Pivotal HDB 2.1.2 Release Notes

HDB 2.1.2 is a minor release of the product and is based on Apache HAWQ 2.1.0.0 (Incubating). This release includes bug fixes, enhancements to the Beta release of Optimized Row Columnar (ORC) file format support, and enhancements to the PXF Hive plug-in.

Supported Platforms

The supported platform for running Pivotal HDB 2.1.2 comprises:

Each Pivotal HDB host machine must also meet the Apache HAWQ (Incubating) system requirements. See Apache HAWQ System Requirements for more information.

Product Support Matrix

The following table summarizes Pivotal HDB product support for current and previous versions of HDB, Hadoop, HAWQ, Ambari, and operating systems.

Pivotal HDB Version PXF Version HDP Version Requirement (Pivotal HDP and Hortonworks HDP) Ambari Version Requirement HAWQ Ambari Plug-in Requirement MADlib Version Requirement RHEL/CentOS Version Requirement SuSE Version Requirement
2.1.2.0 3.2.0.0 2.5 2.4.1, 2.4.2 2.1.2.0 1.9, 1.9.1, 1.10 6.4+ (64-bit) n/a
2.1.1.0 3.1.1.0 2.5 2.4.1 2.1.1.0 1.9, 1.9.1 6.4+ (64-bit) n/a
2.1.0.0 3.1.0.0 2.5 2.4.1 2.1.0.0 1.9, 1.9.1 6.4+ (64-bit) n/a
2.0.1.0 3.0.1 2.4.0, 2.4.2 2.2.2, 2.4 2.0.1 1.9, 1.9.1 6.4+ (64-bit) n/a
2.0.0.0 3.0.0 2.3.4, 2.4.0 2.2.2 2.0.0 1.9, 1.9.1 6.4+ (64-bit) n/a
1.3.1.1 2.5.1.1 2.2.6 2.0.x 1.3.1 1.7.1, 1.8, 1.9, 1.9.1 6.4+ SLES 11 SP3
1.3.1.0 2.5.1.1 2.2.6 2.0.x 1.3.1 1.7.1, 1.8, 1.9, 1.9.1 6.4+ SLES 11 SP3
1.3.0.3 1.3.3 2.2.4.2 1.7 1.2 1.7.1, 1.8, 1.9, 1.9.1 6.4+ SLES 11 SP3
1.3.0.2 1.3.3 2.2.4.2 1.7 1.2 1.7.1, 1.8, 1.9, 1.9.1 6.4+ SLES 11 SP3
1.3.0.1 1.3.3 2.2.4.2 1.7 1.1 1.7.1, 1.8, 1.9, 1.9.1 6.4+ n/a
1.3.0.0 1.3.3 n/a n/a n/a 1.7.1, 1.8, 1.9, 1.9.1 n/a n/a

Note: RHEL/CentOS 7 is not supported.

Note: If you are using Ambari 2.4.1 and you want to install both HDP and HAWQ at the same time, see Installing HDP and HDB with Ambari 2.4.1 before you begin.

Procedural Language Support Matrix

The following table summarizes component version support for Procedural Languages available in Pivotal HDB 2.x. The versions listed have been tested with HDB. Higher versions may be compatible. Please test higher versions thoroughly in your non-production environments before deploying to production.

Pivotal HDB Version PL/Java Java Version Requirement PL/R R Version Requirement PL/Perl Perl Version Requirement PL/Python Python Version Requirement
2.1.2.0 1.7 3.3.1 5.10.1 2.6.2
2.1.1.0 1.7 3.3.1 5.10.1 2.6.2
2.1.0.0 1.7 3.3.1 5.10.1 2.6.2
2.0.1.0 1.7 3.3.1 5.10.1 2.6.2
2.0.0.0 1.6, 1.7 3.1.0 5.10.1 2.6.2

AWS Support Requirements

Pivotal HDB is supported on Amazon Web Services (AWS) servers using either Amazon block level Instance store (Amazon uses the volume names ephemeral[0-23]) or Amazon Elastic Block Store (Amazon EBS) storage. Use long-running EC2 instances with these for long-running HAWQ instances, as Spot instances can be interrupted. If using Spot instances, minimize risk of data loss by loading from and exporting to external storage.

Pivotal HDB 2.1.2 Features and Changes

Pivotal HDB 2.1.2 is based on Apache HAWQ 2.1.0.0 (Incubating), and includes the following new features as compared to Pivotal HDB 2.1.1.0:

  • ORC file format support enhancements

    HDB 2.1.2 includes enhancements to the Optimized Row Columnar (ORC) file format support Beta released in HDB 2.1.1. Refer to the ORC Beta documentation for specific information related to these enhancements.

  • PXF Hive Plug-in enhancements

    PXF now selects the optimal Hive profile for the underlying file storage type of each table/partition/fragment when responding to a HAWQ query of Hive data. The optimal profile is selected both for external table and HCatalog integration queries. PXF implementation changes for this improvement require Ambari-managed clusters to perform an additional post-install/upgrade procedure.

Installing HDP and HDB with Ambari 2.4.1

If you are using Ambari 2.4.1 and you want to install both HDP and HAWQ at the same time, special care must be taken if you want to install the very latest version of the HDP stack instead of the default version. Follow these steps:

  1. After installing Ambari, start the Cluster Install Wizard and proceed until you reach the Select Version screen.
  2. On the Select Version screen, select HDB-2.5 from the list of available stack versions.
  3. While still on the Select Version screen, copy the Base URL values for the HDP-2.5 and HDP-UTILS-1.1.0.21 repositories that are listed for your operating system. Paste these values into a temporary file; you will need to restore these Base URL values later.
  4. Use the drop-down menu for HDP-2.5 to select the stack option, HDP-2.5 (Default Version Definition). Verify that the hdb-2.1.2.0 and hdb-add-ons-2.1.2.0 repositories now appear in the list of Repositories for your operating system.
  5. To install the very latest version of HDP, replace the Base URL values for the HDP-2.5 and HDP-UTILS-1.1.0.21 repositories with the values you pasted into the text file in Step 3.
  6. Click Next to continue, and finish installing the new HDP cluster.
  7. Install and configure Pivotal HDP as described in Installing HAWQ Using Ambari.

Note: This workaround may not be required with later versions of Ambari 2.4.

HDB 2.1.2 Upgrade

HDB 2.1.2 upgrade paths:

  • The Upgrading from HDB 2.1.x guide provides specific details on applying the HDB 2.1.2 maintenance release to your HDB 2.1.0 or HDB 2.1.1 installation.

  • The Upgrading from HDB 2.0.x guide details the steps involved to upgrade your HDB 2.0.x installation to HDB 2.1.2.

Note: If you are upgrading an HDB version prior to 2.0, refer to the HDB 2.0 documentation.

Differences Compared to Apache HAWQ (Incubating)

Pivotal HDB 2.1.2 includes all of the functionality in Apache HAWQ 2.1.0.0 (Incubating) and additional bug fixes, noted by an asterisk (*) in the Resolved Issues table below.

Resolved Issues

The following HAWQ and PXF issues were resolved in HDB 2.1.2.

Apache Jira Component Summary
HAWQ-762 PXF Hive aggregation queries through PXF would sometimes hang
HAWQ-870 Query Execution Allocate target’s tuple table slot in PortalHeapMemory during split partition
HAWQ-1177 HCatalog, PXF Use profile based on file format in HCatalog integration for HiveORC profile
HAWQ-1208 Interconnect Fixed random interconnect failure
HAWQ-1214 Resource Manager Removed resource_parameters
HAWQ-1215 PXF PXF HiveORC profile did not handle complex types correctly
HAWQ-1227 Command Line Tools HAWQ init would fail if user name contains capital character
HAWQ-1228 HCatalog, PXF Use profile based on file format in HCatalog integration (HiveRC, HiveText profiles)
HAWQ-1229 Command Line Tools Removed unused option in ‘hawq config’ help message
HAWQ-1240 Query Execution Fixed bug in plan refinement for cursor operation
HAWQ-1241 Core No need to set ext/python in *PATH in file greenplum_path.sh
HAWQ-1242 Core hawq-site.xml default content had wrong guc variable names
HAWQ-1258 Resource Manager Segment resource manager did not switch back when it could not resolve standby host name
HAWQ-1282 Core Shared Input Scan would result in endless loop
HAWQ-1285 Resource Manager Resource manager would output uninitialized string as host name
HAWQ-1286 Security Reduced unnecessary calls to namespace check when run \d
HAWQ-1308 PXF Fixed Javadoc compile warnings
HAWQ-1309 PXF PXF service must default to port 51200 and user pxf
HAWQ-1314 Catalog, HCatalog, PXF Fixed post-upgrade pxf_get_item_fields() function break
HAWQ-1315 Resource Manager Function validateResourcePoolStatus() in resourcepool.c logged the wrong information
HAWQ-1317 Security Ported “Fix some regex issues with out-of-range characters and large char ranges” from pg
HAWQ-1321 Core failNames incorrectly used memory context to build message when ANALYZE failed
HAWQ-1324 Query Execution Query cancel caused the segment to go into crash recovery
HAWQ-1326 Query Execution Cancel the query earlier if one of the segments for the query crashes
HAWQ-1334 Dispatcher QD thread now sets error code if failing so that the main process for the query could exit soon
HAWQ-1338 Core In some cases writer process crashed when running 'hawq stop cluster’
HAWQ-1345* Catalog Could not count blocks of relation: Not a directory
HAWQ-1347* Dispatcher QD would check segment health only

Note*: HDB 2.1.2 includes resolved issues HAWQ-1345 and HAWQ-1347, additional bug fixes applied to the Apache HAWQ 2.1.0.0 (Incubating) release.

Known Issues and Limitations

MADlib Compression

Pivotal HDB 2.1.2 is compatible with MADlib 1.9, 1.9.1, and 1.10. If you have an existing HDB installation with MADlib 1.9.x installed, or are installing MADlib 1.9.x, you must download and execute a script to remove MADlib’s use of Quicklz compression, which is not supported in HDB 2.1.2. Run this script if you are upgrading an HDB installation with MADlib 1.9.x to HDB 2.1.2, or if you are installing MADlib 1.9.x on HDB 2.1.2.

This procedure is not necessary if you are using or installing MADlib 1.10, or if you have previously disabled Quicklz compression.

If you are upgrading an HDB 2.0 system that contains MADlib:

  1. Complete the Pivotal HDB 2.1.2 upgrade procedure as described in Upgrading to Pivotal HDB 2.1.2.

  2. Download and unpack the MADlib 1.9.1 binary distribution from the Pivotal HDB Download Page on Pivotal Network.

  3. Execute the remove_compression.sh script in the MADlib 1.9.1 distribution, providing the path to your existing MADlib installation:

    $ remove_compression.sh --prefix <madlib-installation-path>
    

    Note: If you do not include the --prefix option, the script uses the location ${GPHOME}/madlib.

For new MADlib installations, complete these steps after you install Pivotal HDB 2.1.2:

  1. Download and unpack the MADlib 1.9.1 binary distribution from the Pivotal HDB Download Page on Pivotal Network.

  2. Install the MADlib .gppkg file:

    $ gppkg -i <path-to>/madlib-ossv1.9.1_pv1.9.6_hawq2.1-rhel5-x86_64.gppkg
    
  3. Execute the remove_compression.sh script, optionally providing the MADlib installation path:

    $ remove_compression.sh --prefix <madlib-installation-path>
    

    Note: If you do not include the --prefix option, the script uses the location ${GPHOME}/madlib.

  4. Continue installing MADlib using the madpack install command as described in the MADlib Installation Guide. For example:

    $ madpack –p hawq install
    

Operating System

  • Some Linux kernel versions between 2.6.32 to 4.3.3 (not including 2.6.32 and 4.3.3) have a bug that could introduce a getaddrinfo() function hang. To avoid this issue, upgrade the kernel to version 4.3.3+.

PXF

  • GPSQL-3345 - To take advantage of the change in number of virtual segments, PXF external tables must be dropped and recreated after updating the default_hash_table_bucket_number server configuration parameter.
  • GPSQL-3347 - The LOCATION string provided when creating a PXF external table must use only ASCII characters to identify a file path. Specifying double-byte or multi-byte characters in a file path returns the following error (formatted for clarity):

    ERROR: remote component error (500) from 'IP_Address:51200': type Exception report
      message:  File does not exist: /tmp/??????/ABC-??????-001.csv
      description:  The server encountered an internal error that prevented it from fulfilling this request.
      exception:  java.io.IOException: File does not exist: /tmp/??????/ABC-??????-001.csv (libchurl.c:897) (seg10 hdw2.hdp.local:40000 pid=389911) (dispatcher.c:1801)
    
  • ORC - Refer to ORC Known Issues and Limitations for a list of known issues related to the ORC Beta.

  • PXF in a Kerberos-secured cluster requires YARN to be installed due to a dependency on YARN libraries.

  • In order for PXF to interoperate with HBase, you must manually add the PXF HBase JAR file to the HBase classpath after installation. See Post-Install Procedure for Hive and HBase on HDP.

  • HAWQ-974 - When using certain PXF profiles to query against larger files stored in HDFS, users may occasionally experience hanging or query timeout. This is a known issue that will be improved in a future HDB release. Refer to Addressing PXF Memory Issues for a discussion of the configuration options available to address these issues in your PXF deployment.

PL/R

The HAWQ PL/R extension is provided as a separate RPM in the hdb-add-ons-2.1.2.0 repository. The files installed by this RPM are owned by root. If you installed HAWQ via Ambari, HAWQ files are owned by gpadmin. Perform the following steps on each node in your HAWQ cluster after PL/R RPM installation to align the ownership of PL/R files:

root@hawq-node$ cd /usr/local/hawq
root@hawq-node$ chown gpadmin:gpadmin share/postgresql/contrib/plr.sql docs/contrib/README.plr lib/postgresql/plr.so

Ambari

  • Ambari-managed clusters should only use Ambari for setting server configuration parameters. Parameters modified using the hawq configcommand will be overwritten on Ambari startup or reconfiguration.
  • In certain configurations, the HAWQ Master may fail to start in Ambari versions prior to 2.4.2 when webhdfs is disabled. Refer to AMBARI-18837. To work around this issue, enable webhdfs by setting dfs.webhdfs.enabled to True in hdfs-site.xml, or contact Support.
  • When installing HAWQ in a Kerberos-secured cluster, the installation process may report a warning/failure in Ambari if the HAWQ configuration for resource management type is switched to YARN mode during installation. The warning is related to HAWQ not being able to register with YARN until the HDFS & YARN services are restarted with new configurations resulting from the HAWQ installation process.
  • The HAWQ standby master will not work after you change the HAWQ master port number. To enable the standby master you must first remove and then re-initialize it. See Removing the HAWQ Standby Master and Activating the HAWQ Standby Master.
  • The Ambari Re-Synchronize HAWQ Standby Master service action fails if there is an active connection to the HAWQ master node. The HAWQ task output shows the error, Active connections. Aborting shutdown... If this occurs, close all active connections and then try the re-synchronize action again.
  • The Ambari Run Service Check action for HAWQ and PXF may not work properly on a secure cluster if PXF is not co-located with the YARN component.
  • In a secured cluster, if you move the YARN Resource Manager to another host you must manually update hadoop.proxyuser.yarn.hosts in the HDFS core-site.xml file to match the new Resource Manager hostname. If you do not perform this step, HAWQ segments fail to get resources from the Resource Manager.
  • The Ambari Stop HAWQ Server (Immediate Mode) service action or hawq stop -M immediate command may not stop all HAWQ master processes in some cases. Several postgres processes owned by the gpadmin user may remain active.
  • Ambari checks whether the hawq_rm_yarn_address and hawq_rm_yarn_scheduler_address values are valid when YARN HA is not enabled. In clusters that use YARN HA, these properties are not used and may get out-of-sync with the active Resource Manager. This can leading to false warnings from Ambari if you try to change the property value.
  • Ambari does not support Custom Configuration Groups with HAWQ.
  • Certain HAWQ server configuration parameters related to resource enforcement are not active. Modifying the parameters has no effect in HAWQ since the resource enforcement feature is not currently supported. These parameters include hawq_re_cgroup_hierarchy_name, hawq_re_cgroup_mount_point, and hawq_re_cpu_enable. These parameters appear in the Advanced hawq-site configuration section of the Ambari management interface.

Workaround Required after Moving Namenode

If you use the Ambari Move Namenode Wizard to move a Hadoop namenode, the Wizard does not automatically update the HAWQ configuration to reflect the change. This leaves HAWQ in an non-functional state, and will cause HAWQ service checks to fail with an error similar to:


2017-04-19 21:22:59,138 - SQL command executed failed: export PGPORT=5432 && source
/usr/local/hawq/greenplum_path.sh && psql -d template1 -c \\\\\"CREATE  TABLE
ambari_hawq_test (col1 int) DISTRIBUTED RANDOMLY;\\\\\"
Returncode: 1
Stdout:
Stderr: Warning: Permanently added 'ip-10-32-36-168.ore1.vpc.pivotal.io,10.32.36.168'
(RSA) to the list of known hosts.
WARNING:  could not remove relation directory 16385/1/18366: Input/output error
CONTEXT:  Dropping file-system object -- Relation Directory: '16385/1/18366'
ERROR:  could not create relation directory
hdfs://ip-10-32-36-168.ore1.vpc.pivotal.io:8020/hawq_default/16385/1/18366: Input/output error

2016-04-19 21:22:59,139 - SERVICE CHECK FAILED: HAWQ was not able to write and query from a table 2016-04-19 21:23:02,608 - ** FAILURE **: Service check failed 1 of 3 checks stdout: /var/lib/ambari-agent/data/output-281.txt

To work around this problem, perform one of the following procedures after you complete the Move Namenode Wizard.

Workaround for Non-HA NameNode Clusters:
  1. Perform an HDFS service check to ensure that HDFS is running properly after you moved the NameNode.
  2. Use the Ambari config.sh utility to update hawq_dfs_url to the new NameNode address. See the Modify configurations on the Ambari Wiki for more information. For example:

    $ cd /var/lib/ambari-server/resources/scripts/
    $ ./configs.sh set {ambari_server_host} {clustername} hawq-site
    $ hawq_dfs_url {new_namenode_address}:{port}/hawq_default
    
  3. Restart the HAWQ configuration to apply the configuration change.

  4. Use ssh to log into a HAWQ node and run the checkpoint command:

    $ psql -d template1 -c "checkpoint"
    
  5. Stop the HAWQ service.

  6. The master data directory is identified in the $GPHOME/etc/hawq-site.xml file hawq_master_directory property value. Copy the master data directory to a backup location:

    $ export MDATA_DIR=/value/from/hawqsite
    $ cp -r $MDATA_DIR /catalog/backup/location
    
  7. Execute this query to display all available HAWQ filespaces:

  8. SELECT fsname, fsedbid, fselocation FROM pg_filespace AS sp,
    pg_filespace_entry AS entry, pg_filesystem AS fs WHERE sp.fsfsys = fs.oid
    AND fs.fsysname = 'hdfs' AND sp.oid = entry.fsefsoid ORDER BY
    entry.fsedbid;
    
          fsname | fsedbid | fselocation
    -------------+---------+------------------------------------------------
    cdbfast_fs_a | 0       | hdfs://hdfs-cluster/hawq//cdbfast_fs_a
    dfs_system   | 0       | hdfs://test5:9000/hawq/hawq-1459499690
    (2 rows)
    
  9. Execute the hawq filespace command on each filespace that was returned by the previous query. For example:

    $ hawq filespace --movefilespace dfs_system --location=hdfs://new_namenode:port/hawq/hawq-1459499690
    $ hawq filespace --movefilespace cdbfast_fs_a --location=hdfs://new_namenode:port/hawq//cdbfast_fs_a
    
  10. If your cluster uses a HAWQ standby master, reinitialize the standby master in Ambari using the Remove Standby Wizard followed by the Add Standby Wizard.

  11. Start the HAWQ Service.

  12. Run a HAWQ service check to ensure that all tests pass.

Workaround for HA NameNode Clusters:
  1. Perform an HDFS service check to ensure that HDFS is running properly after you moved the NameNode.
  2. Use Ambari to expand Custom hdfs-client in the HAWQ Configs tab, then update the dfs.namenode. properties to match the current NameNode configuration.
  3. Restart the HAWQ configuration to apply the configuration change.
  4. Run a HAWQ service check to ensure that all tests pass.