SINGLEWORD)
# Create an index on the Text field and set the analyzer type to fuzzy tokenization.
#field_b = FieldSchema('t', FieldType.TEXT, index=True, store=True, analyzer=AnalyzerType.FUZZY,analyzer_parameter=FuzzyAnalyzerParameter(1, 6))
# Create an index on the Text field and set the analyzer type to delimiter tokenization.
#field_b = FieldSchema('t', FieldType.TEXT, index=True, store=True, analyzer=AnalyzerType.SPLIT, analyzer_parameter = SplitAnalyzerParameter(","))
# Create an index on the Geo-point field.
field_c = FieldSchema('g', FieldType.GEOPOINT, index=True, store=True)
# Create an index on the array Keyword field.
field_d = FieldSchema('ka', FieldType.KEYWORD, index=True, is_array=True, store=True)
# Create an index on the array Long field.
field_e = FieldSchema('la', FieldType.LONG, index=True, is_array=True, store=True)
# The Nested field consists of three subfields: the nk subfield of the Keyword type, the nl subfield of the Long type, and the nt subfield of the Text type.
field_n = FieldSchema('n', FieldType.NESTED, sub_field_schemas=[
FieldSchema('nk', FieldType.KEYWORD, index=True, store=True),
FieldSchema('nl', FieldType.LONG, index=True, store=True),
FieldSchema('nt', FieldType.TEXT, index=True, store=True),
])
fields = [field_a, field_b, field_c, field_d, field_e, field_n]
index_setting = IndexSetting(routing_fields=['PK1'])
index_sort = None # If the search index contains Nested fields, you cannot configure presorting for the search index.
#index_sort = Sort(sorters=[PrimaryKeySort(SortOrder.ASC)])
index_meta = SearchIndexMeta(fields, index_setting=index_setting, index_sort=index_sort)
client.create_search_index('<TABLE_NAME>', '<SEARCH_INDEX_NAME>', index_meta)Create a search index that contains Vector fields
The following sample code provides an example on how to create a search index that contains Vector fields. In this example, the search index consists of the following fields: the col_keyword field of the Keyword type, the col_long field of the Long type, and the col_vector field of the Vector type. The dot product algorithm is used to measure the distance of vectors.
def create_search_index(client):
index_meta = SearchIndexMeta([
FieldSchema('col_keyword', FieldType.KEYWORD, index=True, enable_sort_and_agg=True, store=True), # The Keyword type.
FieldSchema('col_long', FieldType.LONG, index=True, store=True), # The Long type.
FieldSchema("col_vector", FieldType.VECTOR, # The Vector type.
vector_options=VectorOptions(
data_type=VectorDataType.VD_FLOAT_32,
dimension=4, # Number of vector dimensions: 4. Distance measurement algorithm used for the vector: dot product.
metric_type=VectorMetricType.VM_DOT_PRODUCT
)),
])
client.create_search_index(table_name, index_name, index_meta)Create a search index with the highlight feature enabled
The following sample code provides an example on how to create a search index with the highlight feature enabled. In this example, the search index consists of the following fields: the k field of the Keyword type, the t field of the Text type, and the n field of the Nested type. The n field consists of the following subfields: the nk field of the Keyword type, the nk field of the Long type, and the nt field of the Text type. In addition, the highlight feature is enabled for the t field and nt subfield of the Text field.
def create_search_index0905(client):
# Create an index on the Keyword field and enable the aggregation feature for the field.
field_a = FieldSchema('k', FieldType.KEYWORD, index=True, enable_sort_and_agg=True, store=True)
# Create an index on the Text field, set the analyzer type of the field to single-word tokenization, and enable the highlight feature for the field.
field_b = FieldSchema('t', FieldType.TEXT, index=True, store=True, analyzer=AnalyzerType.SINGLEWORD,
enable_highlighting=True)
# Create an index on the Nested field that consists of the following subfields: the nk field of the Keyword type, the nl field of the Long type, and the nt field of the Text type. Enable the highlight feature for the nt subfield of the Text type.
field_n = FieldSchema('n', FieldType.NESTED, sub_field_schemas=[
FieldSchema('nk', FieldType.KEYWORD, index=True, store=True),
FieldSchema('nl', FieldType.LONG, index=True, store=True),
FieldSchema('nt', FieldType.TEXT, index=True, store=True, enable_highlighting=True),
])
fields = [field_a, field_b, field_n]
index_setting = IndexSetting(routing_fields=['id'])
index_sort = None # If the search index contains Nested fields, you cannot configure presorting for the search index.
# index_sort = Sort(sorters=[PrimaryKeySort(SortOrder.ASC)])
index_meta = SearchIndexMeta(fields, index_setting=index_setting, index_sort=index_sort)
client.create_search_index('pythontest', 'pythontest_0905', index_meta)FAQ
References
After you create a search index, you can use the query methods provided by the search index to query data from multiple dimensions based on your business requirements. A search index usually provides the following query methods: term query, terms query, match all query, match query, match phrase query, prefix query, range query, wildcard query, geo query, Boolean query, KNN vector query, nested query, and exists query.
If you call the Search operation to query data, you can sort or paginate the rows that meet the query conditions by using the sorting and paging features. For more information, see Sorting and paging.
If you call the Search operation to query data, you can use the collapse (distinct) feature to collapse the result set based on a specific field. This way, data of the specified type appears only once in the query results. For more information, see Collapse (distinct).
You can specify the TTL for a search index to delete historical data in the search index or extend the retention period of data in the search index. For more information, see Specify the TTL of a search index.
If you want to analyze data in a table, you can call the Search operation to use the aggregation feature or the SQL query feature. For example, you can query the maximum and minimum values, the sum of the values, and the number of rows. For more information, see Aggregation and SQL query.
If you want to obtain all rows that meet the query conditions without the need to sort the rows, you can call the ParallelScan and ComputeSplits operations to use the parallel scan feature. For more information, see Parallel scan.
You can dynamically modify the schema of a search index to add, update, or remove index fields in the search index. For more information, see Dynamically modify the schema of a search index.
You can call the ListSearchIndex operation to query all search indexes that are created for a data table. For more information, see List search indexes.
You can call the DescribeSearchIndex operation to query the description of a search index, such as the field information and search index configurations. For more information, see Query the description of a search index.
You can delete a search index that you no longer require. For more information, see Delete a search index.