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What is Uniffle

Uniffle is a Remote Shuffle Service, and provides the capability for Apache Spark applications to store shuffle data on remote servers.

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Rss Architecture Uniffle contains coordinator cluster, shuffle server cluster and remote storage(eg, HDFS) if necessary.

Coordinator will collect status of shuffle server and do the assignment for the job.

Shuffle server will receive the shuffle data, merge them and write to storage.

Depend on different situation, Uniffle supports Memory & Local, Memory & Remote Storage(eg, HDFS), Memory & Local & Remote Storage(recommendation for production environment).

Shuffle Process with Uniffle

  • Spark driver ask coordinator to get shuffle server for shuffle process

  • Spark task write shuffle data to shuffle server with following step: Rss Shuffle_Write

    1. Send KV data to buffer
    2. Flush buffer to queue when buffer is full or buffer manager is full
    3. Thread pool get data from queue
    4. Request memory from shuffle server first and send the shuffle data
    5. Shuffle server cache data in memory first and flush to queue when buffer manager is full
    6. Thread pool get data from queue
    7. Write data to storage with index file and data file
    8. After write data, task report all blockId to shuffle server, this step is used for data validation later
    9. Store taskAttemptId in MapStatus to support Spark speculation
  • Depend on different storage type, spark task read shuffle data from shuffle server or remote storage or both of them.

Shuffle file format

The shuffle data is stored with index file and data file. Data file has all blocks for specific partition and index file has metadata for every block.

Rss Shuffle_Write

Supported Spark Version

Current support Spark 2.3.x, Spark 2.4.x, Spark3.0.x, Spark 3.1.x, Spark 3.2.x, Spark 3.3.x

Note: To support dynamic allocation, the patch(which is included in client-spark/patch folder) should be applied to Spark

Supported MapReduce Version

Current support Hadoop 2.8.5's MapReduce framework.

Building Uniffle

note: currently Uniffle requires JDK 1.8 to build, adding later JDK support is on our roadmap.

Uniffle is built using Apache Maven. To build it, run:

mvn -DskipTests clean package

Build against profile Spark2(2.4.6)

mvn -DskipTests clean package -Pspark2

Build against profile Spark3(3.1.2)

mvn -DskipTests clean package -Pspark3

Build against Spark 3.2.x

mvn -DskipTests clean package -Pspark3.2

To package the Uniffle, run:


Package against Spark 3.2.x, run:

./ --spark3-profile 'spark3.2'

rss-xxx.tgz will be generated for deployment


Deploy Coordinator

  1. unzip package to RSS_HOME
  2. update RSS_HOME/bin/, eg,
    HADOOP_HOME=<hadoop home>
  3. update RSS_HOME/conf/coordinator.conf, eg,
      rss.rpc.server.port 19999
    rss.jetty.http.port 19998
    rss.coordinator.server.heartbeat.timeout 30000 60000
    rss.coordinator.shuffle.nodes.max 5
    # enable dynamicClientConf, and coordinator will be responsible for most of client conf
    rss.coordinator.dynamicClientConf.enabled true
    # config the path of client conf
    rss.coordinator.dynamicClientConf.path <RSS_HOME>/conf/dynamic_client.conf
    # config the path of excluded shuffle server
    rss.coordinator.exclude.nodes.file.path <RSS_HOME>/conf/exclude_nodes
  4. update <RSS_HOME>/conf/dynamic_client.conf, rss client will get default conf from coordinator eg,
     # MEMORY_LOCALFILE_HDFS is recommandation for production environment MEMORY_LOCALFILE_HDFS
    # multiple remote storages are supported, and client will get assignment from coordinator hdfs://cluster1/path,hdfs://cluster2/path
    rss.writer.require.memory.retryMax 1200
    rss.client.retry.max 100
    rss.writer.send.check.timeout 600000 14m
  5. start Coordinator
     bash RSS_HOME/bin/

Deploy Shuffle Server

  1. unzip package to RSS_HOME
  2. update RSS_HOME/bin/, eg,
    HADOOP_HOME=<hadoop home>
  3. update RSS_HOME/conf/server.conf, eg,
      rss.rpc.server.port 19999
    rss.jetty.http.port 19998
    rss.rpc.executor.size 2000
    # it should be configed the same as in coordinator MEMORY_LOCALFILE_HDFS
    rss.coordinator.quorum <coordinatorIp1>:19999,<coordinatorIp2>:19999
    # local storage path for shuffle server /data1/rssdata,/data2/rssdata....
    # it's better to config thread num according to local disk num
    rss.server.flush.thread.alive 5
    rss.server.flush.threadPool.size 10
    rss.server.buffer.capacity 40g 20g
    rss.server.heartbeat.timeout 60000
    rss.server.heartbeat.interval 10000
    rss.rpc.message.max.size 1073741824
    rss.server.preAllocation.expired 120000
    rss.server.commit.timeout 600000 120000
    # note: the default value of is 64m
    # there will be no data written to DFS if set it as 100g even
    # please set proper value if DFS is used, eg, 64m, 128m. 100g
  4. start Shuffle Server
     bash RSS_HOME/bin/

Deploy Spark Client

  1. Add client jar to Spark classpath, eg, SPARK_HOME/jars/

    The jar for Spark2 is located in <RSS_HOME>/jars/client/spark2/rss-client-XXXXX-shaded.jar

    The jar for Spark3 is located in <RSS_HOME>/jars/client/spark3/rss-client-XXXXX-shaded.jar

  2. Update Spark conf to enable Uniffle, eg,

    spark.shuffle.manager org.apache.spark.shuffle.RssShuffleManager
    spark.rss.coordinator.quorum <coordinatorIp1>:19999,<coordinatorIp2>:19999
    # Note: For Spark2, spark.sql.adaptive.enabled should be false because Spark2 doesn't support AQE.

Support Spark dynamic allocation

To support spark dynamic allocation with Uniffle, spark code should be updated. There are 3 patches for spark (2.4.6/3.1.2/3.2.1) in spark-patches folder for reference.

After apply the patch and rebuild spark, add following configuration in spark conf to enable dynamic allocation:

spark.shuffle.service.enabled false
spark.dynamicAllocation.enabled true

Deploy MapReduce Client

  1. Add client jar to the classpath of each NodeManager, e.g., /share/hadoop/mapreduce/

The jar for MapReduce is located in /jars/client/mr/rss-client-mr-XXXXX-shaded.jar

  1. Update MapReduce conf to enable Uniffle, eg,


    Note that the RssMRAppMaster will automatically disable slow start (i.e., mapreduce.job.reduce.slowstart.completedmaps=1) and job recovery (i.e.,


The important configuration is listed as following.


Property NameDefaultDescription
rss.coordinator.server.heartbeat.timeout30000Timeout if can't get heartbeat from shuffle server
rss.coordinator.assignment.strategyPARTITION_BALANCEStrategy for assigning shuffle server, PARTITION_BALANCE should be used for workload balance expired time (ms), the heartbeat interval should be less than it
rss.coordinator.shuffle.nodes.max9The max number of shuffle server when do the assignment
rss.coordinator.dynamicClientConf.path-The path of configuration file which have default conf for rss client
rss.coordinator.exclude.nodes.file.path-The path of configuration file which have exclude nodes
rss.coordinator.exclude.nodes.check.interval.ms60000Update interval (ms) for exclude nodes
rss.rpc.server.port-RPC port for coordinator
rss.jetty.http.port-Http port for coordinator

Shuffle Server

Property NameDefaultDescription
rss.coordinator.quorum-Coordinator quorum
rss.rpc.server.port-RPC port for Shuffle server
rss.jetty.http.port-Http port for Shuffle server
rss.server.buffer.capacity-Max memory of buffer manager for shuffle server
rss.server.memory.shuffle.highWaterMark.percentage75.0Threshold of spill data to storage, percentage of rss.server.buffer.capacity
rss.server.memory.shuffle.lowWaterMark.percentage25.0Threshold of keep data in memory, percentage of rss.server.buffer.capacity size of buffer for reading data
rss.server.heartbeat.interval10000Heartbeat interval to Coordinator (ms)
rss.server.flush.threadPool.size10Thread pool for flush data to file
rss.server.commit.timeout600000Timeout when commit shuffle data (ms) MEMORY_LOCALFILE, MEMORY_HDFS, MEMORY_LOCALFILE_HDFS threshold of data size for LOACALFILE and HDFS if MEMORY_LOCALFILE_HDFS is used
rss.server.tags-The comma-separated list of tags to indicate the shuffle server's attributes. It will be used as the assignment basis for the coordinator

Shuffle Client

For more details of advanced configuration, please see Uniffle Shuffle Client Guide.


Uniffle is under the Apache License Version 2.0. See the LICENSE file for details.


For more information about contributing issues or pull requests, see Uniffle Contributing Guide.