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Posted to commits@storm.apache.org by bo...@apache.org on 2016/03/22 16:30:59 UTC
[12/32] storm git commit: STORM-1617: 0.10.x release docs
http://git-wip-us.apache.org/repos/asf/storm/blob/d909db8f/docs/storm-hdfs.md
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+---
+title: Storm HDFS Integration
+layout: documentation
+documentation: true
+---
+
+Storm components for interacting with HDFS file systems
+
+
+## Usage
+The following example will write pipe("|")-delimited files to the HDFS path hdfs://localhost:54310/foo. After every
+1,000 tuples it will sync filesystem, making that data visible to other HDFS clients. It will rotate files when they
+reach 5 megabytes in size.
+
+```java
+// use "|" instead of "," for field delimiter
+RecordFormat format = new DelimitedRecordFormat()
+ .withFieldDelimiter("|");
+
+// sync the filesystem after every 1k tuples
+SyncPolicy syncPolicy = new CountSyncPolicy(1000);
+
+// rotate files when they reach 5MB
+FileRotationPolicy rotationPolicy = new FileSizeRotationPolicy(5.0f, Units.MB);
+
+FileNameFormat fileNameFormat = new DefaultFileNameFormat()
+ .withPath("/foo/");
+
+HdfsBolt bolt = new HdfsBolt()
+ .withFsUrl("hdfs://localhost:54310")
+ .withFileNameFormat(fileNameFormat)
+ .withRecordFormat(format)
+ .withRotationPolicy(rotationPolicy)
+ .withSyncPolicy(syncPolicy);
+```
+
+### Packaging a Topology
+When packaging your topology, it's important that you use the [maven-shade-plugin]() as opposed to the
+[maven-assembly-plugin]().
+
+The shade plugin provides facilities for merging JAR manifest entries, which the hadoop client leverages for URL scheme
+resolution.
+
+If you experience errors such as the following:
+
+```
+java.lang.RuntimeException: Error preparing HdfsBolt: No FileSystem for scheme: hdfs
+```
+
+it's an indication that your topology jar file isn't packaged properly.
+
+If you are using maven to create your topology jar, you should use the following `maven-shade-plugin` configuration to
+create your topology jar:
+
+```xml
+<plugin>
+ <groupId>org.apache.maven.plugins</groupId>
+ <artifactId>maven-shade-plugin</artifactId>
+ <version>1.4</version>
+ <configuration>
+ <createDependencyReducedPom>true</createDependencyReducedPom>
+ </configuration>
+ <executions>
+ <execution>
+ <phase>package</phase>
+ <goals>
+ <goal>shade</goal>
+ </goals>
+ <configuration>
+ <transformers>
+ <transformer
+ implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
+ <transformer
+ implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
+ <mainClass></mainClass>
+ </transformer>
+ </transformers>
+ </configuration>
+ </execution>
+ </executions>
+</plugin>
+
+```
+
+### Specifying a Hadoop Version
+By default, storm-hdfs uses the following Hadoop dependencies:
+
+```xml
+<dependency>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-client</artifactId>
+ <version>2.2.0</version>
+ <exclusions>
+ <exclusion>
+ <groupId>org.slf4j</groupId>
+ <artifactId>slf4j-log4j12</artifactId>
+ </exclusion>
+ </exclusions>
+</dependency>
+<dependency>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-hdfs</artifactId>
+ <version>2.2.0</version>
+ <exclusions>
+ <exclusion>
+ <groupId>org.slf4j</groupId>
+ <artifactId>slf4j-log4j12</artifactId>
+ </exclusion>
+ </exclusions>
+</dependency>
+```
+
+If you are using a different version of Hadoop, you should exclude the Hadoop libraries from the storm-hdfs dependency
+and add the dependencies for your preferred version in your pom.
+
+Hadoop client version incompatibilites can manifest as errors like:
+
+```
+com.google.protobuf.InvalidProtocolBufferException: Protocol message contained an invalid tag (zero)
+```
+
+## Customization
+
+### Record Formats
+Record format can be controlled by providing an implementation of the `org.apache.storm.hdfs.format.RecordFormat`
+interface:
+
+```java
+public interface RecordFormat extends Serializable {
+ byte[] format(Tuple tuple);
+}
+```
+
+The provided `org.apache.storm.hdfs.format.DelimitedRecordFormat` is capable of producing formats such as CSV and
+tab-delimited files.
+
+
+### File Naming
+File naming can be controlled by providing an implementation of the `org.apache.storm.hdfs.format.FileNameFormat`
+interface:
+
+```java
+public interface FileNameFormat extends Serializable {
+ void prepare(Map conf, TopologyContext topologyContext);
+ String getName(long rotation, long timeStamp);
+ String getPath();
+}
+```
+
+The provided `org.apache.storm.hdfs.format.DefaultFileNameFormat` will create file names with the following format:
+
+ {prefix}{componentId}-{taskId}-{rotationNum}-{timestamp}{extension}
+
+For example:
+
+ MyBolt-5-7-1390579837830.txt
+
+By default, prefix is empty and extenstion is ".txt".
+
+
+
+### Sync Policies
+Sync policies allow you to control when buffered data is flushed to the underlying filesystem (thus making it available
+to clients reading the data) by implementing the `org.apache.storm.hdfs.sync.SyncPolicy` interface:
+
+```java
+public interface SyncPolicy extends Serializable {
+ boolean mark(Tuple tuple, long offset);
+ void reset();
+}
+```
+The `HdfsBolt` will call the `mark()` method for every tuple it processes. Returning `true` will trigger the `HdfsBolt`
+to perform a sync/flush, after which it will call the `reset()` method.
+
+The `org.apache.storm.hdfs.sync.CountSyncPolicy` class simply triggers a sync after the specified number of tuples have
+been processed.
+
+### File Rotation Policies
+Similar to sync policies, file rotation policies allow you to control when data files are rotated by providing a
+`org.apache.storm.hdfs.rotation.FileRotation` interface:
+
+```java
+public interface FileRotationPolicy extends Serializable {
+ boolean mark(Tuple tuple, long offset);
+ void reset();
+}
+```
+
+The `org.apache.storm.hdfs.rotation.FileSizeRotationPolicy` implementation allows you to trigger file rotation when
+data files reach a specific file size:
+
+```java
+FileRotationPolicy rotationPolicy = new FileSizeRotationPolicy(5.0f, Units.MB);
+```
+
+### File Rotation Actions
+Both the HDFS bolt and Trident State implementation allow you to register any number of `RotationAction`s.
+What `RotationAction`s do is provide a hook to allow you to perform some action right after a file is rotated. For
+example, moving a file to a different location or renaming it.
+
+
+```java
+public interface RotationAction extends Serializable {
+ void execute(FileSystem fileSystem, Path filePath) throws IOException;
+}
+```
+
+Storm-HDFS includes a simple action that will move a file after rotation:
+
+```java
+public class MoveFileAction implements RotationAction {
+ private static final Logger LOG = LoggerFactory.getLogger(MoveFileAction.class);
+
+ private String destination;
+
+ public MoveFileAction withDestination(String destDir){
+ destination = destDir;
+ return this;
+ }
+
+ @Override
+ public void execute(FileSystem fileSystem, Path filePath) throws IOException {
+ Path destPath = new Path(destination, filePath.getName());
+ LOG.info("Moving file {} to {}", filePath, destPath);
+ boolean success = fileSystem.rename(filePath, destPath);
+ return;
+ }
+}
+```
+
+If you are using Trident and sequence files you can do something like this:
+
+```java
+ HdfsState.Options seqOpts = new HdfsState.SequenceFileOptions()
+ .withFileNameFormat(fileNameFormat)
+ .withSequenceFormat(new DefaultSequenceFormat("key", "data"))
+ .withRotationPolicy(rotationPolicy)
+ .withFsUrl("hdfs://localhost:54310")
+ .addRotationAction(new MoveFileAction().withDestination("/dest2/"));
+```
+
+
+## Support for HDFS Sequence Files
+
+The `org.apache.storm.hdfs.bolt.SequenceFileBolt` class allows you to write storm data to HDFS sequence files:
+
+```java
+ // sync the filesystem after every 1k tuples
+ SyncPolicy syncPolicy = new CountSyncPolicy(1000);
+
+ // rotate files when they reach 5MB
+ FileRotationPolicy rotationPolicy = new FileSizeRotationPolicy(5.0f, Units.MB);
+
+ FileNameFormat fileNameFormat = new DefaultFileNameFormat()
+ .withExtension(".seq")
+ .withPath("/data/");
+
+ // create sequence format instance.
+ DefaultSequenceFormat format = new DefaultSequenceFormat("timestamp", "sentence");
+
+ SequenceFileBolt bolt = new SequenceFileBolt()
+ .withFsUrl("hdfs://localhost:54310")
+ .withFileNameFormat(fileNameFormat)
+ .withSequenceFormat(format)
+ .withRotationPolicy(rotationPolicy)
+ .withSyncPolicy(syncPolicy)
+ .withCompressionType(SequenceFile.CompressionType.RECORD)
+ .withCompressionCodec("deflate");
+```
+
+The `SequenceFileBolt` requires that you provide a `org.apache.storm.hdfs.bolt.format.SequenceFormat` that maps tuples to
+key/value pairs:
+
+```java
+public interface SequenceFormat extends Serializable {
+ Class keyClass();
+ Class valueClass();
+
+ Writable key(Tuple tuple);
+ Writable value(Tuple tuple);
+}
+```
+
+## Trident API
+storm-hdfs also includes a Trident `state` implementation for writing data to HDFS, with an API that closely mirrors
+that of the bolts.
+
+ ```java
+ Fields hdfsFields = new Fields("field1", "field2");
+
+ FileNameFormat fileNameFormat = new DefaultFileNameFormat()
+ .withPath("/trident")
+ .withPrefix("trident")
+ .withExtension(".txt");
+
+ RecordFormat recordFormat = new DelimitedRecordFormat()
+ .withFields(hdfsFields);
+
+ FileRotationPolicy rotationPolicy = new FileSizeRotationPolicy(5.0f, FileSizeRotationPolicy.Units.MB);
+
+ HdfsState.Options options = new HdfsState.HdfsFileOptions()
+ .withFileNameFormat(fileNameFormat)
+ .withRecordFormat(recordFormat)
+ .withRotationPolicy(rotationPolicy)
+ .withFsUrl("hdfs://localhost:54310");
+
+ StateFactory factory = new HdfsStateFactory().withOptions(options);
+
+ TridentState state = stream
+ .partitionPersist(factory, hdfsFields, new HdfsUpdater(), new Fields());
+ ```
+
+ To use the sequence file `State` implementation, use the `HdfsState.SequenceFileOptions`:
+
+ ```java
+ HdfsState.Options seqOpts = new HdfsState.SequenceFileOptions()
+ .withFileNameFormat(fileNameFormat)
+ .withSequenceFormat(new DefaultSequenceFormat("key", "data"))
+ .withRotationPolicy(rotationPolicy)
+ .withFsUrl("hdfs://localhost:54310")
+ .addRotationAction(new MoveFileAction().toDestination("/dest2/"));
+```
+
+##Working with Secure HDFS
+If your topology is going to interact with secure HDFS, your bolts/states needs to be authenticated by NameNode. We
+currently have 2 options to support this:
+
+### Using HDFS delegation tokens
+Your administrator can configure nimbus to automatically get delegation tokens on behalf of the topology submitter user.
+The nimbus need to start with following configurations:
+
+nimbus.autocredential.plugins.classes : ["org.apache.storm.hdfs.common.security.AutoHDFS"]
+nimbus.credential.renewers.classes : ["org.apache.storm.hdfs.common.security.AutoHDFS"]
+hdfs.keytab.file: "/path/to/keytab/on/nimbus" (This is the keytab of hdfs super user that can impersonate other users.)
+hdfs.kerberos.principal: "superuser@EXAMPLE.com"
+nimbus.credential.renewers.freq.secs : 82800 (23 hours, hdfs tokens needs to be renewed every 24 hours so this value should be
+less then 24 hours.)
+topology.hdfs.uri:"hdfs://host:port" (This is an optional config, by default we will use value of "fs.defaultFS" property
+specified in hadoop's core-site.xml)
+
+Your topology configuration should have:
+topology.auto-credentials :["org.apache.storm.hdfs.common.security.AutoHDFS"]
+
+If nimbus did not have the above configuration you need to add it and then restart it. Ensure the hadoop configuration
+files(core-site.xml and hdfs-site.xml) and the storm-hdfs jar with all the dependencies is present in nimbus's classpath.
+Nimbus will use the keytab and principal specified in the config to authenticate with Namenode. From then on for every
+topology submission, nimbus will impersonate the topology submitter user and acquire delegation tokens on behalf of the
+topology submitter user. If topology was started with topology.auto-credentials set to AutoHDFS, nimbus will push the
+delegation tokens to all the workers for your topology and the hdfs bolt/state will authenticate with namenode using
+these tokens.
+
+As nimbus is impersonating topology submitter user, you need to ensure the user specified in hdfs.kerberos.principal
+has permissions to acquire tokens on behalf of other users. To achieve this you need to follow configuration directions
+listed on this link
+http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/Superusers.html
+
+You can read about setting up secure HDFS here: http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/SecureMode.html.
+
+### Using keytabs on all worker hosts
+If you have distributed the keytab files for hdfs user on all potential worker hosts then you can use this method. You should specify a
+hdfs config key using the method HdfsBolt/State.withconfigKey("somekey") and the value map of this key should have following 2 properties:
+
+hdfs.keytab.file: "/path/to/keytab/"
+hdfs.kerberos.principal: "user@EXAMPLE.com"
+
+On worker hosts the bolt/trident-state code will use the keytab file with principal provided in the config to authenticate with
+Namenode. This method is little dangerous as you need to ensure all workers have the keytab file at the same location and you need
+to remember this as you bring up new hosts in the cluster.
http://git-wip-us.apache.org/repos/asf/storm/blob/d909db8f/docs/storm-hive.md
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+---
+title: Storm Hive Integration
+layout: documentation
+documentation: true
+---
+
+ Hive offers streaming API that allows data to be written continuously into Hive. The incoming data
+ can be continuously committed in small batches of records into existing Hive partition or table. Once the data
+ is committed its immediately visible to all hive queries. More info on Hive Streaming API
+ https://cwiki.apache.org/confluence/display/Hive/Streaming+Data+Ingest
+
+ With the help of Hive Streaming API, HiveBolt and HiveState allows users to stream data from Storm into Hive directly.
+ To use Hive streaming API users need to create a bucketed table with ORC format. Example below
+
+ ```sql
+ create table test_table ( id INT, name STRING, phone STRING, street STRING) partitioned by (city STRING, state STRING) stored as orc tblproperties ("orc.compress"="NONE");
+ ```
+
+
+## HiveBolt (org.apache.storm.hive.bolt.HiveBolt)
+
+HiveBolt streams tuples directly into Hive. Tuples are written using Hive Transactions.
+Partitions to which HiveBolt will stream to can either created or pre-created or optionally
+HiveBolt can create them if they are missing. Fields from Tuples are mapped to table columns.
+User should make sure that Tuple field names are matched to the table column names.
+
+```java
+DelimitedRecordHiveMapper mapper = new DelimitedRecordHiveMapper()
+ .withColumnFields(new Fields(colNames));
+HiveOptions hiveOptions = new HiveOptions(metaStoreURI,dbName,tblName,mapper);
+HiveBolt hiveBolt = new HiveBolt(hiveOptions);
+```
+
+### RecordHiveMapper
+ This class maps Tuple field names to Hive table column names.
+ There are two implementaitons available
+
+
+ + DelimitedRecordHiveMapper (org.apache.storm.hive.bolt.mapper.DelimitedRecordHiveMapper)
+ + JsonRecordHiveMapper (org.apache.storm.hive.bolt.mapper.JsonRecordHiveMapper)
+
+ ```java
+ DelimitedRecordHiveMapper mapper = new DelimitedRecordHiveMapper()
+ .withColumnFields(new Fields(colNames))
+ .withPartitionFields(new Fields(partNames));
+ or
+ DelimitedRecordHiveMapper mapper = new DelimitedRecordHiveMapper()
+ .withColumnFields(new Fields(colNames))
+ .withTimeAsPartitionField("YYYY/MM/DD");
+ ```
+
+|Arg | Description | Type
+|--- |--- |---
+|withColumnFields| field names in a tuple to be mapped to table column names | Fields (required) |
+|withPartitionFields| field names in a tuple can be mapped to hive table partitions | Fields |
+|withTimeAsPartitionField| users can select system time as partition in hive table| String . Date format|
+
+### HiveOptions (org.apache.storm.hive.common.HiveOptions)
+
+HiveBolt takes in HiveOptions as a constructor arg.
+
+ ```java
+ HiveOptions hiveOptions = new HiveOptions(metaStoreURI,dbName,tblName,mapper)
+ .withTxnsPerBatch(10)
+ .withBatchSize(1000)
+ .withIdleTimeout(10)
+ ```
+
+
+HiveOptions params
+
+|Arg |Description | Type
+|--- |--- |---
+|metaStoreURI | hive meta store URI (can be found in hive-site.xml) | String (required) |
+|dbName | database name | String (required) |
+|tblName | table name | String (required) |
+|mapper| Mapper class to map Tuple field names to Table column names | DelimitedRecordHiveMapper or JsonRecordHiveMapper (required) |
+|withTxnsPerBatch | Hive grants a *batch of transactions* instead of single transactions to streaming clients like HiveBolt.This setting configures the number of desired transactions per Transaction Batch. Data from all transactions in a single batch end up in a single file. Flume will write a maximum of batchSize events in each transaction in the batch. This setting in conjunction with batchSize provides control over the size of each file. Note that eventually Hive will transparently compact these files into larger files.| Integer . default 100 |
+|withMaxOpenConnections| Allow only this number of open connections. If this number is exceeded, the least recently used connection is closed.| Integer . default 100|
+|withBatchSize| Max number of events written to Hive in a single Hive transaction| Integer. default 15000|
+|withCallTimeout| (In milliseconds) Timeout for Hive & HDFS I/O operations, such as openTxn, write, commit, abort. | Integer. default 10000|
+|withHeartBeatInterval| (In seconds) Interval between consecutive heartbeats sent to Hive to keep unused transactions from expiring. Set this value to 0 to disable heartbeats.| Integer. default 240 |
+|withAutoCreatePartitions| HiveBolt will automatically create the necessary Hive partitions to stream to. |Boolean. default true |
+|withKerberosPrinicipal| Kerberos user principal for accessing secure Hive | String|
+|withKerberosKeytab| Kerberos keytab for accessing secure Hive | String |
+|withTickTupleInterval| (In seconds) If > 0 then the Hive Bolt will periodically flush transaction batches. Enabling this is recommended to avoid tuple timeouts while waiting for a batch to fill up.| Integer. default 0|
+
+
+
+## HiveState (org.apache.storm.hive.trident.HiveTrident)
+
+Hive Trident state also follows similar pattern to HiveBolt it takes in HiveOptions as an arg.
+
+```java
+ DelimitedRecordHiveMapper mapper = new DelimitedRecordHiveMapper()
+ .withColumnFields(new Fields(colNames))
+ .withTimeAsPartitionField("YYYY/MM/DD");
+
+ HiveOptions hiveOptions = new HiveOptions(metaStoreURI,dbName,tblName,mapper)
+ .withTxnsPerBatch(10)
+ .withBatchSize(1000)
+ .withIdleTimeout(10)
+
+ StateFactory factory = new HiveStateFactory().withOptions(hiveOptions);
+ TridentState state = stream.partitionPersist(factory, hiveFields, new HiveUpdater(), new Fields());
+ ```
+
+
+
+
+
http://git-wip-us.apache.org/repos/asf/storm/blob/d909db8f/docs/storm-jdbc.md
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+---
+title: Storm JDBC Integration
+layout: documentation
+documentation: true
+---
+
+Storm/Trident integration for JDBC. This package includes the core bolts and trident states that allows a storm topology
+to either insert storm tuples in a database table or to execute select queries against a database and enrich tuples
+in a storm topology.
+
+**Note**: Throughout the examples below, we make use of com.google.common.collect.Lists and com.google.common.collect.Maps.
+
+## Inserting into a database.
+The bolt and trident state included in this package for inserting data into a database tables are tied to a single table.
+
+### ConnectionProvider
+An interface that should be implemented by different connection pooling mechanism `org.apache.storm.jdbc.common.ConnectionProvider`
+
+```java
+public interface ConnectionProvider extends Serializable {
+ /**
+ * method must be idempotent.
+ */
+ void prepare();
+
+ /**
+ *
+ * @return a DB connection over which the queries can be executed.
+ */
+ Connection getConnection();
+
+ /**
+ * called once when the system is shutting down, should be idempotent.
+ */
+ void cleanup();
+}
+```
+
+Out of the box we support `org.apache.storm.jdbc.common.HikariCPConnectionProvider` which is an implementation that uses HikariCP.
+
+###JdbcMapper
+The main API for inserting data in a table using JDBC is the `org.apache.storm.jdbc.mapper.JdbcMapper` interface:
+
+```java
+public interface JdbcMapper extends Serializable {
+ List<Column> getColumns(ITuple tuple);
+}
+```
+
+The `getColumns()` method defines how a storm tuple maps to a list of columns representing a row in a database.
+**The order of the returned list is important. The place holders in the supplied queries are resolved in the same order as returned list.**
+For example if the user supplied insert query is `insert into user(user_id, user_name, create_date) values (?,?, now())` the 1st item
+of the returned list of `getColumns` method will map to the 1st place holder and the 2nd to the 2nd and so on. We do not parse
+the supplied queries to try and resolve place holder by column names. Not making any assumptions about the query syntax allows this connector
+to be used by some non-standard sql frameworks like Pheonix which only supports upsert into.
+
+### JdbcInsertBolt
+To use the `JdbcInsertBolt`, you construct an instance of it by specifying a `ConnectionProvider` implementation
+and a `JdbcMapper` implementation that converts storm tuple to DB row. In addition, you must either supply
+a table name using `withTableName` method or an insert query using `withInsertQuery`.
+If you specify a insert query you should ensure that your `JdbcMapper` implementation will return a list of columns in the same order as in your insert query.
+You can optionally specify a query timeout seconds param that specifies max seconds an insert query can take.
+The default is set to value of topology.message.timeout.secs and a value of -1 will indicate not to set any query timeout.
+You should set the query timeout value to be <= topology.message.timeout.secs.
+
+ ```java
+Map hikariConfigMap = Maps.newHashMap();
+hikariConfigMap.put("dataSourceClassName","com.mysql.jdbc.jdbc2.optional.MysqlDataSource");
+hikariConfigMap.put("dataSource.url", "jdbc:mysql://localhost/test");
+hikariConfigMap.put("dataSource.user","root");
+hikariConfigMap.put("dataSource.password","password");
+ConnectionProvider connectionProvider = new HikariCPConnectionProvider(hikariConfigMap);
+
+String tableName = "user_details";
+JdbcMapper simpleJdbcMapper = new SimpleJdbcMapper(tableName, connectionProvider);
+
+JdbcInsertBolt userPersistanceBolt = new JdbcInsertBolt(connectionProvider, simpleJdbcMapper)
+ .withTableName("user")
+ .withQueryTimeoutSecs(30);
+ Or
+JdbcInsertBolt userPersistanceBolt = new JdbcInsertBolt(connectionProvider, simpleJdbcMapper)
+ .withInsertQuery("insert into user values (?,?)")
+ .withQueryTimeoutSecs(30);
+ ```
+
+### SimpleJdbcMapper
+`storm-jdbc` includes a general purpose `JdbcMapper` implementation called `SimpleJdbcMapper` that can map Storm
+tuple to a Database row. `SimpleJdbcMapper` assumes that the storm tuple has fields with same name as the column name in
+the database table that you intend to write to.
+
+To use `SimpleJdbcMapper`, you simply tell it the tableName that you want to write to and provide a connectionProvider instance.
+
+The following code creates a `SimpleJdbcMapper` instance that:
+
+1. Will allow the mapper to transform a storm tuple to a list of columns mapping to a row in table test.user_details.
+2. Will use the provided HikariCP configuration to establish a connection pool with specified Database configuration and
+automatically figure out the column names and corresponding data types of the table that you intend to write to.
+Please see https://github.com/brettwooldridge/HikariCP#configuration-knobs-baby to learn more about hikari configuration properties.
+
+```java
+Map hikariConfigMap = Maps.newHashMap();
+hikariConfigMap.put("dataSourceClassName","com.mysql.jdbc.jdbc2.optional.MysqlDataSource");
+hikariConfigMap.put("dataSource.url", "jdbc:mysql://localhost/test");
+hikariConfigMap.put("dataSource.user","root");
+hikariConfigMap.put("dataSource.password","password");
+ConnectionProvider connectionProvider = new HikariCPConnectionProvider(hikariConfigMap);
+String tableName = "user_details";
+JdbcMapper simpleJdbcMapper = new SimpleJdbcMapper(tableName, connectionProvider);
+```
+The mapper initialized in the example above assumes a storm tuple has value for all the columns of the table you intend to insert data into and its `getColumn`
+method will return the columns in the order in which Jdbc connection instance's `connection.getMetaData().getColumns();` method returns them.
+
+**If you specified your own insert query to `JdbcInsertBolt` you must initialize `SimpleJdbcMapper` with explicit columnschema such that the schema has columns in the same order as your insert queries.**
+For example if your insert query is `Insert into user (user_id, user_name) values (?,?)` then your `SimpleJdbcMapper` should be initialized with the following statements:
+```java
+List<Column> columnSchema = Lists.newArrayList(
+ new Column("user_id", java.sql.Types.INTEGER),
+ new Column("user_name", java.sql.Types.VARCHAR));
+JdbcMapper simpleJdbcMapper = new SimpleJdbcMapper(columnSchema);
+```
+
+If your storm tuple only has fields for a subset of columns i.e. if some of the columns in your table have default values and you want to only insert values for columns with no default values you can enforce the behavior by initializing the
+`SimpleJdbcMapper` with explicit columnschema. For example, if you have a user_details table `create table if not exists user_details (user_id integer, user_name varchar(100), dept_name varchar(100), create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP);`
+In this table the create_time column has a default value. To ensure only the columns with no default values are inserted
+you can initialize the `jdbcMapper` as below:
+
+```java
+List<Column> columnSchema = Lists.newArrayList(
+ new Column("user_id", java.sql.Types.INTEGER),
+ new Column("user_name", java.sql.Types.VARCHAR),
+ new Column("dept_name", java.sql.Types.VARCHAR));
+JdbcMapper simpleJdbcMapper = new SimpleJdbcMapper(columnSchema);
+```
+### JdbcTridentState
+We also support a trident persistent state that can be used with trident topologies. To create a jdbc persistent trident
+state you need to initialize it with the table name or an insert query, the JdbcMapper instance and connection provider instance.
+See the example below:
+
+```java
+JdbcState.Options options = new JdbcState.Options()
+ .withConnectionProvider(connectionProvider)
+ .withMapper(jdbcMapper)
+ .withTableName("user_details")
+ .withQueryTimeoutSecs(30);
+JdbcStateFactory jdbcStateFactory = new JdbcStateFactory(options);
+```
+similar to `JdbcInsertBolt` you can specify a custom insert query using `withInsertQuery` instead of specifying a table name.
+
+## Lookup from Database
+We support `select` queries from databases to allow enrichment of storm tuples in a topology. The main API for
+executing select queries against a database using JDBC is the `org.apache.storm.jdbc.mapper.JdbcLookupMapper` interface:
+
+```java
+ void declareOutputFields(OutputFieldsDeclarer declarer);
+ List<Column> getColumns(ITuple tuple);
+ List<Values> toTuple(ITuple input, List<Column> columns);
+```
+
+The `declareOutputFields` method is used to indicate what fields will be emitted as part of output tuple of processing a storm
+tuple.
+
+The `getColumns` method specifies the place holder columns in a select query and their SQL type and the value to use.
+For example in the user_details table mentioned above if you were executing a query `select user_name from user_details where
+user_id = ? and create_time > ?` the `getColumns` method would take a storm input tuple and return a List containing two items.
+The first instance of `Column` type's `getValue()` method will be used as the value of `user_id` to lookup for and the
+second instance of `Column` type's `getValue()` method will be used as the value of `create_time`.
+**Note: the order in the returned list determines the place holder's value. In other words the first item in the list maps
+to first `?` in select query, the second item to second `?` in query and so on.**
+
+The `toTuple` method takes in the input tuple and a list of columns representing a DB row as a result of the select query
+and returns a list of values to be emitted.
+**Please note that it returns a list of `Values` and not just a single instance of `Values`.**
+This allows a for a single DB row to be mapped to multiple output storm tuples.
+
+###SimpleJdbcLookupMapper
+`storm-jdbc` includes a general purpose `JdbcLookupMapper` implementation called `SimpleJdbcLookupMapper`.
+
+To use `SimpleJdbcMapper`, you have to initialize it with the fields that will be outputted by your bolt and the list of
+columns that are used in your select query as place holder. The following example shows initialization of a `SimpleJdbcLookupMapper`
+that declares `user_id,user_name,create_date` as output fields and `user_id` as the place holder column in select query.
+SimpleJdbcMapper assumes the field name in your tuple is equal to the place holder column name, i.e. in our example
+`SimpleJdbcMapper` will look for a field `use_id` in the input tuple and use its value as the place holder's value in the
+select query. For constructing output tuples, it looks for fields specified in `outputFields` in the input tuple first,
+and if it is not found in input tuple then it looks at select query's output row for a column with same name as field name.
+So in the example below if the input tuple had fields `user_id, create_date` and the select query was
+`select user_name from user_details where user_id = ?`, For each input tuple `SimpleJdbcLookupMapper.getColumns(tuple)`
+will return the value of `tuple.getValueByField("user_id")` which will be used as the value in `?` of select query.
+For each output row from DB, `SimpleJdbcLookupMapper.toTuple()` will use the `user_id, create_date` from the input tuple as
+is adding only `user_name` from the resulting row and returning these 3 fields as a single output tuple.
+
+```java
+Fields outputFields = new Fields("user_id", "user_name", "create_date");
+List<Column> queryParamColumns = Lists.newArrayList(new Column("user_id", Types.INTEGER));
+this.jdbcLookupMapper = new SimpleJdbcLookupMapper(outputFields, queryParamColumns);
+```
+
+### JdbcLookupBolt
+To use the `JdbcLookupBolt`, construct an instance of it using a `ConnectionProvider` instance, `JdbcLookupMapper` instance and the select query to execute.
+You can optionally specify a query timeout seconds param that specifies max seconds the select query can take.
+The default is set to value of topology.message.timeout.secs. You should set this value to be <= topology.message.timeout.secs.
+
+```java
+String selectSql = "select user_name from user_details where user_id = ?";
+SimpleJdbcLookupMapper lookupMapper = new SimpleJdbcLookupMapper(outputFields, queryParamColumns)
+JdbcLookupBolt userNameLookupBolt = new JdbcLookupBolt(connectionProvider, selectSql, lookupMapper)
+ .withQueryTimeoutSecs(30);
+```
+
+### JdbcTridentState for lookup
+We also support a trident query state that can be used with trident topologies.
+
+```java
+JdbcState.Options options = new JdbcState.Options()
+ .withConnectionProvider(connectionProvider)
+ .withJdbcLookupMapper(new SimpleJdbcLookupMapper(new Fields("user_name"), Lists.newArrayList(new Column("user_id", Types.INTEGER))))
+ .withSelectQuery("select user_name from user_details where user_id = ?");
+ .withQueryTimeoutSecs(30);
+```
+
+## Example:
+A runnable example can be found in the `src/test/java/topology` directory.
+
+### Setup
+* Ensure you have included JDBC implementation dependency for your chosen database as part of your build configuration.
+* The test topologies executes the following queries so your intended DB must support these queries for test topologies
+to work.
+```SQL
+create table if not exists user (user_id integer, user_name varchar(100), dept_name varchar(100), create_date date);
+create table if not exists department (dept_id integer, dept_name varchar(100));
+create table if not exists user_department (user_id integer, dept_id integer);
+insert into department values (1, 'R&D');
+insert into department values (2, 'Finance');
+insert into department values (3, 'HR');
+insert into department values (4, 'Sales');
+insert into user_department values (1, 1);
+insert into user_department values (2, 2);
+insert into user_department values (3, 3);
+insert into user_department values (4, 4);
+select dept_name from department, user_department where department.dept_id = user_department.dept_id and user_department.user_id = ?;
+```
+### Execution
+Run the `org.apache.storm.jdbc.topology.UserPersistanceTopology` class using storm jar command. The class expects 5 args
+storm jar org.apache.storm.jdbc.topology.UserPersistanceTopology <dataSourceClassName> <dataSource.url> <user> <password> [topology name]
+
+To make it work with Mysql, you can add the following to the pom.xml
+
+```
+<dependency>
+ <groupId>mysql</groupId>
+ <artifactId>mysql-connector-java</artifactId>
+ <version>5.1.31</version>
+</dependency>
+```
+
+You can generate a single jar with dependencies using mvn assembly plugin. To use the plugin add the following to your pom.xml and execute
+`mvn clean compile assembly:single`
+
+```
+<plugin>
+ <artifactId>maven-assembly-plugin</artifactId>
+ <configuration>
+ <archive>
+ <manifest>
+ <mainClass>fully.qualified.MainClass</mainClass>
+ </manifest>
+ </archive>
+ <descriptorRefs>
+ <descriptorRef>jar-with-dependencies</descriptorRef>
+ </descriptorRefs>
+ </configuration>
+</plugin>
+```
+
+Mysql Example:
+```
+storm jar ~/repo/incubator-storm/external/storm-jdbc/target/storm-jdbc-0.10.0-SNAPSHOT-jar-with-dependencies.jar org.apache.storm.jdbc.topology.UserPersistanceTopology com.mysql.jdbc.jdbc2.optional.MysqlDataSource jdbc:mysql://localhost/test root password UserPersistenceTopology
+```
+
+You can execute a select query against the user table which should show newly inserted rows:
+
+```
+select * from user;
+```
+
+For trident you can view `org.apache.storm.jdbc.topology.UserPersistanceTridentTopology`.
http://git-wip-us.apache.org/repos/asf/storm/blob/d909db8f/docs/storm-kafka.md
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diff --git a/docs/storm-kafka.md b/docs/storm-kafka.md
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+---
+title: Storm Kafka Integration
+layout: documentation
+documentation: true
+---
+
+Provides core Storm and Trident spout implementations for consuming data from Apache Kafka 0.8.x.
+
+##Spouts
+We support both Trident and core Storm spouts. For both spout implementations, we use a BrokerHost interface that
+tracks Kafka broker host to partition mapping and kafkaConfig that controls some Kafka related parameters.
+
+###BrokerHosts
+In order to initialize your Kafka spout/emitter you need to construct an instance of the marker interface BrokerHosts.
+Currently, we support the following two implementations:
+
+####ZkHosts
+ZkHosts is what you should use if you want to dynamically track Kafka broker to partition mapping. This class uses
+Kafka's ZooKeeper entries to track brokerHost -> partition mapping. You can instantiate an object by calling
+```java
+ public ZkHosts(String brokerZkStr, String brokerZkPath)
+ public ZkHosts(String brokerZkStr)
+```
+Where brokerZkStr is just ip:port (e.g. localhost:2181). brokerZkPath is the root directory under which all the topics and
+partition information is stored. By default this is /brokers which is what the default Kafka implementation uses.
+
+By default, the broker-partition mapping is refreshed every 60 seconds from ZooKeeper. If you want to change it, you
+should set host.refreshFreqSecs to your chosen value.
+
+####StaticHosts
+This is an alternative implementation where broker -> partition information is static. In order to construct an instance
+of this class, you need to first construct an instance of GlobalPartitionInformation.
+
+```java
+ Broker brokerForPartition0 = new Broker("localhost");//localhost:9092
+ Broker brokerForPartition1 = new Broker("localhost", 9092);//localhost:9092 but we specified the port explicitly
+ Broker brokerForPartition2 = new Broker("localhost:9092");//localhost:9092 specified as one string.
+ GlobalPartitionInformation partitionInfo = new GlobalPartitionInformation();
+ partitionInfo.addPartition(0, brokerForPartition0);//mapping from partition 0 to brokerForPartition0
+ partitionInfo.addPartition(1, brokerForPartition1);//mapping from partition 1 to brokerForPartition1
+ partitionInfo.addPartition(2, brokerForPartition2);//mapping from partition 2 to brokerForPartition2
+ StaticHosts hosts = new StaticHosts(partitionInfo);
+```
+
+###KafkaConfig
+The second thing needed for constructing a kafkaSpout is an instance of KafkaConfig.
+```java
+ public KafkaConfig(BrokerHosts hosts, String topic)
+ public KafkaConfig(BrokerHosts hosts, String topic, String clientId)
+```
+
+The BrokerHosts can be any implementation of BrokerHosts interface as described above. The topic is name of Kafka topic.
+The optional ClientId is used as a part of the ZooKeeper path where the spout's current consumption offset is stored.
+
+There are 2 extensions of KafkaConfig currently in use.
+
+Spoutconfig is an extension of KafkaConfig that supports additional fields with ZooKeeper connection info and for controlling
+behavior specific to KafkaSpout. The Zkroot will be used as root to store your consumer's offset. The id should uniquely
+identify your spout.
+```java
+public SpoutConfig(BrokerHosts hosts, String topic, String zkRoot, String id);
+public SpoutConfig(BrokerHosts hosts, String topic, String id);
+```
+In addition to these parameters, SpoutConfig contains the following fields that control how KafkaSpout behaves:
+```java
+ // setting for how often to save the current Kafka offset to ZooKeeper
+ public long stateUpdateIntervalMs = 2000;
+
+ // Exponential back-off retry settings. These are used when retrying messages after a bolt
+ // calls OutputCollector.fail().
+ // Note: be sure to set backtype.storm.Config.MESSAGE_TIMEOUT_SECS appropriately to prevent
+ // resubmitting the message while still retrying.
+ public long retryInitialDelayMs = 0;
+ public double retryDelayMultiplier = 1.0;
+ public long retryDelayMaxMs = 60 * 1000;
+
+ // if set to true, spout will set Kafka topic as the emitted Stream ID
+ public boolean topicAsStreamId = false;
+```
+Core KafkaSpout only accepts an instance of SpoutConfig.
+
+TridentKafkaConfig is another extension of KafkaConfig.
+TridentKafkaEmitter only accepts TridentKafkaConfig.
+
+The KafkaConfig class also has bunch of public variables that controls your application's behavior. Here are defaults:
+```java
+ public int fetchSizeBytes = 1024 * 1024;
+ public int socketTimeoutMs = 10000;
+ public int fetchMaxWait = 10000;
+ public int bufferSizeBytes = 1024 * 1024;
+ public MultiScheme scheme = new RawMultiScheme();
+ public boolean ignoreZkOffsets = false;
+ public long startOffsetTime = kafka.api.OffsetRequest.EarliestTime();
+ public long maxOffsetBehind = Long.MAX_VALUE;
+ public boolean useStartOffsetTimeIfOffsetOutOfRange = true;
+ public int metricsTimeBucketSizeInSecs = 60;
+```
+
+Most of them are self explanatory except MultiScheme.
+###MultiScheme
+MultiScheme is an interface that dictates how the byte[] consumed from Kafka gets transformed into a storm tuple. It
+also controls the naming of your output field.
+
+```java
+ public Iterable<List<Object>> deserialize(byte[] ser);
+ public Fields getOutputFields();
+```
+
+The default `RawMultiScheme` just takes the `byte[]` and returns a tuple with `byte[]` as is. The name of the
+outputField is "bytes". There are alternative implementation like `SchemeAsMultiScheme` and
+`KeyValueSchemeAsMultiScheme` which can convert the `byte[]` to `String`.
+
+
+### Examples
+
+#### Core Spout
+
+```java
+BrokerHosts hosts = new ZkHosts(zkConnString);
+SpoutConfig spoutConfig = new SpoutConfig(hosts, topicName, "/" + topicName, UUID.randomUUID().toString());
+spoutConfig.scheme = new SchemeAsMultiScheme(new StringScheme());
+KafkaSpout kafkaSpout = new KafkaSpout(spoutConfig);
+```
+
+#### Trident Spout
+```java
+TridentTopology topology = new TridentTopology();
+BrokerHosts zk = new ZkHosts("localhost");
+TridentKafkaConfig spoutConf = new TridentKafkaConfig(zk, "test-topic");
+spoutConf.scheme = new SchemeAsMultiScheme(new StringScheme());
+OpaqueTridentKafkaSpout spout = new OpaqueTridentKafkaSpout(spoutConf);
+```
+
+
+### How KafkaSpout stores offsets of a Kafka topic and recovers in case of failures
+
+As shown in the above KafkaConfig properties, you can control from where in the Kafka topic the spout begins to read by
+setting `KafkaConfig.startOffsetTime` as follows:
+
+1. `kafka.api.OffsetRequest.EarliestTime()`: read from the beginning of the topic (i.e. from the oldest messages onwards)
+2. `kafka.api.OffsetRequest.LatestTime()`: read from the end of the topic (i.e. any new messsages that are being written to the topic)
+3. A Unix timestamp aka seconds since the epoch (e.g. via `System.currentTimeMillis()`):
+ see [How do I accurately get offsets of messages for a certain timestamp using OffsetRequest?](https://cwiki.apache.org/confluence/display/KAFKA/FAQ#FAQ-HowdoIaccuratelygetoffsetsofmessagesforacertaintimestampusingOffsetRequest?) in the Kafka FAQ
+
+As the topology runs the Kafka spout keeps track of the offsets it has read and emitted by storing state information
+under the ZooKeeper path `SpoutConfig.zkRoot+ "/" + SpoutConfig.id`. In the case of failures it recovers from the last
+written offset in ZooKeeper.
+
+> **Important:** When re-deploying a topology make sure that the settings for `SpoutConfig.zkRoot` and `SpoutConfig.id`
+> were not modified, otherwise the spout will not be able to read its previous consumer state information (i.e. the
+> offsets) from ZooKeeper -- which may lead to unexpected behavior and/or to data loss, depending on your use case.
+
+This means that when a topology has run once the setting `KafkaConfig.startOffsetTime` will not have an effect for
+subsequent runs of the topology because now the topology will rely on the consumer state information (offsets) in
+ZooKeeper to determine from where it should begin (more precisely: resume) reading.
+If you want to force the spout to ignore any consumer state information stored in ZooKeeper, then you should
+set the parameter `KafkaConfig.ignoreZkOffsets` to `true`. If `true`, the spout will always begin reading from the
+offset defined by `KafkaConfig.startOffsetTime` as described above.
+
+
+## Using storm-kafka with different versions of Scala
+
+Storm-kafka's Kafka dependency is defined as `provided` scope in maven, meaning it will not be pulled in
+as a transitive dependency. This allows you to use a version of Kafka built against a specific Scala version.
+
+When building a project with storm-kafka, you must explicitly add the Kafka dependency. For example, to
+use Kafka 0.8.1.1 built against Scala 2.10, you would use the following dependency in your `pom.xml`:
+
+```xml
+ <dependency>
+ <groupId>org.apache.kafka</groupId>
+ <artifactId>kafka_2.10</artifactId>
+ <version>0.8.1.1</version>
+ <exclusions>
+ <exclusion>
+ <groupId>org.apache.zookeeper</groupId>
+ <artifactId>zookeeper</artifactId>
+ </exclusion>
+ <exclusion>
+ <groupId>log4j</groupId>
+ <artifactId>log4j</artifactId>
+ </exclusion>
+ </exclusions>
+ </dependency>
+```
+
+Note that the ZooKeeper and log4j dependencies are excluded to prevent version conflicts with Storm's dependencies.
+
+##Writing to Kafka as part of your topology
+You can create an instance of storm.kafka.bolt.KafkaBolt and attach it as a component to your topology or if you
+are using trident you can use storm.kafka.trident.TridentState, storm.kafka.trident.TridentStateFactory and
+storm.kafka.trident.TridentKafkaUpdater.
+
+You need to provide implementation of following 2 interfaces
+
+###TupleToKafkaMapper and TridentTupleToKafkaMapper
+These interfaces have 2 methods defined:
+
+```java
+ K getKeyFromTuple(Tuple/TridentTuple tuple);
+ V getMessageFromTuple(Tuple/TridentTuple tuple);
+```
+
+As the name suggests, these methods are called to map a tuple to Kafka key and Kafka message. If you just want one field
+as key and one field as value, then you can use the provided FieldNameBasedTupleToKafkaMapper.java
+implementation. In the KafkaBolt, the implementation always looks for a field with field name "key" and "message" if you
+use the default constructor to construct FieldNameBasedTupleToKafkaMapper for backward compatibility
+reasons. Alternatively you could also specify a different key and message field by using the non default constructor.
+In the TridentKafkaState you must specify what is the field name for key and message as there is no default constructor.
+These should be specified while constructing and instance of FieldNameBasedTupleToKafkaMapper.
+
+###KafkaTopicSelector and trident KafkaTopicSelector
+This interface has only one method
+```java
+public interface KafkaTopicSelector {
+ String getTopics(Tuple/TridentTuple tuple);
+}
+```
+The implementation of this interface should return the topic to which the tuple's key/message mapping needs to be published
+You can return a null and the message will be ignored. If you have one static topic name then you can use
+DefaultTopicSelector.java and set the name of the topic in the constructor.
+
+### Specifying Kafka producer properties
+You can provide all the produce properties , see http://kafka.apache.org/documentation.html#producerconfigs
+section "Important configuration properties for the producer", in your Storm topology config by setting the properties
+map with key kafka.broker.properties.
+
+###Putting it all together
+
+For the bolt :
+```java
+ TopologyBuilder builder = new TopologyBuilder();
+
+ Fields fields = new Fields("key", "message");
+ FixedBatchSpout spout = new FixedBatchSpout(fields, 4,
+ new Values("storm", "1"),
+ new Values("trident", "1"),
+ new Values("needs", "1"),
+ new Values("javadoc", "1")
+ );
+ spout.setCycle(true);
+ builder.setSpout("spout", spout, 5);
+ KafkaBolt bolt = new KafkaBolt()
+ .withTopicSelector(new DefaultTopicSelector("test"))
+ .withTupleToKafkaMapper(new FieldNameBasedTupleToKafkaMapper());
+ builder.setBolt("forwardToKafka", bolt, 8).shuffleGrouping("spout");
+
+ Config conf = new Config();
+ //set producer properties.
+ Properties props = new Properties();
+ props.put("metadata.broker.list", "localhost:9092");
+ props.put("request.required.acks", "1");
+ props.put("serializer.class", "kafka.serializer.StringEncoder");
+ conf.put(KafkaBolt.KAFKA_BROKER_PROPERTIES, props);
+
+ StormSubmitter.submitTopology("kafkaboltTest", conf, builder.createTopology());
+```
+
+For Trident:
+
+```java
+ Fields fields = new Fields("word", "count");
+ FixedBatchSpout spout = new FixedBatchSpout(fields, 4,
+ new Values("storm", "1"),
+ new Values("trident", "1"),
+ new Values("needs", "1"),
+ new Values("javadoc", "1")
+ );
+ spout.setCycle(true);
+
+ TridentTopology topology = new TridentTopology();
+ Stream stream = topology.newStream("spout1", spout);
+
+ TridentKafkaStateFactory stateFactory = new TridentKafkaStateFactory()
+ .withKafkaTopicSelector(new DefaultTopicSelector("test"))
+ .withTridentTupleToKafkaMapper(new FieldNameBasedTupleToKafkaMapper("word", "count"));
+ stream.partitionPersist(stateFactory, fields, new TridentKafkaUpdater(), new Fields());
+
+ Config conf = new Config();
+ //set producer properties.
+ Properties props = new Properties();
+ props.put("metadata.broker.list", "localhost:9092");
+ props.put("request.required.acks", "1");
+ props.put("serializer.class", "kafka.serializer.StringEncoder");
+ conf.put(TridentKafkaState.KAFKA_BROKER_PROPERTIES, props);
+ StormSubmitter.submitTopology("kafkaTridentTest", conf, topology.build());
+```
http://git-wip-us.apache.org/repos/asf/storm/blob/d909db8f/docs/storm-redis.md
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diff --git a/docs/storm-redis.md b/docs/storm-redis.md
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+---
+title: Storm Redis Integration
+layout: documentation
+documentation: true
+---
+
+Storm/Trident integration for [Redis](http://redis.io/)
+
+Storm-redis uses Jedis for Redis client.
+
+## Usage
+
+### How do I use it?
+
+use it as a maven dependency:
+
+```xml
+<dependency>
+ <groupId>org.apache.storm</groupId>
+ <artifactId>storm-redis</artifactId>
+ <version>${storm.version}</version>
+ <type>jar</type>
+</dependency>
+```
+
+### For normal Bolt
+
+Storm-redis provides basic Bolt implementations, ```RedisLookupBolt``` and ```RedisStoreBolt```.
+
+As name represents its usage, ```RedisLookupBolt``` retrieves value from Redis using key, and ```RedisStoreBolt``` stores key / value to Redis. One tuple will be matched to one key / value pair, and you can define match pattern to ```TupleMapper```.
+
+You can also choose data type from ```RedisDataTypeDescription``` to use. Please refer ```RedisDataTypeDescription.RedisDataType``` to see what data types are supported. In some data types (hash and sorted set), it requires additional key and converted key from tuple becomes element.
+
+These interfaces are combined with ```RedisLookupMapper``` and ```RedisStoreMapper``` which fit ```RedisLookupBolt``` and ```RedisStoreBolt``` respectively.
+
+#### RedisLookupBolt example
+
+```java
+
+class WordCountRedisLookupMapper implements RedisLookupMapper {
+ private RedisDataTypeDescription description;
+ private final String hashKey = "wordCount";
+
+ public WordCountRedisLookupMapper() {
+ description = new RedisDataTypeDescription(
+ RedisDataTypeDescription.RedisDataType.HASH, hashKey);
+ }
+
+ @Override
+ public List<Values> toTuple(ITuple input, Object value) {
+ String member = getKeyFromTuple(input);
+ List<Values> values = Lists.newArrayList();
+ values.add(new Values(member, value));
+ return values;
+ }
+
+ @Override
+ public void declareOutputFields(OutputFieldsDeclarer declarer) {
+ declarer.declare(new Fields("wordName", "count"));
+ }
+
+ @Override
+ public RedisDataTypeDescription getDataTypeDescription() {
+ return description;
+ }
+
+ @Override
+ public String getKeyFromTuple(ITuple tuple) {
+ return tuple.getStringByField("word");
+ }
+
+ @Override
+ public String getValueFromTuple(ITuple tuple) {
+ return null;
+ }
+}
+
+```
+
+```java
+
+JedisPoolConfig poolConfig = new JedisPoolConfig.Builder()
+ .setHost(host).setPort(port).build();
+RedisLookupMapper lookupMapper = new WordCountRedisLookupMapper();
+RedisLookupBolt lookupBolt = new RedisLookupBolt(poolConfig, lookupMapper);
+```
+
+#### RedisStoreBolt example
+
+```java
+
+class WordCountStoreMapper implements RedisStoreMapper {
+ private RedisDataTypeDescription description;
+ private final String hashKey = "wordCount";
+
+ public WordCountStoreMapper() {
+ description = new RedisDataTypeDescription(
+ RedisDataTypeDescription.RedisDataType.HASH, hashKey);
+ }
+
+ @Override
+ public RedisDataTypeDescription getDataTypeDescription() {
+ return description;
+ }
+
+ @Override
+ public String getKeyFromTuple(ITuple tuple) {
+ return tuple.getStringByField("word");
+ }
+
+ @Override
+ public String getValueFromTuple(ITuple tuple) {
+ return tuple.getStringByField("count");
+ }
+}
+```
+
+```java
+
+JedisPoolConfig poolConfig = new JedisPoolConfig.Builder()
+ .setHost(host).setPort(port).build();
+RedisStoreMapper storeMapper = new WordCountStoreMapper();
+RedisStoreBolt storeBolt = new RedisStoreBolt(poolConfig, storeMapper);
+```
+
+### For non-simple Bolt
+
+If your scenario doesn't fit ```RedisStoreBolt``` and ```RedisLookupBolt```, storm-redis also provides ```AbstractRedisBolt``` to let you extend and apply your business logic.
+
+```java
+
+ public static class LookupWordTotalCountBolt extends AbstractRedisBolt {
+ private static final Logger LOG = LoggerFactory.getLogger(LookupWordTotalCountBolt.class);
+ private static final Random RANDOM = new Random();
+
+ public LookupWordTotalCountBolt(JedisPoolConfig config) {
+ super(config);
+ }
+
+ public LookupWordTotalCountBolt(JedisClusterConfig config) {
+ super(config);
+ }
+
+ @Override
+ public void execute(Tuple input) {
+ JedisCommands jedisCommands = null;
+ try {
+ jedisCommands = getInstance();
+ String wordName = input.getStringByField("word");
+ String countStr = jedisCommands.get(wordName);
+ if (countStr != null) {
+ int count = Integer.parseInt(countStr);
+ this.collector.emit(new Values(wordName, count));
+
+ // print lookup result with low probability
+ if(RANDOM.nextInt(1000) > 995) {
+ LOG.info("Lookup result - word : " + wordName + " / count : " + count);
+ }
+ } else {
+ // skip
+ LOG.warn("Word not found in Redis - word : " + wordName);
+ }
+ } finally {
+ if (jedisCommands != null) {
+ returnInstance(jedisCommands);
+ }
+ this.collector.ack(input);
+ }
+ }
+
+ @Override
+ public void declareOutputFields(OutputFieldsDeclarer declarer) {
+ // wordName, count
+ declarer.declare(new Fields("wordName", "count"));
+ }
+ }
+
+```
+
+### Trident State usage
+
+1. RedisState and RedisMapState, which provide Jedis interface just for single redis.
+
+2. RedisClusterState and RedisClusterMapState, which provide JedisCluster interface, just for redis cluster.
+
+RedisState
+```java
+ JedisPoolConfig poolConfig = new JedisPoolConfig.Builder()
+ .setHost(redisHost).setPort(redisPort)
+ .build();
+ RedisStoreMapper storeMapper = new WordCountStoreMapper();
+ RedisLookupMapper lookupMapper = new WordCountLookupMapper();
+ RedisState.Factory factory = new RedisState.Factory(poolConfig);
+
+ TridentTopology topology = new TridentTopology();
+ Stream stream = topology.newStream("spout1", spout);
+
+ stream.partitionPersist(factory,
+ fields,
+ new RedisStateUpdater(storeMapper).withExpire(86400000),
+ new Fields());
+
+ TridentState state = topology.newStaticState(factory);
+ stream = stream.stateQuery(state, new Fields("word"),
+ new RedisStateQuerier(lookupMapper),
+ new Fields("columnName","columnValue"));
+```
+
+RedisClusterState
+```java
+ Set<InetSocketAddress> nodes = new HashSet<InetSocketAddress>();
+ for (String hostPort : redisHostPort.split(",")) {
+ String[] host_port = hostPort.split(":");
+ nodes.add(new InetSocketAddress(host_port[0], Integer.valueOf(host_port[1])));
+ }
+ JedisClusterConfig clusterConfig = new JedisClusterConfig.Builder().setNodes(nodes)
+ .build();
+ RedisStoreMapper storeMapper = new WordCountStoreMapper();
+ RedisLookupMapper lookupMapper = new WordCountLookupMapper();
+ RedisClusterState.Factory factory = new RedisClusterState.Factory(clusterConfig);
+
+ TridentTopology topology = new TridentTopology();
+ Stream stream = topology.newStream("spout1", spout);
+
+ stream.partitionPersist(factory,
+ fields,
+ new RedisClusterStateUpdater(storeMapper).withExpire(86400000),
+ new Fields());
+
+ TridentState state = topology.newStaticState(factory);
+ stream = stream.stateQuery(state, new Fields("word"),
+ new RedisClusterStateQuerier(lookupMapper),
+ new Fields("columnName","columnValue"));
+```
+
+## License
+
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+
+## Committer Sponsors
+
+ * Robert Evans ([@revans2](https://github.com/revans2))
+ * Jungtaek Lim ([@HeartSaVioR](https://github.com/HeartSaVioR))