/7.2/aws/sql/Recent content in SQL on Kinetica DocsHugo -- gohugo.ioen/7.2/aws/sql/ddl/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/ddl/Column Types Column Properties CREATE SCHEMA ALTER SCHEMA DROP SCHEMA SHOW SCHEMA DESCRIBE SCHEMA SET CURRENT SCHEMA CREATE TABLE CREATE EXTERNAL TABLE CREATE TABLE ... AS ALTER TABLE REFRESH EXTERNAL TABLE REPAIR TABLE TRUNCATE TABLE DROP TABLE SHOW TABLE DESCRIBE TABLE CREATE VIEW CREATE MATERIALIZED VIEW ALTER VIEW ALTER MATERIALIZED VIEW REFRESH VIEW DROP VIEW SHOW VIEW DESCRIBE VIEW CREATE CREDENTIAL ALTER CREDENTIAL DROP CREDENTIAL SHOW CREDENTIAL DESCRIBE CREDENTIAL CREATE DATA SOURCE ALTER DATA SOURCE LIST DATA SOURCE DROP DATA SOURCE SHOW DATA SOURCE DESCRIBE DATA SOURCE CREATE DATA SINK ALTER DATA SINK DROP DATA SINK SHOW DATA SINK DESCRIBE DATA SINK CREATE STREAM DROP STREAM SHOW STREAM DESCRIBE STREAM DESCRIBE Comments Kinetica supports the basic notion of SQL tables as containers of one or more columns of data./7.2/aws/sql/dml/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/dml/INSERT INSERT INTO ... VALUES INSERT INTO ... SELECT Upserting Ignoring Duplicates UPDATE Overwriting Duplicates Ignoring Duplicates MERGE INTO DELETE INSERT There are two methods for inserting data into a table from within the database: Inserting values Copying data from another table When specifying a column list using either method, any non-nullable fields not included in the list will be given default values--empty string for strings, and 0 for numerics./7.2/aws/sql/backup_restore/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/backup_restore/CREATE BACKUP Backup Options Examples ALTER BACKUP Examples RESTORE BACKUP Restore Options Examples DROP BACKUP Examples SHOW BACKUP Examples DESCRIBE BACKUP Examples Kinetica provides a SQL interface for performing hot backups via full, incremental, & differential snapshots and restoration of schema objects and data. For information on full system backups, see System Backup instead. The ability to manage backups is available using the following commands: CREATE BACKUP ALTER BACKUP RESTORE BACKUP DROP BACKUP SHOW BACKUP DESCRIBE BACKUP See Backing Up & Restoring the Cluster for details on backing up and restoring data./7.2/aws/sql/export/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/export/EXPORT ... INTO Delimited Text Options Export Options Examples EXPORT ... INTO Kinetica can export data from one or more tables, using an EXPORT ... INTO statement. The data can be exported to either of the following: KiFS a data sink configured to allow write access to: remote files on Azure, GCS, HDFS, or S3 a writable remote database table via JDBC Note For contextualized examples, see Examples. For copy/paste examples, see Exporting Data./7.2/aws/sql/kifs/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/kifs/CREATE DIRECTORY ALTER DIRECTORY Set Data Limit LIST DIRECTORY DOWNLOAD DIRECTORY DROP DIRECTORY UPLOAD FILE UPLOAD URL LIST FILE DOWNLOAD FILE DROP FILE Kinetica, via KiFS, provides support for staging files within the database for subsequent ingestion. The file structure consists of a single layer of top-level directories, with each file contained within one of those directories. File names can contain / characters to give the appearance of existing under a hierarchy of one or more sub-directories./7.2/aws/sql/graph/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/graph/SQL/PGQ Examples Limitations & Suggestions QUERY_GRAPH Examples MATCH_GRAPH Examples SOLVE_GRAPH Examples CREATE GRAPH Examples ALTER GRAPH DROP GRAPH SHOW GRAPH DESCRIBE GRAPH Kinetica provides support for graph creation and management in SQL. Graphs represent topological relationships (both geospatial and non-geospatial) via nodes that are connected by edges. Graph features accessible via SQL include: Query SQL/PGQ QUERY_GRAPH Solve MATCH_GRAPH SOLVE_GRAPH Management CREATE GRAPH ALTER GRAPH DROP GRAPH SHOW GRAPH DESCRIBE GRAPH SQL/PGQ SQL/PGQ can be used to query an existing graph using the supported subset of the Cypher Query Language syntax./7.2/aws/sql/limit/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/limit/ Feature Limitations Legacy Features Not Yet Supported SQL Kinetica has some limitations with current features and has removed support for some legacy features. Feature Limitations Correlated subqueries have the following limitations: They cannot reference grouping columns in the parent query They cannot reference tables beyond the immediate outer query; i.e., a table cannot be referenced in a correlated subquery that is two or more levels of nesting deeper than it is They cannot contain disjunctive conditions They cannot be part of an OUTER JOIN ON clause condition Legacy Features Store-only columns are no longer supported Disk-optimized columns are no longer supported Not Yet Supported SQL Windowing Features Frame Exclusion (EXCLUDE) Ranking Functions PERCENTILE_CONT PERCENTILE_DISC/7.2/aws/sql/load/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/load/LOAD INTO Delimited Text Options Delimited Text Option Characters Load Options Table Property Clause Examples File Paths File Formats Table Options Column Options Dry Run Data Sources Change Data Capture INSERT INTO ... SELECT ... FROM FILE Options Option Characters Examples File Paths Options Considerations Kinetica supports loading of data via SQL from a variety of file types and through several mechanisms. There are two primary paths: Loading server-accessible data sets via SQL (or the /execute/sql endpoint call) that either: can be reached by Kinetica via an external data source object have been uploaded to Kinetica via SQL, Workbench, or API Loading client-side files via ODBC/JDBC LOAD INTO Kinetica can load either internal or external data into a table, using a LOAD INTO statement./7.2/aws/sql/ml/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/ml/CREATE CONTAINER REGISTRY DROP CONTAINER REGISTRY SHOW CONTAINER REGISTRY IMPORT MODEL EVALUATE_MODEL ALTER MODEL REFRESH MODEL DROP MODEL SHOW MODEL DESCRIBE MODEL Kinetica provides support for Machine Learning (ML) container registry & model creation and management in SQL. A Docker registry serves as a container for ML models, while an ML model represents a function from which an inference can be computed. Note For statistical analysis functions that don't require a model, see ML Functions./7.2/aws/sql/naming/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/naming/Name Resolution Naming Criteria Reserved Words Name Resolution Most database objects (tables, views, etc.) can be addressed using a qualified name, by prefixing the name of the object with the name of its containing schema, separated by a dot; e.g.: 1 <schema name>.<table/view name> For the full list of schema-contained objects, see CREATE SCHEMA. If these objects are referenced without a schema, they will be looked for in the user's default schema, if one has been assigned./7.2/aws/sql/query/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/query/Tableless Query Join ASOF Aggregation Grouping ROLLUP CUBE GROUPING SETS Window Aggregate Functions Ranking Functions Examples PIVOT UNPIVOT Set Operations UNION INTERSECT EXCEPT WITH (Common Table Expressions) Recursive Queries Iteration Constants Numeric Constants String-Based Constants Binary Constants Date/Time Constants Expressions Array Functions Scalar Functions Aggregation/Transposition Functions Conditional Functions CASE Conversion Functions Date/Time Conversion Codes Date/Time Functions Date/Time Base Functions Date/Time Complex Conversion Functions Geospatial/Geometry Functions Enhanced Performance Scalar Functions Scalar Functions Aggregation Functions Track Functions H3 Functions JSON Functions Scalar Functions Aggregation Functions Math Functions Scalar Functions Aggregate Functions Null Functions Record Distribution Functions String Functions Scalar Functions Aggregation Functions LIKE / ILIKE FILTER_BY_STRING User/Security Functions Aggregation Functions Lookup/Grouping Functions Grouping Functions ML Functions PREDICT OUTLIERS Distribution Functions Sharding Example Predicates Predicate Operators Predicate Clauses Compound Predicate Operators Subqueries Non-Correlated Subqueries Correlated Subqueries Remote Queries Hints Scoped Hints Global Hints EXPLAIN SELECT Statement Syntax 1 2 3 4 5 6 7 8 9 SELECT [DISTINCT | TOP <n>] <column expression> [EXCLUDE(<column exclusion list>)],./7.2/aws/sql/resource_group/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/resource_group/CREATE RESOURCE GROUP Resource Group Options Examples ALTER RESOURCE GROUP Resource Group Options Examples DROP RESOURCE GROUP SHOW RESOURCE GROUP DESCRIBE RESOURCE GROUP ALTER TIER Examples Kinetica provides a SQL interface for managing resource groups, which can be used to limit memory and thread usage, and execution priority for a user or group of users. It also provides the means to modify the tiers that the resource groups reference./7.2/aws/sql/reveal/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/reveal/LOAD DASHBOARD Kinetica provides a SQL interface for loading Reveal dashboards from a variety of sources: KiFS path Cloud provider via data source Public URL (GitHub, etc.) LOAD DASHBOARD Syntax 1 2 3 LOAD DASHBOARD "<dashboard name>" FROM FILE PATH '<file path>' [WITH OPTIONS (DATASOURCE = '<data source name>')] Parameters Description <dashboard name> Name of the dashboard. Important This name must match the name of the dashboard in the file./7.2/aws/sql/security/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/security/User Security Detail SHOW SECURITY FOR User Role Security Detail SHOW SECURITY FOR Role Privilege Management GRANT SYSTEM Permission GRANT Schema Permission GRANT Table Permission GRANT Credential Permission GRANT Data Source Permission GRANT Data Sink Permission GRANT Directory Permission GRANT Function (UDF/UDTF) Permission GRANT Graph Permission GRANT Procedure Permission GRANT SQL-GPT Context Permission GRANT Stream Permission REVOKE SYSTEM Permission REVOKE Schema Permission REVOKE Table Permission REVOKE Credential Permission REVOKE Data Source Permission REVOKE Data Sink Permission REVOKE Directory Permission REVOKE Function (UDF/UDTF) Permission REVOKE Graph Permission REVOKE Procedure Permission REVOKE SQL-GPT Context Permission REVOKE Stream Permission SHOW SECURITY CHECK Impersonation (Execute As./7.2/aws/sql/procedure/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/procedure/Supported Statements CREATE PROCEDURE ALTER PROCEDURE Set Execution User Set Execute Mode EXECUTE PROCEDURE DROP PROCEDURE SHOW PROCEDURE Security Kinetica provides support for basic SQL procedures, as an executable batch of SQL statements. A SQL procedure can be executed by two means: on-demand - called directly by a user scheduled execution - configured, upon creation, to execute at a user-specified interval Note Even if a SQL procedure is configured for scheduled execution, it can still be executed directly by a user in on-demand fashion./7.2/aws/sql/sql_reserved_words/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/sql_reserved_words/ Kinetica reserves the following words within its SQL context. Any tables, columns, or other named references that conflict with these must be double-quoted to be used. For instance, if using a column name of abs, it must be double-quoted: 1 2 3 4 CREATE TABLE example.quoted_reserved_word_column_name ( "abs" INT ) Jump to Letter A B C D E F G H I J K L M N O P Q R S T U V W A ABS ACCESS ALL ALLOCATE ALLOW ALTER AND ANY ARE ARRAY ARRAY_MAX_CARDINALITY AS ASENSITIVE ASOF ASYMMETRIC ATOMIC AUTHORIZATION AVG B BEGIN BEGIN_FRAME BEGIN_PARTITION BETWEEN BIGINT BINARY BIT BLOB BOOLEAN BOTH BUCKET BY BYTES C CALL CALLED CARDINALITY CASCADED CASE CAST CEIL CEILING CHAR_LENGTH CHARACTER CHARACTER_LENGTH CHECK CLASSIFIER CLOB CLOSE COALESCE COLLATE COLLECT COLLECTION COLUMN COMMIT COMPRESSION CONDITION CONNECT CONSTRAINT CONVERT COPY CORR CORRESPONDING COVAR_POP COVAR_SAMP CREATE CROSS CUBE CUME_DIST CURRENT CURRENT_CATALOG CURRENT_DATE CURRENT_DEFAULT_TRANSFORM_GROUP CURRENT_PATH CURRENT_ROLE CURRENT_ROW CURRENT_SCHEMA CURRENT_TIME CURRENT_TIMESTAMP CURRENT_TRANSFORM_GROUP_FOR_TYPE CURRENT_USER CURSOR CYCLE D DEALLOCATE DEC DECLARE DEFAULT_ DEFAULTVALUE DEFINE DELETE DELIMITED DENSE_RANK DEREF DESCRIBE DETERMINISTIC DICT DISALLOW DISCONNECT DISK_OPTIMIZED DISTINCT DROP DYNAMIC E EACH ELEMENT ELSE EMPTY END END_EXEC END_FRAME END_PARTITION EQUALS ESCAPE EVERY EXCEPT EXEC EXECUTE EXISTS EXP EXPLAIN EXTEND EXTERNAL EXTRACT F FALSE FETCH FIRST_VALUE FLOOR FOR FOREIGN FORMAT FRAME_ROW FREE FROM FULL FUNCTION FUSION G GET GLOBAL GRANT GROUP GROUPING GROUPS H HAVING HOLD I IDENTIFIED IDENTITY IGNORE IMPORT IN INCLUDE INDICATOR INITIAL INNER INOUT INSENSITIVE INSERT INTEGER INTERSECT INTERSECTION INTERVAL INTO IS J JOIN JSON_ARRAY JSON_ARRAYAGG JSON_EXISTS JSON_OBJECT JSON_OBJECTAGG JSON_QUERY JSON_VALUE K KERBEROS L LAG LANGUAGE LARGE LAST_VALUE LATERAL LEAD LEADING LEFT LIKE LIKE_REGEX LIMIT LN LOCAL LOCALTIME LOCALTIMESTAMP LOWER M MATCH MATCH_NUMBER MATCH_RECOGNIZE MAX MEASURES MEMBER MERGE METHOD MIN MINUS MOD MODIFIES MODIFY MODULE MOVE MULTISET N NATIONAL NATURAL NCHAR NCLOB NEW NEXT NO NONE NORMALIZE NOT NTH_VALUE NTILE NULL NULLIF NUMERIC O OCCURRENCES_REGEX OCTET_LENGTH OF OFFSET OLD OMIT ON ONE ONLY OPEN OR ORDER OUT OUTER OVER OVERLAPS OVERLAY P PARAMETER PARTITION PASSWORD PATHS PATTERN PER PERCENT PERCENT_RANK PERCENTILE_CONT PERCENTILE_DISC PERIOD PERMUTE PIVOT PORTION POSITION_REGEX POWER PRECEDES PRECISION PREPARE PREV PRIMARY PRIMARY_KEY PROCEDURE PROTECTED Q QUOTE_ R RANGE RANK READS REAL RECURSIVE REFERENCES REFERENCING REGR_AVGX REGR_AVGY REGR_COUNT REGR_INTERCEPT REGR_R2 REGR_SLOPE REGR_SXX REGR_SXY REGR_SYY RELEASE RENAME REPLICATED RESET RESOURCE_ RESPECT RESULT RETURN REVOKE RIGHT ROLLBACK ROLLUP ROW ROW_NUMBER ROWS RUNNING S SAVEPOINT SCOPE SCROLL SEARCH SECONDS SEEK SELECT SENSITIVE SESSION_USER SET SET_MINUS SHARD SHARD_KEY SHOW SIMILAR SKIP_ SMALLINT SOME SPECIFIC SPECIFICTYPE SQL SQL_DATETIME SQL_TSI_MILLISECOND SQLEXCEPTION SQLSTATE SQLWARNING SQRT START STATIC STATISTICS STDDEV_POP STDDEV_SAMP STREAM SUBMULTISET SUBSET SUBSTR SUBSTRING SUBSTRING_REGEX SUCCEEDS SUM SYMMETRIC SYSTEM_TIME SYSTEM_USER T TABLE TABLESAMPLE TEXT_SEARCH THEN TIMEZONE_HOUR TIMEZONE_MINUTE TINYINT TO TOKEN_ TOP TRAILING TRANSLATE TRANSLATE_REGEX TRANSLATION TREAT TRIGGER TRIM TRIM_ARRAY TRUE TRUNCATE TTL U UESCAPE UNION UNIQUE UNKNOWN UNNEST UNPIVOT UNSIGNED UPDATE UPPER UPSERT USER USING V VALUE_OF VALUES VAR_POP VAR_SAMP VARBINARY VARCHAR VARYING VERSIONING W WHEN WHENEVER WHERE WIDTH_BUCKET WINDOW WITH WITHIN WITHOUT/7.2/aws/sql/sqlgpt/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/sqlgpt/CREATE CONTEXT Table Definition Clause Sample Definition Clause Rule Definition Clause Example ALTER CONTEXT Add Context Table Drop Context Table Add Context Samples Drop Context Samples Add Context Rules Drop Context Rules DROP CONTEXT SHOW CONTEXT DESCRIBE CONTEXT GENERATE SQL SQL Generation Options SQL Generation Example GENERATE TOKENS SQL Token Generation Options SQL Token Generation Example EXECUTE_AI_QUERY SQL Generation Options SQL Execution Example Kinetica provides support for generative AI via SQL-GPT./7.2/aws/sql/system_properties/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/system_properties/ALTER SYSTEM SET PROPERTIES Alterable Properties ALTER SYSTEM Examples SHOW SYSTEM PROPERTIES Kinetica provides a SQL interface for managing system properties, which control everything from GPUs, hostnames, storage, graph server, and more. The ability to manage system properties is available through SQL, using the following commands: ALTER SYSTEM SET PROPERTIES SHOW SYSTEM PROPERTIES ALTER SYSTEM SET PROPERTIES Only a small subset of the system properties can be altered at runtime./7.2/aws/sql/udf/Mon, 01 Jan 0001 00:00:00 +0000/7.2/aws/sql/udf/CREATE FUNCTION Executing Functions Function Parameters Examples DROP FUNCTION SHOW FUNCTION SHOW FUNCTION STATUS DESCRIBE FUNCTION CREATE FUNCTION ENVIRONMENT ALTER FUNCTION ENVIRONMENT DROP FUNCTION ENVIRONMENT SHOW FUNCTION ENVIRONMENT DESCRIBE FUNCTION ENVIRONMENT Kinetica provides support for User-Defined Function (UDF) creation and management in SQL. Unlike conventional UDFs, Kinetica UDFs are external programs that can be managed via SQL and may run in distributed fashion across the cluster. A UDF that is defined to return a specific table type is known as a User-Defined Table Function (UDTF), which can be used within a SELECT statement to return the output of the function as a query result set.