Amazon EMR release 6.1.0 - Amazon EMR

Amazon EMR release 6.1.0

Application versions

The following applications are supported in this release: Flink, Ganglia, HBase, HCatalog, Hadoop, Hive, Hudi, Hue, JupyterHub, Livy, MXNet, Oozie, Phoenix, Pig, Presto, PrestoSQL, Spark, Sqoop, TensorFlow, Tez, Zeppelin, and ZooKeeper.

The table below lists the application versions available in this release of Amazon EMR and the application versions in the preceding three Amazon EMR releases (when applicable).

For a comprehensive history of application versions for each release of Amazon EMR, see the following topics:

Application version information
emr-6.1.1 emr-6.1.0 emr-6.0.1 emr-6.0.0
AWS SDK for Java 1.11.828 1.11.828 1.11.711 1.11.711
Flink 1.11.0 1.11.0 - -
Ganglia 3.7.2 3.7.2 3.7.2 3.7.2
HBase 2.2.5 2.2.5 2.2.3 2.2.3
HCatalog 3.1.2 3.1.2 3.1.2 3.1.2
Hadoop 3.2.1 3.2.1 3.2.1 3.2.1
Hive 3.1.2 3.1.2 3.1.2 3.1.2
Hudi 0.5.2-incubating-amzn-2 0.5.2-incubating-amzn-2 0.5.0-incubating-amzn-1 0.5.0-incubating-amzn-1
Hue 4.7.1 4.7.1 4.4.0 4.4.0
JupyterEnterpriseGateway - - - -
JupyterHub 1.1.0 1.1.0 1.0.0 1.0.0
Livy 0.7.0 0.7.0 0.6.0 0.6.0
MXNet 1.6.0 1.6.0 1.5.1 1.5.1
Mahout - - - -
Oozie 5.2.0 5.2.0 5.1.0 5.1.0
Phoenix 5.0.0 5.0.0 5.0.0 5.0.0
Pig 0.17.0 0.17.0 - -
Presto 0.232 0.232 0.230 0.230
PrestoSQL 338 338 - -
Spark 3.0.0 3.0.0 2.4.4 2.4.4
Sqoop 1.4.7 1.4.7 - -
TensorFlow 2.1.0 2.1.0 1.14.0 1.14.0
Tez 0.9.2 0.9.2 0.9.2 0.9.2
Trino - - - -
Zeppelin 0.9.0 0.9.0 0.9.0 0.9.0
ZooKeeper 3.4.14 3.4.14 3.4.14 3.4.14

Release notes

The following release notes include information for Amazon EMR release version 6.1.0. Changes are relative to 6.0.0.

Initial release date: Sept 04, 2020

Last updated date: Oct 15, 2020

Supported applications

  • AWS SDK for Java version 1.11.828

  • Flink version 1.11.0

  • Ganglia version 3.7.2

  • Hadoop version 3.2.1-amzn-1

  • HBase version 2.2.5

  • HBase-operator-tools 1.0.0

  • HCatalog version 3.1.2-amzn-0

  • Hive version 3.1.2-amzn-1

  • Hudi version 0.5.2-incubating

  • Hue version 4.7.1

  • JupyterHub version 1.1.0

  • Livy version 0.7.0

  • MXNet version 1.6.0

  • Oozie version 5.2.0

  • Phoenix version 5.0.0

  • Presto version 0.232

  • PrestoSQL version 338

  • Spark version 3.0.0-amzn-0

  • TensorFlow version 2.1.0

  • Zeppelin version 0.9.0-preview1

  • Zookeeper version 3.4.14

  • Connectors and drivers: DynamoDB Connector 4.14.0

New features

  • ARM instance types are supported starting with Amazon EMR version 5.30.0 and Amazon EMR version 6.1.0.

  • M6g general purpose instance types are supported starting with Amazon EMR versions 6.1.0 and 5.30.0. For more information, see Supported Instance Types in the Amazon EMR Management Guide.

  • The EC2 placement group feature is supported starting with Amazon EMR version 5.23.0 as an option for multiple master node clusters. Currently, only master node types are supported by the placement group feature, and the SPREAD strategy is applied to those master nodes. The SPREAD strategy places a small group of instances across separate underlying hardware to guard against the loss of multiple master nodes in the event of a hardware failure. For more information, see EMR Integration with EC2 Placement Group in the Amazon EMR Management Guide.

  • Managed Scaling – With Amazon EMR version 6.1.0, you can enable EMR managed scaling to automatically increase or decrease the number of instances or units in your cluster based on workload. EMR continuously evaluates cluster metrics to make scaling decisions that optimize your clusters for cost and speed. Managed Scaling is also available on Amazon EMR version 5.30.0 and later, except 6.0.0. For more information, see Scaling Cluster Resources in the Amazon EMR Management Guide.

  • PrestoSQL version 338 is supported with EMR 6.1.0. For more information, see Presto.

    • PrestoSQL is supported on EMR 6.1.0 and later versions only, not on EMR 6.0.0 or EMR 5.x.

    • The application name, Presto continues to be used to install PrestoDB on clusters. To install PrestoSQL on clusters, use the application name PrestoSQL.

    • You can install either PrestoDB or PrestoSQL, but you cannot install both on a single cluster. If both PrestoDB and PrestoSQL are specified when attempting to create a cluster, a validation error occurs and the cluster creation request fails.

    • PrestoSQL is supported on both single-master and muti-master clusters. On multi-master clusters, an external Hive metastore is required to run PrestoSQL or PrestoDB. See Supported applications in an EMR Cluster with Multiple Master Nodes.

  • ECR auto authentication support on Apache Hadoop and Apache Spark with Docker: Spark users can use Docker images from Docker Hub and Amazon Elastic Container Registry (Amazon ECR) to define environment and library dependencies.

    Configure Docker and Run Spark Applications with Docker Using Amazon EMR 6.x.

  • EMR supports Apache Hive ACID transactions: Amazon EMR 6.1.0 adds support for Hive ACID transactions so it complies with the ACID properties of a database. With this feature, you can run INSERT, UPDATE, DELETE, and MERGE operations in Hive managed tables with data in Amazon Simple Storage Service (Amazon S3). This is a key feature for use cases like streaming ingestion, data restatement, bulk updates using MERGE, and slowly changing dimensions. For more information, including configuration examples and use cases, see Amazon EMR supports Apache Hive ACID transactions.

Changes, enhancements, and resolved issues

  • Amazon EMR version 6.1.1 fixed issues with Managed Scaling unable to complete or causing application failures.

  • Apache Flink is not supported on EMR 6.0.0, but it is supported on EMR 6.1.0 with Flink 1.11.0. This is the first version of Flink to officially support Hadoop 3. See Apache Flink 1.11.0 Release Announcement.

  • Ganglia has been removed from default EMR 6.1.0 package bundles.

Known issues

  • Lower "Max open files" limit on older AL2. Amazon EMR releases: emr-5.30.x, emr-5.31.0, emr-5.32.0, emr-6.0.0, emr-6.1.0, and emr-6.2.0 are based on older versions ofAmazon Linux 2 (AL2), which have a lower ulimit setting for "Max open files" when EMR clusters are created with the default AMI. The lower open file limit causes a "Too many open files" error when submitting Spark job. In the impacted EMR releases, the Amazon EMR default AMI has a default ulimit setting of 4096 for "Max open files," which is lower than the 65536 file limit in the latestAmazon Linux 2 AMI. The lower ulimit setting for "Max open files" causes Spark job failure when the Spark driver and executor try to open more than 4096 files. To fix the issue, Amazon EMR has a bootstrap action (BA) script that adjusts the ulimit setting at cluster creation. Amazon EMR releases 6.3.0 and 5.33.0 will include a permanent fix with a higher "Max open files" setting.

    The following workaround for this issue lets you to explicitly set the instance-controller ulimit to a maximum of 65536 files.

    Explicitly set a ulimit from the command line

    1. Edit /etc/systemd/system/instance-controller.service to add the following parameters to Service section.



    2. Restart InstanceController

      $ sudo systemctl daemon-reload

      $ sudo systemctl restart instance-controller

    Set a ulimit using bootstrap action (BA)

    You can also use a bootstrap action (BA) script to configure the instance-controller ulimit to 65536 files at cluster creation.

    #!/bin/bash for user in hadoop spark hive; do sudo tee /etc/security/limits.d/$user.conf << EOF $user - nofile 65536 $user - nproc 65536 EOF done for proc in instancecontroller logpusher; do sudo mkdir -p /etc/systemd/system/$proc.service.d/ sudo tee /etc/systemd/system/$proc.service.d/override.conf << EOF [Service] LimitNOFILE=65536 LimitNPROC=65536 EOF pid=$(pgrep -f aws157.$proc.Main) sudo prlimit --pid $pid --nofile=65535:65535 --nproc=65535:65535 done sudo systemctl daemon-reload
  • Important

    Amazon EMR 6.1.0 and 6.2.0 include a performance issue that can critically affect all Hudi insert, upsert, and delete operations. If you plan to use Hudi with Amazon EMR 6.1.0 or 6.2.0, you should contact AWS support to obtain a patched Hudi RPM.

  • If you set custom garbage collection configuration with spark.driver.extraJavaOptions and spark.executor.extraJavaOptions, this will result in driver/executor launch failure with EMR 6.1 due to conflicting garbage collection configuration. With EMR Release 6.1.0, you should specify custom Spark garbage collection configuration for drivers and executors with the properties spark.driver.defaultJavaOptions and spark.executor.defaultJavaOptions instead. Read more in Apache Spark Runtime Environment and Configuring Spark Garbage Collection on Amazon EMR 6.1.0.

  • Using Pig with Oozie (and within Hue, since Hue uses Oozie actions to run Pig scripts), generates an error that a native-lzo library cannot be loaded. This error message is informational and does not block Pig from running.

  • Hudi Concurrency Support: Currently Hudi doesn't support concurrent writes to a single Hudi table. In addition, Hudi rolls back any changes being done by in-progress writers before allowing a new writer to start. Concurrent writes can interfere with this mechanism and introduce race conditions, which can lead to data corruption. You should ensure that as part of your data processing workflow, there is only a single Hudi writer operating against a Hudi table at any time. Hudi does support multiple concurrent readers operating against the same Hudi table.

  • Known issue in clusters with multiple master nodes and Kerberos authentication

    If you run clusters with multiple master nodes and Kerberos authentication in EMR releases 5.20.0 and later, you may encounter problems with cluster operations such as scale down or step submission, after the cluster has been running for some time. The time period depends on the Kerberos ticket validity period that you defined. The scale-down problem impacts both automatic scale-down and explicit scale down requests that you submitted. Additional cluster operations can also be impacted.


    • SSH as hadoop user to the lead master node of the EMR cluster with multiple master nodes.

    • Run the following command to renew Kerberos ticket for hadoop user.

      kinit -kt <keytab_file> <principal>

      Typically, the keytab file is located at /etc/hadoop.keytab and the principal is in the form of hadoop/<hostname>@<REALM>.


    This workaround will be effective for the time period the Kerberos ticket is valid. This duration is 10 hours by default, but can configured by your Kerberos settings. You must re-run the above command once the Kerberos ticket expires.

  • There is an issue in Amazon EMR 6.1.0 that affects clusters running Presto. After an extended period of time (days), the cluster may throw errors such as, "su: failed to execute /bin/bash: Resource temporarily unavailable" or "shell request failed on channel 0". This issue is caused by an internal Amazon EMR process (InstanceController) that is spawning too many Light Weight Processes (LWP), which eventually causes the Hadoop user to exceed their nproc limit. This prevents the user from opening additional processes. The solution for this issue is to upgrade to EMR 6.2.0.

Component versions

The components that Amazon EMR installs with this release are listed below. Some are installed as part of big-data application packages. Others are unique to Amazon EMR and installed for system processes and features. These typically start with emr or aws. Big-data application packages in the most recent Amazon EMR release are usually the latest version found in the community. We make community releases available in Amazon EMR as quickly as possible.

Some components in Amazon EMR differ from community versions. These components have a version label in the form CommunityVersion-amzn-EmrVersion. The EmrVersion starts at 0. For example, if open source community component named myapp-component with version 2.2 has been modified three times for inclusion in different Amazon EMR release versions, its release version is listed as 2.2-amzn-2.

Component Version Description
aws-sagemaker-spark-sdk 1.3.0 Amazon SageMaker Spark SDK
emr-ddb 4.14.0 Amazon DynamoDB connector for Hadoop ecosystem applications.
emr-goodies 3.1.0 Extra convenience libraries for the Hadoop ecosystem.
emr-kinesis 3.5.0 Amazon Kinesis connector for Hadoop ecosystem applications.
emr-s3-dist-cp 2.14.0 Distributed copy application optimized for Amazon S3.
emr-s3-select 2.0.0 EMR S3Select Connector
emrfs 2.42.0 Amazon S3 connector for Hadoop ecosystem applications.
flink-client 1.11.0 Apache Flink command line client scripts and applications.
ganglia-monitor 3.7.2 Embedded Ganglia agent for Hadoop ecosystem applications along with the Ganglia monitoring agent.
ganglia-metadata-collector 3.7.2 Ganglia metadata collector for aggregating metrics from Ganglia monitoring agents.
ganglia-web 3.7.1 Web application for viewing metrics collected by the Ganglia metadata collector.
hadoop-client 3.2.1-amzn-1 Hadoop command-line clients such as 'hdfs', 'hadoop', or 'yarn'.
hadoop-hdfs-datanode 3.2.1-amzn-1 HDFS node-level service for storing blocks.
hadoop-hdfs-library 3.2.1-amzn-1 HDFS command-line client and library
hadoop-hdfs-namenode 3.2.1-amzn-1 HDFS service for tracking file names and block locations.
hadoop-hdfs-journalnode 3.2.1-amzn-1 HDFS service for managing the Hadoop filesystem journal on HA clusters.
hadoop-httpfs-server 3.2.1-amzn-1 HTTP endpoint for HDFS operations.
hadoop-kms-server 3.2.1-amzn-1 Cryptographic key management server based on Hadoop's KeyProvider API.
hadoop-mapred 3.2.1-amzn-1 MapReduce execution engine libraries for running a MapReduce application.
hadoop-yarn-nodemanager 3.2.1-amzn-1 YARN service for managing containers on an individual node.
hadoop-yarn-resourcemanager 3.2.1-amzn-1 YARN service for allocating and managing cluster resources and distributed applications.
hadoop-yarn-timeline-server 3.2.1-amzn-1 Service for retrieving current and historical information for YARN applications.
hbase-hmaster 2.2.5 Service for an HBase cluster responsible for coordination of Regions and execution of administrative commands.
hbase-region-server 2.2.5 Service for serving one or more HBase regions.
hbase-client 2.2.5 HBase command-line client.
hbase-rest-server 2.2.5 Service providing a RESTful HTTP endpoint for HBase.
hbase-thrift-server 2.2.5 Service providing a Thrift endpoint to HBase.
hcatalog-client 3.1.2-amzn-2 The 'hcat' command line client for manipulating hcatalog-server.
hcatalog-server 3.1.2-amzn-2 Service providing HCatalog, a table and storage management layer for distributed applications.
hcatalog-webhcat-server 3.1.2-amzn-2 HTTP endpoint providing a REST interface to HCatalog.
hive-client 3.1.2-amzn-2 Hive command line client.
hive-hbase 3.1.2-amzn-2 Hive-hbase client.
hive-metastore-server 3.1.2-amzn-2 Service for accessing the Hive metastore, a semantic repository storing metadata for SQL on Hadoop operations.
hive-server2 3.1.2-amzn-2 Service for accepting Hive queries as web requests.
hudi 0.5.2-incubating-amzn-2 Incremental processing framework to power data pipline at low latency and high efficiency.
hudi-presto 0.5.2-incubating-amzn-2 Bundle library for running Presto with Hudi.
hudi-prestosql 0.5.2-incubating-amzn-2 Bundle library for running PrestoSQL with Hudi.
hudi-spark 0.5.2-incubating-amzn-2 Bundle library for running Spark with Hudi.
hue-server 4.7.1 Web application for analyzing data using Hadoop ecosystem applications
jupyterhub 1.1.0 Multi-user server for Jupyter notebooks
livy-server 0.7.0-incubating REST interface for interacting with Apache Spark
nginx 1.12.1 nginx [engine x] is an HTTP and reverse proxy server
mxnet 1.6.0 A flexible, scalable, and efficient library for deep learning.
mariadb-server 5.5.64+ MariaDB database server.
nvidia-cuda 9.2.88 Nvidia drivers and Cuda toolkit
oozie-client 5.2.0 Oozie command-line client.
oozie-server 5.2.0 Service for accepting Oozie workflow requests.
opencv 4.3.0 Open Source Computer Vision Library.
phoenix-library 5.0.0-HBase-2.0 The phoenix libraries for server and client
phoenix-query-server 5.0.0-HBase-2.0 A light weight server providing JDBC access as well as Protocol Buffers and JSON format access to the Avatica API
presto-coordinator 0.232 Service for accepting queries and managing query execution among presto-workers.
presto-worker 0.232 Service for executing pieces of a query.
presto-client 0.232 Presto command-line client which is installed on an HA cluster's stand-by masters where Presto server is not started.
prestosql-coordinator 338 Service for accepting queries and managing query execution among prestosql-workers.
prestosql-worker 338 Service for executing pieces of a query.
prestosql-client 338 Presto command-line client which is installed on an HA cluster's stand-by masters where Presto server is not started.
pig-client 0.17.0 Pig command-line client.
r 3.4.3 The R Project for Statistical Computing
ranger-kms-server 2.0.0 Apache Ranger Key Management System
spark-client 3.0.0-amzn-0 Spark command-line clients.
spark-history-server 3.0.0-amzn-0 Web UI for viewing logged events for the lifetime of a completed Spark application.
spark-on-yarn 3.0.0-amzn-0 In-memory execution engine for YARN.
spark-yarn-slave 3.0.0-amzn-0 Apache Spark libraries needed by YARN slaves.
sqoop-client 1.4.7 Apache Sqoop command-line client.
tensorflow 2.1.0 TensorFlow open source software library for high performance numerical computation.
tez-on-yarn 0.9.2 The tez YARN application and libraries.
webserver 2.4.41+ Apache HTTP server.
zeppelin-server 0.9.0-preview1 Web-based notebook that enables interactive data analytics.
zookeeper-server 3.4.14 Centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.
zookeeper-client 3.4.14 ZooKeeper command line client.

Configuration classifications

Configuration classifications allow you to customize applications. These often correspond to a configuration XML file for the application, such as hive-site.xml. For more information, see Configure applications.

emr-6.1.0 classifications
Classifications Description


Change values in Hadoop's capacity-scheduler.xml file.


Change values in Hadoop YARN's container-executor.cfg file.


Change values in Hadoop YARN's file.


Change values in Hadoop's core-site.xml file.


Change EMRFS settings.


Change flink-conf.yaml settings.


Change Flink settings.


Change Flink settings.


Change Flink settings.


Change values in the Hadoop environment for all Hadoop components.


Change values in Hadoop's file.


Change hadoop ssl server configuration


Change hadoop ssl client configuration


Amazon EMR-curated settings for Apache HBase.


Change values in HBase's environment.


Change values in HBase's file.


Change values in HBase's file.


Change values in HBase's hbase-policy.xml file.


Change values in HBase's hbase-site.xml file.


Configure HDFS encryption zones.


Change values in the HDFS environment.


Change values in HDFS's hdfs-site.xml.


Change values in HCatalog's environment.


Change values in HCatalog's


Change values in HCatalog's proto-hive-site.xml.


Change values in HCatalog WebHCat's environment.


Change values in HCatalog WebHCat's


Change values in HCatalog WebHCat's webhcat-site.xml file.


Amazon EMR-curated settings for Apache Hive.


Change values in Hive's file.


Change values in Hive's file.


Change values in the Hive environment.


Change values in Hive's file.


Change values in Hive's file.


Change values in Hive's file.


Change values in Hive's hive-site.xml file


Change values in Hive Server2's hiveserver2-site.xml file


Change values in Hue's ini file


Change values in the HTTPFS environment.


Change values in Hadoop's httpfs-site.xml file.


Change values in Hadoop's kms-acls.xml file.


Change values in the Hadoop KMS environment.


Change values in Hadoop's file.


Change values in Hadoop's kms-site.xml file.


Change values in the Hudi environment.


Change values in Jupyter Notebook's file.


Change values in JupyterHubs's file.


Configure Jupyter Notebook S3 persistence.


Change values in Sparkmagic's config.json file.


Change values in Livy's livy.conf file.


Change values in the Livy environment.


Change Livy settings.


Change values in the MapReduce application's environment.


Change values in the MapReduce application's mapred-site.xml file.


Change values in Oozie's environment.


Change values in Oozie's file.


Change values in Oozie's oozie-site.xml file.


Change values in Phoenix's file.


Change values in Phoenix's hbase-site.xml file.


Change values in Phoenix's file.


Change values in Phoenix's file.


Change values in the Pig environment.


Change values in Pig's file.


Change values in Pig's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in Presto's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in PrestoSQL's file.


Change values in dbks-site.xml file of Ranger KMS.


Change values in ranger-kms-site.xml file of Ranger KMS.


Change values in the Ranger KMS environment.


Change values in file of Ranger KMS.


Change values for CA file on S3 for MySQL SSL connection with Ranger KMS.


Amazon EMR-curated settings for Apache Spark.


Change values in Spark's spark-defaults.conf file.


Change values in the Spark environment.


Change values in Spark's hive-site.xml file


Change values in Spark's file.


Change values in Spark's file.


Change values in Sqoop's environment.


Change values in Sqoop OraOop's oraoop-site.xml file.


Change values in Sqoop's sqoop-site.xml file.


Change values in Tez's tez-site.xml file.


Change values in the YARN environment.


Change values in YARN's yarn-site.xml file.


Change values in the Zeppelin environment.


Change values in ZooKeeper's zoo.cfg file.


Change values in ZooKeeper's file.