neoremind opened a new pull request #1996: URL: https://github.com/apache/calcite/pull/1996
https://issues.apache.org/jira/browse/CALCITE-4034 Calcite’s InnoDB adapter allows you to query the data based on InnoDB data files directy, data files are also known as .ibd files. It leverages [innodb-java-reader](https://github.com/alibaba/innodb-java-reader). This adapter is different from JDBC adapter which maps a schema in a JDBC data source and requires a MySQL server to serve response. With .ibd files and the corresponding DDLs, InnoDB adapter is able to work like a simple "MySQL server", it accepts SQL query and attempts to compile the query based on InnoDB file accessing APIs provided by [innodb-java-reader](https://github.com/alibaba/innodb-java-reader), it will exploit projecting, filtering and sorting directly in InnoDB data file where possible. What’s more, with DDLs, the adapter is "index aware", it leverages rules to choose the right index to scan, for example, using primary key or secondary keys to look up data, then it tries to push down some conditions into storage engine. Also, the adapter leaves option to provide hint as well, so that user can indicate the optimizer to force use one specific index. The InnoDB adapter can, 1) Full scan a table. 2) Scan by index such as primary key or secondary key, single column or composite keys. 3) Push down filtering condition to storage engine, support both point query and range query. 4) Push down projection. 5) Push down ordering if it matches the natural collation of an index. 6) Support almost all the commonly used data types. 7) Work as a simple MySQL server being able to read .ibd data files directly through file system, this can be a tool to query or dump table data by offloading from MySQL process under some conditions. A basic example of a model file is given below, this schema reads from a MySQL "scott" database: ``` { "version": "1.0", "defaultSchema": "scott", "schemas": [ { "name": "scott", "type": "custom", "factory": "org.apache.calcite.adapter.innodb.InnodbSchemaFactory", "operand": { "sqlFilePath": [ "/path/scott.sql" ], "ibdDataFileBasePath": "/usr/local/mysql/data/scott" } } ] } ``` `sqlFilePath` is a list of DDL files, you can generate table definitions by executing `mysqldump -d -u<username> -p<password> -h <hostname> <dbname>` in command-line. The file content of `/path/scott.sql` is given below: ``` CREATE TABLE `DEPT`( `DEPTNO` TINYINT NOT NULL, `DNAME` VARCHAR(50) NOT NULL, `LOC` VARCHAR(20), UNIQUE KEY `DEPT_PK` (`DEPTNO`) )ENGINE=InnoDB DEFAULT CHARSET=latin1; CREATE TABLE `EMP`( `EMPNO` INT(11) NOT NULL, `ENAME` VARCHAR(100) NOT NULL, `JOB` VARCHAR(15) NOT NULL, `AGE` SMALLINT, `MGR` BIGINT, `HIREDATE` DATE, `SAL` DECIMAL(8,2) NOT NULL, `COMM` DECIMAL(6,2), `DEPTNO` TINYINT, `EMAIL` VARCHAR(100) DEFAULT NULL, `CREATE_DATETIME` DATETIME, `CREATE_TIME` TIME, `UPSERT_TIME` TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP, PRIMARY KEY (`EMPNO`), KEY `ENAME_KEY` (`ENAME`), KEY `HIREDATE_KEY` (`HIREDATE`), KEY `CREATE_DATETIME_JOB_KEY` (`CREATE_DATETIME`, `JOB`), KEY `CREATE_TIME_KEY` (`CREATE_TIME`), KEY `UPSERT_TIME_KEY` (`UPSERT_TIME`), KEY `DEPTNO_JOB_KEY` (`DEPTNO`, `JOB`), KEY `DEPTNO_SAL_COMM_KEY` (`DEPTNO`, `SAL`, `COMM`), KEY `DEPTNO_MGR_KEY` (`DEPTNO`, `MGR`), KEY `AGE_KEY` (`AGE`) )ENGINE=InnoDB DEFAULT CHARSET=utf8mb4; ``` ibdDataFileBasePath is the parent file path of .ibd files. Assuming the model file is stored as `model.json`, you can connect to InnoDB data file to performa query via [sqlline](https://github.com/julianhyde/sqlline) as follows: ``` sqlline> !connect jdbc:calcite:model=model.json admin admin ``` We can issue a simple query to query all employees by writing standard SQL: ``` sqlline> select empno,ename,job,age,mgr from "EMP"; +-------+--------+-----------+-----+------+ | EMPNO | ENAME | JOB | AGE | MGR | +-------+--------+-----------+-----+------+ | 7369 | SMITH | CLERK | 30 | 7902 | | 7499 | ALLEN | SALESMAN | 24 | 7698 | | 7521 | WARD | SALESMAN | 41 | 7698 | | 7566 | JONES | MANAGER | 28 | 7839 | | 7654 | MARTIN | SALESMAN | 27 | 7698 | | 7698 | BLAKE | MANAGER | 38 | 7839 | | 7782 | CLARK | MANAGER | 32 | 7839 | | 7788 | SCOTT | ANALYST | 45 | 7566 | | 7839 | KING | PRESIDENT | 22 | null | | 7844 | TURNER | SALESMAN | 54 | 7698 | | 7876 | ADAMS | CLERK | 35 | 7788 | | 7900 | JAMES | CLERK | 40 | 7698 | | 7902 | FORD | ANALYST | 28 | 7566 | | 7934 | MILLER | CLERK | 32 | 7782 | +-------+--------+-----------+-----+------+ ``` While executing this query, the InnoDB adapter scans the InnoDB data file `EMP.ibd` using primary key, also known as clustering B+ tree index in MySQL, and is able to push down projection to underlying storage engine. Projection can reduce the size of data fetched from the storage engine. We can look up one employee by filtering. The InnoDB adapter retrieves all indexes through DDL file provided in `model.json`. ``` sqlline> select empno,ename,job,age,mgr from "EMP" where empno = 7782; +-------+-------+---------+-----+------+ | EMPNO | ENAME | JOB | AGE | MGR | +-------+-------+---------+-----+------+ | 7782 | CLARK | MANAGER | 32 | 7839 | +-------+-------+---------+-----+------+ ``` The InnoDB adapter is able to recognize that `empno` is the primary key and do a point query lookup by using clustering index instead of full table scan. We can do range query on primary key as well. ``` sqlline> select empno,ename,job,age,mgr from "EMP" where empno > 7782 and empno < 7900; ``` Note that such query is usually efficient in MySQL with InnoDB storage engine, because for clustering B+ tree index, records close in index are close in data file, which is good for scanning. We can look up one employee by secondary key. For example, the filtering condition will be on a `VARCHAR` field `ename`. ``` sqlline> select empno,ename,job,age,mgr from "EMP" where ename = 'smith'; +-------+-------+-------+-----+------+ | EMPNO | ENAME | JOB | AGE | MGR | +-------+-------+-------+-----+------+ | 7369 | SMITH | CLERK | 30 | 7902 | +-------+-------+-------+-----+------+ ``` The InnoDB adapter works well on almost all the commonly used data types in MySQL, for more information on supported data types, please refer to [innodb-java-reader](https://github.com/alibaba/innodb-java-reader#3-features). We can query by composite key. For example, given secondary index of `DEPTNO_MGR_KEY`, we can issue a query below: ``` sqlline> select empno,ename,job,age,mgr from "EMP" where deptno = 20 and mgr = 7566; +-------+-------+---------+-----+------+ | EMPNO | ENAME | JOB | AGE | MGR | +-------+-------+---------+-----+------+ | 7788 | SCOTT | ANALYST | 45 | 7566 | | 7902 | FORD | ANALYST | 28 | 7566 | +-------+-------+---------+-----+------+ ``` The InnoDB adapter will leverage the right key to push down both filtering conditions. In some cases, only part of the conditions can be pushed down since there is a limitation in the underlying storage engine API, leaving unpushed condition in the rest of the plan. Given the below SQL, only `DEPTNO` condition is pushed down, ``` select empno,ename,job,age,mgr from "EMP" where deptno = 20 and upsert_time > '2018-01-01 00:00:00'; ``` You can think of this as `Index Condition Pushdown (ICP)` in MySQL server, `innodb-java-reader` only supports range query with lower and upper bound using an index, the storage engine returns a range of rows and Calcite will evaluates the rest of `WHERE` condition from the rows fetched. For the below SQL, there are multiple index satisfying the left-prefix index rule, these possible indexes are `DEPTNO_JOB_KEY`, `DEPTNO_SAL_COMM_KEY` and `DEPTNO_MGR_KEY`, the Innod adapter will choose one of them according to the ordinal defined in DDL, only `DEPTNO` condition is pushed down, leaving `SAL` condition handled by Calcite built-in execution engine. ``` sqlline> select empno,deptno,sal from "EMP" where deptno = 20 and sal > 2000; +-------+--------+---------+ | EMPNO | DEPTNO | SAL | +-------+--------+---------+ | 7788 | 20 | 3000.00 | | 7902 | 20 | 3000.00 | | 7566 | 20 | 2975.00 | +-------+--------+---------+ ``` For secondary key, the execution process will be scanning by secondary index and look up full record back to clustering index in InnoDB, for a "big" scanning, that would introduce many random IOs, so the performance is usually not good enough. Note that the query ablove can be more performant by using `EPTNO_SAL_COMM_KEY` index, because covering index does not need to look up back to clustering index anymore. We can force using `DEPTNO_SAL_COMM_KEY ` index by hint. ``` sqlline> select empno,ename,job,age,sal from "emp"/*+ index(DEPTNO_SAL_COMM_KEY) */ where deptno = 20 and sal > 2000; ``` Hint strategy can be configured in `SqlToRelConverter`, you should register `index` hint on `TableScan` strategy beforehand in order to use this feature. Index hint takes effect when issuing SQL query with filtering, for query without any filtering, a full scan on clustering index will be used. Ordering can be pushed down if it matches the natural collation of the index used. ``` sqlline> select deptno,ename,hiredate from "EMP" where hiredate < '2020-01-01' order by hiredate desc; +--------+--------+------------+ | DEPTNO | ENAME | HIREDATE | +--------+--------+------------+ | 20 | ADAMS | 1987-05-23 | | 20 | SCOTT | 1987-04-19 | | 10 | MILLER | 1982-01-23 | | 20 | FORD | 1981-12-03 | | 30 | JAMES | 1981-12-03 | | 10 | KING | 1981-11-17 | | 30 | MARTIN | 1981-09-28 | | 30 | TURNER | 1981-09-08 | | 10 | CLARK | 1981-06-09 | | 30 | WARD | 1981-02-22 | | 30 | ALLEN | 1981-02-20 | | 20 | JONES | 1981-02-04 | | 30 | BLAKE | 1981-01-05 | | 20 | SMITH | 1980-12-17 | +--------+--------+------------+ ``` ## Limitations `innodb-java-reader` has some prerequisites for .ibd files, please refer to [Prerequisites](https://github.com/alibaba/innodb-java-reader#2-prerequisites). Currently the InnoDB adapter does not aware row count and cardinality of a .ibd data file, so it will only rely on simple rules to perform optimization, once underlying storage engine could provide such metrics and metadata, this can be integrated in Calcite by leveraging cost based optimization in the future. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
