Known Issues and Limitations in CDH 6.1.1

A fatal error has been detected by the Java Runtime Environment:
SIGBUS (0x7) at pc=0x00007fe91ef6cebc, pid=30321, tid=0x00007fe930c67700

Cloudera services for HDFS and Impala cannot start after applying the patch.

Commonly used Linux distributions are shown in the table below. However, the issue affects any CDH release that runs on RHEL, CentOS, Oracle Linux, SUSE Linux, or Ubuntu and that has had the Linux kernel security patch for CVE-2017-1000364 applied.

If you have already applied the patch for your OS according to the advisories for CVE-2017-1000364, apply the kernel update that contains the fix for your operating system (some of which are listed in the table). If you cannot apply the kernel update, you can workaround the issue by increasing the Java thread stack size as detailed in the steps below.

Impact: After a leap-second event, Java applications (including CDH services) using older Java and Linux kernel versions, may consume almost 100% CPU. See https://access.redhat.com/articles/15145.

Leap-second events are tied to the time synchronization methods of the Linux kernel, the Linux distribution and version, and the Java version used by applications running on affected kernels.

Although Java is increasingly agnostic to system clock progression (and less susceptible to a kernel's mishandling of a leap-second event), using JDK 7 or 8 should prevent issues at the CDH level (for CDH components that use the Java Virtual Machine).

Immediate action required:

(1) Ensure that the kernel is up to date.

• RHEL6/7, CentOS 6/7 - 2.6.32-298 or higher

• Oracle Enterprise Linux (OEL) - Kernels built in 2013 or later

• SLES12 - No action required.

(3) Ensure that your systems use either NTP or PTP synchronization.

For systems not using time synchronization, update both the OS tzdata and Java tzdata packages to the tzdata-2016g version, at a minimum. For OS tzdata package updates, contact OS support or check updated OS repositories. For Java tzdata package updates, see Oracle's Timezone Updater Tool.

Cloudera Issue: CDH-44788, TSB-189

If an encrypted file channel is set to use fast replay, the replay will fail and the channel will fail to start.

Workaround: Disable fast replay for the encrypted channel by setting use-fast-replay to false.

Apache Issue: FLUME-1885

Several Hadoop and HDFS properties have been deprecated as of Hadoop 3.0 and later. For details, see Deprecated Properties.

Navigator Audit logging for HBase access can have a big impact on HBase performance most noticeable during data ingestion.

Component affected: HBase

Products affected: CDH

Releases affected: CDH 6.x

Impact: 4x performance increase was observed in batchMutate calls after disabling Navigator Audit.

Severity: High

Upgrade: Upgrade to CDP where Navigator is no longer used.

HBASE-25206 can cause data loss either through corrupting an existing hbase snapshot or destroying data that backs a clone of a previous snapshot.

Component affected: HBase

Users affected: Users of the affected releases.

Impact: Potential risk of Data Loss.

Severity: High

Apache issue: HBASE-25206

KB article: For the latest update on this issue see the corresponding Knowledge article: TSB 2021-453: HBASE-25206 "snapshot and cloned table corruption when original table is deleted"

The HDFS short-circuit setting dfs.client.read.shortcircuit is overwritten to disabled by hbase-default.xml. HDFS short-circuit reads bypass access to data in HDFS by using a domain socket (file) instead of a network socket. This alleviates the overhead of TCP to read data from HDFS which can have a meaningful improvement on HBase performance (as high as 30-40%).

Users can restore short-circuit reads by explicitly setting dfs.client.read.shortcircuit in HBase configuration via the configuration management tool for their product (e.g. Cloudera Manager or Ambari).

Impact: HBase reads with high data-locality will not execute as fast as previously. HBase random read performance is heavily affected as random reads are expected to have low latency (e.g. Get, Multi-Get). Scan workloads would also be affected, but may be less impacted as latency of scans is greater.

Severity: High

After making these configuration changes, restart the HBase service.

Cloudera will continue to pursue product changes which may alleviate the need to make these configuration changes.

For CDP 7.1.1.0 and newer, the metric shortCircuitBytesRead can be viewed for each RegionServer under the RegionServer/Server JMX metrics endpoint. When short circuit reads are not enabled, this metric will be zero. When short circuit reads are enabled and the data locality for this RegionServer is greater than zero, the metric should be greater than zero.

Knowledge article: For the latest update on this issue see the corresponding Knowledge article: TSB 2021-463: HBase Performance Issue

HDP and CDH releases suffer from low compaction throughput limits, which cause storefiles to back up faster than compactions can re-write them. This was originally identified upstream in HBASE-21000.

Users affected: Users of above mentioned HDP and CDH versions.

Severity: Medium

Impact: For non-read-only workloads, this will eventually cause back-pressure onto new writes when the blocking store files limit is reached.

Apache issue: HBASE-21000

Knowledge article: For the latest update on this issue see the corresponding Knowledge article: Cloudera Customer Advisory: Default limits for PressureAwareCompactionThroughputController are too low

The restore snapshot command causes data loss when the target table was split or truncated after snapshot creation.

Products affected: HBase

User affected: Users relying on Restore Snapshot functionality.

Impact: Restored table could have missing data when split or truncate happened after snapshot creation.

Immediate action required: Update to a version of CDH containing the fix.

hbase> disable 'table'
hbase> drop 'table'
hbase> clone_snapshot 'snapshot_name', 'table'
hbase> enable 'table'

Knowledge article: For the latest update on this issue see the corresponding Knowledge article: TSB 2020-379: Data loss with restore snapshot

OfflineMetaRepair helps you to rebuild the HBase meta table from the underlying file system. This tool is often used to correct meta table corruption or loss. It is designed to work only with hbase-1.x (CDH 5.x). Users must not run the OfflineMetaRepair tool against CDH 6.x since it uses hbase-2.x. If a user runs OfflineMetaRepair tool in CDH 6.x, then it will break or corrupt the HBase meta table.

If you have already corrupted your meta table or you believe your meta table requires the use of something like the former OfflineMetaRepair tool, do not attempt any further changes, contact Cloudera Support.

Products affected: CDH

User affected: Clusters with HBase installed.

Impact: Cluster becomes inoperable.

Immediate action required: Update to a version of CDH containing the fix.

Workaround: Do not run OfflineMetaRepair tool.

Knowledge article: For the latest update on this issue see the corresponding Knowledge article: TSB 2020-376: CDH users must not use Apache HBase's OfflineMetaRepair tool

Cloudera Manager does not allow to deploy multiple HBase services on the same host of an HDFS cluster as by design a DataNode can only have a single HBase service per host. It is possible to have two HBase services on the same HDFS cluster but they have to be on different DataNodes, meaning that there will be one RegionServer per DataNode per HBase cluster. However, that requires additional configuration, for example you have to pin /hbase_enc and /hbase to avoid the HDFS balancer to cluster. However, that requires additional configuration, for example you have to pin /hbase_enc and /hbase to avoid the HDFS balancer to cause issues with data locality.

If Cloudera Manager is not used, you can manage multiple configurations per host for different RegionServers that are part of different HBase clusters but that can lead to multiple issues and difficult troubleshooting procedures. Thus, Cloudera does not support managing multiple HBase services on the same CDH cluster.

CDH 5.12.0 includes the fix HBASE-16604, where the internal scanner that retries in case of IOException from timeouts could potentially miss data. Java clients were properly updated to account for the new behavior, but thrift clients will now see exceptions where the previous missing data would be.

Workaround: Create a new scanner and retry the operation when encountering this issue.

During IntegrationTestReplication, if the verify phase starts before the replication phase finishes, the test will fail because the target cluster does not contain all of the data. If the HBase services in the target cluster does not have enough memory, long garbage-collection pauses might occur.

Workaround: Use the -t flag to set the timeout value before starting verification.

Cloudera Issue: None.

Cloudera has tested the performance impact of using HDFS encryption with HBase. The overall overhead of HDFS encryption on HBase performance is in the range of 3 to 4% for both read and update workloads. Scan performance has not been thoroughly tested.

ExportSnapshot or DistCP operations may fail on AWS when using certain JDK 8 versions, due to an incompatibility between the AWS Java SDK 1.9.x and the joda-time date-parsing module.

Workaround: Use joda-time 2.8.1 or higher, which is included in AWS Java SDK 1.10.1 or higher.

Cloudera Issue: None.

There are two distinct causes for the regressions, depending on the workload:

• For a cached workload, the regression may be up to 11%, as compared to RHEL6. The cause relates to differences in the CPU's C-state (power saving state) behavior. With the same workload, the CPU is around 40% busier in RHEL7, and the CPU spends more time transitioning between C-states in RHEL7. Transitions out of deeper C-states add latency. When CPUs are configured to never enter a C-state lower than 1, RHEL7 is slightly faster than RHEL6 on the cached workload. The root cause is still under investigation and may be hardware-dependent.
• For an IO-bound workload, the regression may be up to 8%, even with common C-state settings. A 6% difference in average disk service time has been observed, which in turn seems to be caused by a 10% higher average read size at the drive on RHEL7. The read sizes issued by HBase are the same in both cases, so the root cause seems to be a change in the EXT4 filesystem or the Linux block IO later. The root cause is still under investigation.

Bug: None

Severity: Medium

Workaround: Avoid using RHEL 7 if you have a latency-critical workload. For a cached workload, consider tuning the C-state (power-saving) behavior of your CPUs.

CDH 5.3 and higher supports Azure Blob Storage for some applications. However, a null pointer exception occurs when you specify a wasb:// or wasbs:// location in the --copy-to option of the ExportSnapshot command or as the output directory (the second positional argument) of the Export command.

Workaround: None.

Apache Issue: HADOOP-12717

When security and Access Control are enabled, the following problems occur:

• If a Delete Table fails for a reason other than missing permissions, the access rights are removed but the table may still exist and may be used again.

• If hbaseAdmin.modifyTable() is used to delete column families, the rights are not removed from the Access Control List (ACL) table. The postOperation is implemented only for postDeleteColumn().

• If Create Table fails, full rights for that table persist for the user who attempted to create it. If another user later succeeds in creating the table, the user who made the failed attempt still has the full rights.

Workaround: None

Apache Issue: HBASE-6992

There are no notable known issues in this release of HCatalog.

Products affected: Hue

User affected: All users of the affected versions

Severity (Low/Medium/High): Medium

Impact:Security Vulnerabilities as mentioned in the CVEs

Issuing DDL statements using Hue’s Impala editor or invoking Hue’s “Refresh Cache” function in the left-side metadata browser results in Hue issuing INVALIDATE METADATA calls to the Impala service. This call is expensive and can result in a significant system impact, up to and including full system outage, when repeated in sufficient volume. This has been corrected in HUE-8882.

Users affected: End-users using Impala editor in Hue.

Severity: High

Impact: Users running DDL statements using the Hue Impala editor or invoking Hue’s Refresh Cache function causes INVALIDATE METADATA commands to be sent to Impala. Impala’s metadata invalidation is an expensive operation and could cause impact on the performance of subsequent queries, hence leading to the potential for significant impact on the entire cluster, including the potential for whole-system outage.

Apache issue: HUE-8882

Knowledge article: For the latest update on this issue see the corresponding Knowledge article: Cloudera Customer Advisory: High DDL usage in Hue Impala Editor may issue flood of INVALIDATE Calls

There are two mechanisms to secure communication to an LDAP server. One is to use an ‘ldaps’ connection, where all traffic is encrypted inside a TLS tunnel - much like ‘https’. The other is to use ‘StartTLS’, where traffic begins unencrypted in the “ldap” protocol and then upgrades itself to a TLS connection.

If StartTLS is enabled in the Hue configuration but the ‘ldap_cert’ parameter is not configured, then Hue silently disables StartTLS.

StartTLS will not be used for synchronization or import, even if StartTLS is enabled and the ‘ldap_cert’ parameter is set.

The result is that connections that the administrator assumes to be secured, using StartTLS, are not actually secure.

CVE: CVE-2019-19146

Date/time of detection: 22nd March, 2019

Detected by: Ben Gooley, Cloudera

Severity (Low/Medium/High): 8.8 High CVSS AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Products affected: CDH

Users affected: All users who are using StartTLS enabled in the Hue configuration when using LDAP as Authentication Backend to login in Hue.

Impact: Sensitive data exposure.

Addressed in release/refresh/patch: CDH 6.3.1 and above

Knowledge article: For the latest update on this issue see the corresponding Knowledge article: TSB 2020-371: Hue Silently Disables StartTLS in LDAP Connections

If Hue receives an unsigned assertion, it continues to process it as valid. This means it is possible for an end-user to forge or remove the signature and manipulate a SAML assertion to gain access without a successful authentication.

Products affected: Hue, CDH

User affected: All users who are using SAML with Hue.

CVE: CVE-2019-14775

Date/time of detection: January 2019

Detected by: Joel Snape

Severity (Low/Medium/High): High

Impact:

This is a significant security risk as it allows anyone to fake their access validity and therefore access Hue, even if they should not have access. In more detail: if Hue receives an unsigned assertion, it continues to process it as valid. This means it is possible for an end-user to forge or remove the signature and manipulate a SAML assertion to gain access without a successful authentication.

CVE: CVE-2019-14775

Hue does not currently support the Spark application.

After upgrading to CDH 6 if you check the logs in /var/log/hue and note that they are not being updated, this means that the alternatives link was lost during the upgrade.

Workaround: To resolve this issue, open a terminal window and perform the following on every Hue server:

For RHEL/Centos:

/usr/sbin/alternatives --install /etc/hue/conf hue-conf /opt/cloudera/parcels/CDH/etc/hue/conf.empty 10

For SLES:

/usr/sbin/update-alternatives --install /etc/hue/conf hue-conf /opt/cloudera/parcels/CDH/etc/hue/conf.empty 10

These issues can prevent one or more Impala-related daemons from starting properly.

These issues can cause Impala to quit or become unresponsive.

These issues involve the performance of operations such as queries or DDL statements.

These issues relate to security features, such as Kerberos authentication, Sentry authorization, encryption, auditing, and redaction.

These issues involve memory or disk usage, including out-of-memory conditions, the spill-to-disk feature, and resource management features.

These issues can cause incorrect or unexpected results from queries. They typically only arise in very specific circumstances.

These issues affect how Impala interacts with metadata. They cover areas such as the metastore database and the Impala Catalog Server daemon.

These issues affect the ability to interchange data between Impala and other systems. They cover areas such as data types and file formats.

These issues are current limitations of Impala that require evaluation as you plan how to integrate Impala into your data management workflow.

These issues do not fall into one of the above categories or have not been categorized yet.

It is possible to manually craft a Produce request which bypasses transaction and idempotent ACL validation. Only authenticated clients with Write permission on the respective topics are able to exploit this vulnerability.

Users affected: All users who run Kafka in CDH and CDK.

Date/time of detection: September, 2018

Severity (Low/Medium/High):7.1 (High) (CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:H/A:H)

Impact: Attackers can exploit this issue to bypass certain security restrictions to perform unauthorized actions. This can aid in further attacks.

CVE: CVE-2018-17196

Immediate action required: Update to a version of CDH containing the fix.

Knowledge article: For the latest update on this issue see the corresponding Knowledge article: TSB 2020-378: Potential to bypass transaction and idempotent ACL checks in Apache Kafka

Topics that are created and deleted via Kafka are secured (for example, auto created topics). However, most topic creation and deletion is done via the kafka-topics tool, which talks directly to ZooKeeper or some other third-party tool that talks directly to ZooKeeper. Because security is the responsibility of ZooKeeper authorization and authentication, Kafka cannot prevent users from making ZooKeeper changes. Anyone with access to ZooKeeper can create and delete topics. They will not be able to describe, read, or write to the topics even if they can create them.

The offsets.topic.replication.factor broker configuration is now enforced upon auto topic creation. Internal auto topic creation will fail with a GROUP_COORDINATOR_NOT_AVAILABLE error until the cluster size meets this replication factor requirement.

The first few produce requests fail when sending to a nonexistent topic with auto.create.topics.enable set to true.

Workaround: Increase the number of retries in the Producer configuration setting retries.

When using ZooKeeper authentication and a custom Kerberos principal, Kerberos-enabled Kafka does not start.

Workaround: None. You must disable ZooKeeper authentication for Kafka or use the default Kerberos principals for ZooKeeper and Kafka.

Significant performance degradation can occur when SSL is enabled. The impact varies depending on your CPU, JVM version, and message size. Consumers are typically more affected than producers.

Workaround: Configure brokers and clients with ssl.secure.random.implementation = SHA1PRNG. It often reduces this degradation drastically, but its effect is CPU and JVM dependent.

Affected Versions: CDK 2.x and later

Fixed Versions: None

Apache Issue: KAFKA-2561

Cloudera Issue: None

This issue is encountered on cluster startup and is caused by misalignment between Kafka, Sentry, and HMS. The slow startup of HMS slows down Sentry startup which consequently makes the Kafka connection to Sentry time out. Ultimately, the Kafka broker will be unable to start.

Workaround: Manually increase the number of remote procedure call retries between Sentry and Kafka through the Sentry Client Advanced Configuration Snippet (Safety Valve) for sentry-site.xml property.

Affected Versions: CDH 6.1.0 and higher

Fixed Versions: N/A

Cloudera Issue: CDH-74713

The Kafka JMX tool cannot connect to the JMX agent of the Kafka Broker or MirrorMaker if the specified address of the JMX remote connector is bound to 127.0.0.1.

Affected Versions: CDH 6.0.0 and higher

Fixed Versions: CDH 6.2.0

Cloudera Issue: OPSAPS-48695

The idempotent and transactional capabilities of Kafka are not compatible with Sentry. The issue is due to Sentry being unable to handle authorization policies for Kafka transactions. As a result, users cannot use Kafka transaction in combination with Sentry.

Workaround: Use the Sentry super user in applications where idempotent producing is a requirement or disable Sentry.

Affected Versions: CDK 4.0 and later, CDH 6.0.0, 6.0.1, 6.1.0, 6.1.1, 6.2.0, 6.3.0

Fixed Versions: CDH 6.2.1, 6.3.1

Apache Issue: N/A

Cloudera Issue: CDH-80606

When MirrorMaker is used in conjunction with Sentry, MirrorMaker reports an authorization issue and does not start. This is due to Sentry being unable to authorize the kafka_mirror_maker principal which is automatically created.

Affected Versions: CDH 6.0.0 and later

Fixed Versions: N/A

Apache Issue: N/A

Cloudera Issue: CDH-53706

In a multi master Kudu environment, if a master is restarted or goes offline for a few minutes, it can occasionally have trouble joining the cluster on startup. For example, if this happens in case of three kudu masters, and one of the other two masters is stopped or dies during this time, then the overall Kudu cluster is down because the majority of the masters are not running.

This issue is resolved by the KUDU-2748 upstream JIRA.

Products affected: Apache Kudu

For the latest update on this issue see the corresponding Knowledge article:TSB 2020-442: Kudu Masters unable to join back after a restart

Due to KUDU-2463, upon restarting Kudu, inconsistent rows may be returned from tables that have not recently been written to, resulting in any of the following:

• multiple rows for the same key being returned
• deleted data being returned
• inconsistent results consistently being returned for the same query

If this happens, you have two options to resolve the conflicts: write to the affected Kudu partitions by:

• re-deleting the known and deleted data
• upserting the most up-to-date version of affected rows.

Products affected: Apache Kudu

For the latest update on this issue see the corresponding Knowledge article:TSB 2019-353: Inconsistent rows returned from queries in Kudu

A security-related issue can cause Impala queries to start failing on busy clusters in the following scenario:

• The cluster runs with the --rpc_authentication set as optional or required. The default is optional. Secure clusters use required.
• The cluster is using multiple masters.
• Impala queries happen frequently enough that the leader master connection to some impalad isn't idle-closed (more than 1 query per 65 seconds).
• The connection stays alive for longer than the authentication token timeout (1 week by default).
• A master leadership change occurs after the authentication token expiration.

I0904 13:53:08.748968 95857 client-internal.cc:283] Unable to determine the new leader Master: Not authorized: Client connection negotiation failed: client connection to 10.164.44.13:7051: FATAL_INVALID_AUTHENTICATION_TOKEN: Not authorized: authentication token expired
I0904 13:53:10.389009 95861 status.cc:125] Unable to open Kudu table: Timed out: GetTableSchema timed out after deadline expired
 @ 0x95b1e9 impala::Status::Status()
 @ 0xff22d4 impala::KuduScanNodeBase::Open()
 @ 0xff101e impala::KuduScanNode::Open()
 @ 0xb73ced impala::FragmentInstanceState::Open()
 @ 0xb7532b impala::FragmentInstanceState::Exec()
 @ 0xb64ae8 impala::QueryState::ExecFInstance()
 @ 0xd15193 impala::Thread::SuperviseThread()
 @ 0xd158d4 boost::detail::thread_data<>::run()
 @ 0x129188a (unknown)
 @ 0x7f717ceade25 start_thread
 @ 0x7f717cbdb34d __clone

Unable to open Kudu table: Timed out: GetTableSchema timed out after deadline expired

Affected Versions: From CDH 5.11 through CDH 6.0.1

Apache Issue: KUDU-2580

If the Kudu master is configured with the -log_force_fsync_all option, tablet servers and clients will experience frequent timeouts, and the cluster may become unusable.

Affected Versions: All CDH 6 versions

If a tablet server has a very large number of tablets, it may take several minutes to start up. It is recommended to limit the number of tablets per server to 1000 or fewer. The maximum allowed number of tablets is 2000 per server. Consider this limitation when pre-splitting your tables. If you notice slow start-up times, you can monitor the number of tablets per server in the web UI.

Affected Versions: All CDH 6 versions

Unlike regular scans, fault tolerant scans will allocate all required memory when the scan begins rather than as it progresses. This can be significant for big tablets. Moreover, this memory usage isn't counted towards the tablet server's overall memory limit, raising the likelihood of the tablet server being out-of-memory killed by the kernel.

Affected Versions: CDH 6.2 / Kudu 1.9 and lower

Apache Issue: KUDU-2466

Use the descriptions in the following table to better understand the TLS/SSL settings in the Cloudera Manager Admin Console.

Affected Versions: All CDH 6 versions

Oozie database upgrade fails when PostgreSQL version 9.6 or higher is used due to a sys table change in PostgreSQL from version 9.5 to 9.6. The failure only happens if Oozie uses a JDBC driver earlier than 9.4.1209.

You can download the driver from the PostgreSQL JDBC driver homepage.

Affected Versions: CDH 6.0.0 and higher

Fixed Version: CDH 6.2.1 and higher

Cloudera Issue: CDH-75951

If the JobHistory server is down on a YARN (MRv2) cluster, Oozie attempts to submit a job, by default, three times. If the job fails, Oozie automatically puts the workflow in a SUSPEND state.

Workaround: When the JobHistory server is running again, use the resume command to tell Oozie to continue the workflow from the point at which it left off.

Affected Versions: CDH 5 and higher

Cloudera Issue: CDH-14623

When using the Spark Crunch Indexer or another client application which utilizes the SolrJ API to send Solr Update requests with delegation token authentication, the server side processing of the request might fail with a NullPointerException.

Affected Versions: CDH 6.0.0, 6.0.1, 6.1.0, 6.1.1, 6.2.0, 6.2.1, 6.3.0, 6.3.1, 6.3.2

Fixed Version: CDH 6.3.3

Apache Issue: SOLR-13921

Cloudera Issue: CDH-82599

Failed to execute command Bootstrap Solr Collections on service Solr

Workaround: If there are no collections added to it, remove the Solr service from your cluster before you start the upgrade.

Affected Versions: CDH 6.0.0, 6.0.1, 6.1.0, 6.1.1, 6.2.0, 6.2.1, 6.3.0, 6.3.1, 6.3.2

Fixed Version: CDH 6.3.3

Cloudera Issue: CDH-82042

In certain scenarios the HBase Lily Indexer (Key-Value Store Indexer) fails to write its role log files.

Workaround: None

Affected Versions: CDH 6.0.0, 6.0.1, 6.1.0, 6.1.1, 6.2.0, 6.2.1, 6.3.0, 6.3.1, 6.3.2

Fixed Version: CDH 6.3.3

Cloudera Issue: CDH-82342

When Kerberos authentication is enabled and adding a new indexer instance to HBase Lily Indexer (Key-Value Store Indexer), the authentication might fail when Lily is communicating to the HBase Master process, throwing a similar Exception:

javax.security.sasl.SaslException: GSS initiate failed [Caused by GSSException: No valid credentials provided (Mechanism level: Failed to find any Kerberos tgt)]

Affected Versions: CDH 6.0.0, 6.0.1, 6.1.0, 6.1.1, 6.2.0, 6.2.1, 6.3.0, 6.3.1, 6.3.2

Fixed Version: CDH 6.3.3

Cloudera Issue: CDH-82566

The Solr SQL, Graph, and Stream handlers do not support document-level security, and are disabled if document-level security is enabled on the collection. If necessary, these handlers can be re-enabled by setting the following Java system properties, but document-level security is not enforced for these handlers:

• SQL: solr.sentry.enableSqlQuery=true
• Graph: solr.sentry.enableGraphQuery=true
• Stream: solr.sentry.enableStreams=true

Workaround: None

Affected Versions: All CDH 6 releases

Cloudera Issue: CDH-66345

Before CDH 5.5.0, a collection could be created without specifying a configuration. If no -c value was specified, then:

• If there was only one configuration, that configuration was chosen.
• If the collection name matched a configuration name, that configuration was chosen.

Search for CDH 5.5.0 includes multiple built-in configurations. As a result, there is no longer a case in which only one configuration can be chosen by default.

Workaround: Explicitly specify the collection configuration to use by passing -c <configName> to solrctl collection --create.

Affected Versions: CDH 5.5.0 and higher

Cloudera Issue: CDH-34050

If the --input-file-format option is specified with CrunchIndexerTool, then its argument must be text, avro, or avroParquet, rather than a fully qualified class name.

Workaround: None

Affected Versions: All

Cloudera Issue: CDH-22190

The quickstart.sh file uses the timeout function to determine if ZooKeeper and the NameNode are available. To ensure this check can be complete as intended, the quickstart.sh determines if the operating system on which the script is running supports timeout. If the script detects that the operating system does not support timeout, the script continues without checking if the NameNode and ZooKeeper are available. If your environment is configured properly or you are using an operating system that supports timeout, this issue does not apply.

Workaround: This issue only occurs in some operating systems. If timeout is not available, the quickstart continues and final validation is always done by the MapReduce jobs and Solr commands that are run by the quickstart.

Affected Versions: All

Cloudera Issue: CDH-19923

The MapReduceIndexerTool and the HBaseMapReduceIndexerTool can be used with a Managed Schema created via NRT indexing of documents or via the Solr Schema API. However, neither tool supports adding fields automatically to the schema during ingest.

Workaround: Define the schema before running the MapReduceIndexerTool or HBaseMapReduceIndexerTool. In non-schemaless mode, define in the schema using the schema.xml file. In schemaless mode, either define the schema using the Solr Schema API or index sample documents using NRT indexing before invoking the tools. In either case, Cloudera recommends that you verify that the schema is what you expect using the List Fields API command.

Affected Versions: All

Cloudera Issue: CDH-26856

The Browse and Spell Request Handlers require certain fields be present in the schema. Since those fields cannot be guaranteed to exist in a Schemaless setup, the Browse and Spell Request Handlers are not enabled by default.

Workaround: If you require the “Browse” and “Spell” Request Handlers, add them to the solrconfig.xml configuration file. Generate a non-schemaless configuration to see the usual settings and modify the required fields to fit your schema.

Affected Versions: All

Cloudera Issue: CDH-19407

It is possible to create indexes with solr.hdfs.blockcache.write.enabled set to true. Such indexes may appear corrupt to readers, and reading these indexes may irrecoverably corrupt indexes. Blockcache writing is disabled by default.

Workaround: None

Affected Versions: All

Cloudera Issue: CDH-17978

Users who are not authorized to use the Solr Admin UI are not given page explaining that access is denied, and instead receive a web page that never finishes loading.

Workaround: None

Affected Versions: All

Cloudera Issue: CDH-58276

Repeatedly running the MapReduceIndexerTool on the same set of input files can result in duplicate entries in the Solr collection. This occurs because the tool can only insert documents and cannot update or delete existing Solr documents. This issue does not apply to the HBaseMapReduceIndexerTool unless it is run with more than zero reducers.

Workaround: To avoid this issue, use HBaseMapReduceIndexerTool with zero reducers. This must be done without Kerberos.

Affected Versions: All

Cloudera Issue: CDH-15441

It is possible to delete a collection when hosts that host some of the collection are unavailable. After such a deletion, if the previously unavailable hosts are brought back online, the deleted collection may be restored.

Workaround: Ensure all hosts are online before deleting collections.

Affected Versions: All

Cloudera Issue: CDH-58694

Cloudera Search does not support the ability to save search results.

Workaround: None

Affected Versions: All

Cloudera Issue: CDH-21162

Cloudera Search does not support HDFS Federation.

Workaround: None

Affected Versions: All

Cloudera Issue: CDH-11357

Solr contrib modules are not supported (Morphlines, Spark Crunch indexer, MapReduce and Lily HBase indexers are part of the Cloudera Search product itself, therefore they are supported).

Workaround: None

Affected Versions: All

Cloudera Issue: CDH-72658

Using the Sentry Service with Cloudera Search may introduce latency because authorization requests must be sent to the Sentry Service.

Workaround: You can alleviate this latency by enabling caching for the Sentry Service. For instructions, see: Enabling Caching for the Sentry Service.

Affected Versions: All

Cloudera Issue: CDH-73407

If multiple Solr instances are configured to depend on the same Sentry service, it is not possible to create unique Solr Sentry privileges per Solr deployment. Since privileges are enforced in all Solr instances simultaneously, you cannot add distinct privileges that apply to one Solr cluster, but not to another.

Workaround: None

Affected Versions: All

Cloudera Issue: CDH-72676

If you enable TLS/SSL on a Solr instance with existing collections, the collections will break and become unavailable. Collections created after enabling TLS/SSL are not affected by this issue.

Workaround: Recreate the collection after enabling TLS. For more information, see How to update existing collections in Non-SSL to SSL in Solr.

Affected Versions: All

Cloudera Issue: CDPD-4139

A Kafka topic name can have 249 characters, but Sentry only supports topic names up to 64 characters.

Workaround: Keep Kafka topic names to 64 charcters or less.

Affected Versions: All CDH 5.x and 6.x versions

Cloudera Issue: CDH-64317

Sentry takes a long time to grant or revoke a large number of column-level privileges that are requested in a single statement. For example if you execute the following command:

GRANT SELECT(col1, col2, …) ON TABLE table1;

Sentry applies the grants to each column separately and the refresh process causes long delays.

Workaround: Split the grant statement up into smaller chunks. This prevents the refresh process from causing delays.

Cloudera Issue: CDH-74982

If you run the command SHOW ROLE GRANT GROUP for a group that has never been granted a role, beeline raises an exception. However, if you run the same command for a group that does not have any roles, but has at one time been granted a role, you do not get an exception, but instead get an empty list of roles granted to the group.

Workaround: Adding a role will prevent the exception.

Affected Versions:

Cloudera Issue: CDH-71694

Under a significant workload, Grant/Revoke operations can have issues.

Workaround: If you need to make many privilege changes, plan them at a time when you do not need to do too many at once.

Affected Versions: CDH 5.13.0 and above

Apache Issue: SENTRY-1855

Cloudera Issue: CDH-56553

Using more than a thousand roles/permissions might cause significant performance problems.

Workaround: Plan your roles so that groups have as few roles as possible and roles have as few permissions as possible.

Affected Versions: CDH 5.13.0 and above

Cloudera Issue: CDH-59010

As a prerequisite of enabling Sentry, Hive impersonation is turned off, which means all YARN jobs are submitted to the Hive job queue, and are run as the hive user. This is an issue because the YARN History Server now has to block users from accessing logs for their own jobs, since their own usernames are not associated with the jobs. As a result, end users cannot access any job logs unless they can get sudo access to the cluster as the hdfs, hive or other admin users.

In CDH 5.8 (and higher), Hive overrides the default configuration, mapred.job.queuename, and places incoming jobs into the connected user's job queue, even though the submitting user remains hive. Hive obtains the relevant queue/username information for each job by using YARN's fair-scheduler.xml file.

Affected Versions: CDH 5.2.0 and above

Cloudera Issue: CDH-22890

GRANT and REVOKE for column level privileges is not supported on Hive Metastore views.

Affected Versions: All CDH versions

Apache Issue: SENTRY-754

Users who have been explicitly granted the SELECT privilege on all columns of a table, will not have the permission to perform table-level operations. For example, operations such as SELECT COUNT (1) or SELECT COUNT (*) will not work even if you have the SELECT privilege on all columns.

There is one exception to this. The SELECT * FROM TABLE command will work even if you do not have explicit table-level access.

Affected Versions: All CDH versions

Apache Issue: SENTRY-838

Users are able to run the EXPLAIN SELECT operation, exposing metadata for all columns, even for tables/columns to which they weren't explicitly granted access.

Affected Versions: All CDH versions

Apache Issue: SENTRY-849

Workaround:Use SHOW GRANT ROLE roleNameto list all privileges granted to the role.

Affected Versions: All CDH versions

Apache Issue: N/A

Cloudera Issue: CDH-19430

Sentry supports only absolute (not relative) URI paths in permission grants. Although some early releases (for example, CDH 5.7.0) might not have raised explicit errors when relative paths were set, upgrading a system that uses relative paths causes the system to lose Sentry permissions.

Resolution: Revoke privileges that have been set using relative paths, and grant permissions using absolute paths before upgrading.

Affected Versions: All versions. Relative paths are not supported in Sentry for permission grants.

When a repartition follows a shuffle, the assignment of rows to partitions is nondeterministic. If Spark has to recompute a partition, for example, due to an executor failure, the retry can consume a different set of input rows than the original computation. As a result, some rows can be dropped, and others can be duplicated.

Products affected: CDS Powered By Apache Spark

When a repartition follows a shuffle, the assignment of records to partitions is nondeterministic. If Spark has to recompute a partition, for example, due to an executor failure, the retry can consume a different set of input records than the original computation. As a result, some records can be dropped, and others can be duplicated.

Products affected: CDS Powered By Apache Spark

The RDD.repartition() transformation, which reshuffles data in the RDD randomly to create either more or fewer partitions and then balances it across the partitions, was using a round-robin method to distribute data that caused incorrect answers to be returned for RDD jobs. This issue has been corrected, but it introduced a behavior change in RDD job failure handling. Now, Spark actively fails a job if there is a fetch failure that was caused by a node failure after repartitioning.

Workaround: Use the RDD.checkpoint() method to save the intermediate RDD data to HDFS. First, call SparkContext.setCheckpointDir(directory: String) to set the checkpoint directory where the intermediate data will be saved. Note that the directory must be an HDFS path. Then mark the RDD for checkpointing by calling RDD.checkpoint() when you use the RDD.repartition() transformation.

Apache Issue: SPARK-23243

Cloudera Issue: CDH-76413

Spark Streaming write-ahead logs (WALs) cannot run on HDFS directories when Erasure Coding is enabled. Erasure Coding does not support hflush(), hsync(), and append(), which prevents the WALs from running.

Workaround: Configure Spark Streaming with a checkpoint directory that does not have Erasure Coding enabled on it. You can set the checkpoint directory with ssc.checkpoint("directory_name"). For example:

ssc.checkpoint("_checkpoint")
      

Affected Versions: CDH 6.1.0

Fixed Versions: CDH 6.2.0

Apache Issue: SPARK-26094

Cloudera Issue: CDH-61127

In Spark Structured Streaming, the exactly-once fault tolerance for file sink is valid only for files that are in the manifest. These files are located in the _spark_metadata subdirectory of the file sink output directory. Only process files that have file names starting with digits. Other temporary files can also appear in this directory, but they should not be processed. Typically, these temporary file file names start with a period (".").

You can list the valid manifest files, excluding the temporary files, by using a command like the following, which assumes your output directory is located at /tmp/output. As the appropriate user, run the following command to list the valid manifest files:

hadoop fs -ls /tmp/output/_spark_metadata/[0-9]*
      

Workaround: None

Affected Versions: CDH 6.1.0 and higher

Cloudera Issue: CDH-75191

Spark SQL treats varchar as a string (that is, there no size limit). The observed behavior is that Spark reads and writes these columns as regular strings; if inserted values exceed the size limit, no error will occur. The data will be truncated when read from Hive, but not when read from Spark.

Workaround: None

Affected Versions: CDH 5.5.0 and higher

Apache Issue: SPARK-5918

Cloudera Issue: CDH-33642

Spark allows you to declare DataFrames with any key type. Avro supports only string keys and trying to write any other key type to an Avro table will fail.

Workaround: None

Affected Versions: CDH 5.5.0 and higher

Cloudera Issue: CDH-33648

Workaround: None

Affected Versions: CDH 5.5.0 and higher

Cloudera Issue: CDH-33649

Even if user is configured via Sentry to not have read permission to a Hive table, a Spark SQL job running as that user can still read the table's metadata directly from the Hive metastore. Cloudera Issue: CDH-76468

If you are using Spark Streaming, Cloudera recommends that you disable dynamic allocation by setting spark.dynamicAllocation.enabled to false when running streaming applications.

When SparkSQL accesses an HBase table through the HiveContext, region pruning is not performed. This limitation can result in slower performance for some SparkSQL queries against tables that use the HBase SerDes than when the same table is accessed through Impala or Hive.

Workaround: None

Affected Versions: All

Cloudera Issue: CDH-56330

The spark-submit script's --principal and --keytab arguments do not work with Spark-on-YARN's client mode.

Workaround: Use cluster mode instead.

Affected Versions: All

Using the --proxy-user argument in client mode does not work and is not supported.

Workaround: Use cluster mode instead.

Affected Versions: All

When a Spark application is killed, the history link in the ResourceManager web UI does not work.

Workaround: To view the history for a killed Spark application, see the Spark HistoryServer web UI instead.

Affected Versions: All CDH versions

Apache Issue: None

Cloudera Issue: CDH-49165

Currently, Cloudera does not support reading and writing Hive tables containing data files in the Apache ORC (Optimized Row Columnar) format from Spark applications. Cloudera recommends using Apache Parquet format for columnar data. That file format can be used with Spark, Hive, and Impala.

Currently, Sqoop is using an Avro schema when writing data as a parquet file. The Avro schema requires that column names do not start with numbers, therefore Sqoop is renaming the columns in this case, prepending them with an underscore character. This can lead to issues when one wants to reuse the data in other tools, such as Impala.

Workaround: Rename the columns to comply with Avro limitations (start with letters or underscore, as specified in the Avro documentation).

Cloudera Issue: None

CentOS 6 systems currently ship with version 5.1.17 of the MySQL JDBC driver. This version does not work correctly with Sqoop.

Workaround: Install version 5.1.31 of the JDBC driver as detailed in Installing the JDBC Drivers for Sqoop 1.

Affected Versions: MySQL JDBC 5.1.17, 5.1.4, 5.3.0

Cloudera Issue: CDH-23180

The integratedSecurity option is not available in the Sqoop CLI.

Workaround: None

Cloudera Issue: None

sqoop import --connect jdbc:db2://djaxludb1001:61035/DDBAT003 --username=dh810202 --P --target-dir /data/hive_scratch/ASDISBURSEMENT --delete-target-dir -m1 --query "select disbursementnumber,disbursementdate,xmldata FROM DB2dba.ASDISBURSEMENT where DISBURSEMENTNUMBER = 2011113210000115311 AND \$CONDITIONS" -hive-import --hive-database adminserver -hive-table asdisbursement_dave --map-column-java XMLDATA=String --as-parquetfile

16/12/05 12:23:46 INFO mapreduce.Job: map 100% reduce 0%
16/12/05 12:23:46 INFO mapreduce.Job: Job job_1480530522947_0096 failed with state FAILED due to: Job commit failed: org.kitesdk.data.DatasetIOException: Could not move contents of hdfs://AJAX01-ns/tmp/adminserver/.temp/job_1480530522947_0096/mr/job_1480530522947_0096 to hdfs://AJAX01-ns/data/RetiredApps/INS/AdminServer/asdisbursement_dave
<SNIP>
Caused by: org.apache.hadoop.ipc.RemoteException(java.io.IOException): /tmp/adminserver/.temp/job_1480530522947_0096/mr/job_1480530522947_0096/5ddcac42-5d69-4e46-88c2-17bbedac4858.parquet can't be moved into an encryption zone.

Workaround: If you use the Parquet Hadoop API based implementation for importing into Parquet, specify a --target-dir which is the same encryption zone as the Hive warehouse directory.

If you use the Kite Dataset API based implementation, use an alternate data file type, for example text or avro.

Apache Issue: SQOOP-2943

Cloudera Issue: CDH-40826

Workaround: None, other than restructuring the data.

Apache Issue: PARQUET-99