Compile SQL queries to Storm topologies.
Run the storm sql command to compile SQL statements into Trident topology, and submit it to the Storm cluster
$ bin/storm sql <sql-file> <topo-name>
In which sql-file contains a list of SQL statements to be executed, and topo-name is the name of the topology.
StormSQL activates explain mode and shows query plan instead of submitting topology when user specifies topo-name as --explain. Detailed explanation is available from Showing Query Plan (explain mode) section.
The following features are supported in the current repository:
In StormSQL data is represented by external tables. Users can specify data sources using the CREATE EXTERNAL TABLE statement. For example, the following statement specifies a Kafka spouts and sink:
CREATE EXTERNAL TABLE FOO (ID INT PRIMARY KEY) LOCATION 'kafka://localhost:2181/brokers?topic=test' TBLPROPERTIES '{"producer":{"bootstrap.servers":"localhost:9092","acks":"1","key.serializer":"org.apache.storm.kafka.IntSerializer","value.serializer":"org.apache.storm.kafka.ByteBufferSerializer"}}'
The syntax of CREATE EXTERNAL TABLE closely follows the one defined in Hive Data Definition Language.
PARALLELISM is StormSQL's own keyword which describes parallelism hint for input data source. This is same as providing parallelism hint to Trident Spout. Downstream operators are executed with same parallelism before repartition (Aggregation triggers repartition).
Default value is 1, and this option is no effect on output data source. (We might change if needed. Normally repartition is the thing to avoid.)
Users plug in external data sources through implementing the ISqlTridentDataSource interface and registers them using the mechanisms of Java's service loader. The external data source will be chosen based on the scheme of the URI of the tables. Please refer to the implementation of storm-sql-kafka for more details.
Users can define user defined function (scalar or aggregate) using CREATE FUNCTION statement. For example, the following statement defines MYPLUS function which uses org.apache.storm.sql.TestUtils$MyPlus class.
CREATE FUNCTION MYPLUS AS 'org.apache.storm.sql.TestUtils$MyPlus'
Storm SQL determines whether the function as scalar or aggregate by checking which methods are defined. If the class defines evaluate method, Storm SQL treats the function as scalar, and if the class defines add method, Storm SQL treats the function as aggregate.
Example of class for scalar function is here:
public class MyPlus {
public static Integer evaluate(Integer x, Integer y) {
return x + y;
}
}
and class for aggregate function is here:
public class MyConcat {
public static String init() {
return "";
}
public static String add(String accumulator, String val) {
return accumulator + val;
}
public static String result(String accumulator) {
return accumulator;
}
}
If users don't define result method, result is the last return value of add method. Users need to define result method only when we need to transform accumulated value.
Let's say there is a Kafka stream that represents the transactions of orders. Each message in the stream contains the id of the order, the unit price of the product and the quantity of the orders. The goal is to filter orders where the transactions are significant and to insert these orders into another Kafka stream for further analysis.
The user can specify the following SQL statements in the SQL file:
CREATE EXTERNAL TABLE ORDERS (ID INT PRIMARY KEY, UNIT_PRICE INT, QUANTITY INT) LOCATION 'kafka://localhost:2181/brokers?topic=orders' TBLPROPERTIES '{"producer":{"bootstrap.servers":"localhost:9092","acks":"1","key.serializer":"org.apache.storm.kafka.IntSerializer","value.serializer":"org.apache.storm.kafka.ByteBufferSerializer"}}'
CREATE EXTERNAL TABLE LARGE_ORDERS (ID INT PRIMARY KEY, TOTAL INT) LOCATION 'kafka://localhost:2181/brokers?topic=large_orders' TBLPROPERTIES '{"producer":{"bootstrap.servers":"localhost:9092","acks":"1","key.serializer":"org.apache.storm.kafka.IntSerializer","value.serializer":"org.apache.storm.kafka.ByteBufferSerializer"}}'
INSERT INTO LARGE_ORDERS SELECT ID, UNIT_PRICE * QUANTITY AS TOTAL FROM ORDERS WHERE UNIT_PRICE * QUANTITY > 50
The first statement defines the table ORDER which represents the input stream. The LOCATION clause specifies the ZkHost (localhost:2181), the path of the brokers in ZooKeeper (/brokers) and the topic (orders). The TBLPROPERTIES clause specifies the configuration of KafkaProducer. Current implementation of storm-sql-kafka requires specifying both LOCATION and TBLPROPERTIES clauses even though the table is read-only or write-only.
Similarly, the second statement specifies the table LARGE_ORDERS which represents the output stream. The third statement is a SELECT statement which defines the topology: it instructs StormSQL to filter all orders in the external table ORDERS, calculates the total price and inserts matching records into the Kafka stream specified by LARGE_ORDER.
To run this example, users need to include the data sources (storm-sql-kafka in this case) and its dependency in the class path. Dependencies for Storm SQL are automatically handled when users run storm sql. Users can include data sources at the submission step like below:
$ bin/storm sql order_filtering.sql order_filtering --artifacts "org.apache.storm:storm-sql-kafka:2.0.0-SNAPSHOT,org.apache.storm:storm-kafka:2.0.0-SNAPSHOT,org.apache.kafka:kafka_2.10:0.8.2.2\!org.slf4j:slf4j-log4j12,org.apache.kafka:kafka-clients:0.8.2.2"
Above command submits the SQL statements to StormSQL. Users need to modify each artifacts' version if users are using different version of Storm or Kafka.
By now you should be able to see the order_filtering topology in the Storm UI.
Like explain on SQL statement, StormSQL provides explain mode when running Storm SQL Runner. In explain mode, StormSQL analyzes each query statement (only DML) and show plan instead of submitting topology.
In order to run explain mode, you need to provide topology name as --explain and run storm sql as same as submitting.
For example, when you run the example seen above with explain mode:
$ bin/storm sql order_filtering.sql --explain --artifacts "org.apache.storm:storm-sql-kafka:2.0.0-SNAPSHOT,org.apache.storm:storm-kafka:2.0.0-SNAPSHOT,org.apache.kafka:kafka_2.10:0.8.2.2\!org.slf4j:slf4j-log4j12,org.apache.kafka:kafka-clients:0.8.2.2"
StormSQL prints out like below:
=========================================================== query> CREATE EXTERNAL TABLE ORDERS (ID INT PRIMARY KEY, UNIT_PRICE INT, QUANTITY INT) LOCATION 'kafka://localhost:2181/brokers?topic=orders' TBLPROPERTIES '{"producer":{"bootstrap.servers":"localhost:9092","acks":"1","key.serializer":"org.apache.storm.kafka.IntSerializer","value.serializer":"org.apache.storm.kafka.ByteBufferSerializer"}}' ----------------------------------------------------------- 16:53:43.951 [main] INFO o.a.s.s.r.DataSourcesRegistry - Registering scheme kafka with org.apache.storm.sql.kafka.KafkaDataSourcesProvider@4d1bf319 No plan presented on DDL =========================================================== =========================================================== query> CREATE EXTERNAL TABLE LARGE_ORDERS (ID INT PRIMARY KEY, TOTAL INT) LOCATION 'kafka://localhost:2181/brokers?topic=large_orders' TBLPROPERTIES '{"producer":{"bootstrap.servers":"localhost:9092","acks":"1","key.serializer":"org.apache.storm.kafka.IntSerializer","value.serializer":"org.apache.storm.kafka.ByteBufferSerializer"}}' ----------------------------------------------------------- No plan presented on DDL =========================================================== =========================================================== query> INSERT INTO LARGE_ORDERS SELECT ID, UNIT_PRICE * QUANTITY AS TOTAL FROM ORDERS WHERE UNIT_PRICE * QUANTITY > 50 ----------------------------------------------------------- plan> LogicalTableModify(table=[[LARGE_ORDERS]], operation=[INSERT], updateColumnList=[[]], flattened=[true]), id = 8 LogicalProject(ID=[$0], TOTAL=[*($1, $2)]), id = 7 LogicalFilter(condition=[>(*($1, $2), 50)]), id = 6 EnumerableTableScan(table=[[ORDERS]]), id = 5 ===========================================================
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