The Storm SQL integration allows users to run SQL queries over streaming data in Storm. Not only the SQL interface allows faster development cycles on streaming analytics, but also opens up the opportunities to unify batch data processing like Apache Hive and real-time streaming data analytics.
At a very high level StormSQL compiles the SQL queries to Trident topologies and executes them in Storm clusters. This document provides information of how to use StormSQL as end users. For people that are interested in more details in the design and the implementation of StormSQL please refer to the this page.
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.
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. The syntax of CREATE EXTERNAL TABLE closely follows the one defined in Hive Data Definition Language:
CREATE EXTERNAL TABLE table_name field_list
[ STORED AS
INPUTFORMAT input_format_classname
OUTPUTFORMAT output_format_classname
]
LOCATION location
[ TBLPROPERTIES tbl_properties ]
[ AS select_stmt ]
You can find detailed explanations of the properties in Hive Data Definition Language. 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"}}'
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.
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. One approach is to put the required jars into the extlib directory:
$ cp curator-client-2.5.0.jar curator-framework-2.5.0.jar zookeeper-3.4.6.jar extlib/ $ cp scala-library-2.10.4.jar kafka-clients-0.8.2.1.jar kafka_2.10-0.8.2.1.jar metrics-core-2.2.0.jar extlib/ $ cp json-simple-1.1.1.jar extlib/ $ cp jackson-annotations-2.6.0.jar extlib/ $ cp storm-kafka-*.jar storm-sql-kafka-*.jar storm-sql-runtime-*.jar extlib/
The next step is to submit the SQL statements to StormSQL:
$ bin/storm sql order_filtering order_filtering.sql
By now you should be able to see the order_filtering topology in the Storm UI.
Aggregation, windowing and joining tables are yet to be implemented. Specifying parallelism hints in the topology is not yet supported. All processors have a parallelism hint of 1.
Users also need to provide the dependency of the external data sources in the extlib directory. Otherwise the topology will fail to run because of ClassNotFoundException.
The current implementation of the Kafka connector in StormSQL assumes both the input and the output are in JSON formats. The connector has not yet recognized the INPUTFORMAT and OUTPUTFORMAT clauses yet.