Known Issues in Impala

Learn about the known issues in Impala, the impact or changes to the functionality, and the workaround.

CDPD-60862: Rolling restart fails during ZDU when DDL operations are in progress

During a Zero Downtime Upgrade (ZDU), the rolling restart of services that support Data Definition Language (DDL) statements might fail if DDL operations are in progress during the upgrade. As a result, ensure that you do not run DDL statements during ZDU.

The following services support DDL statements:

• Impala
• Hive – using HiveQL
• Spark – using SparkSQL
• HBase
• Phoenix
• Kafka

Data Manipulation Lanaguage (DML) statements are not impacted and can be used during ZDU. Following the successful upgrade, you can resume running DDL statements.

None. Cloudera recommends modifying applications to not use DDL statements for the duration of the upgrade. If the upgrade is already in progress, and you have experienced a service failure, you can remove the DDLs in-flight and resume the upgrade from the point of failure.

CDPD-60489: Jackson-dataformat-yaml 2.12.7 and Snakeyaml 2.0 are not compatible.

You must not use Jackson-dataformat-yaml through the platform for YAML parsing.

CDPD-59625: Impala shell in RHEL 9 with Python 2 as default does not work

If you try to run impala-shell on RHEL 9 by setting the default python executable available in PATH to Python 2, it will fail since RHEL 9 is compatible only with Python 3.

If you run into such issues, set this parameter pointing to Python 3, IMPALA_PYTHON_EXECUTABLE=python3.

CDPD-42958: After upgrading the CDH 7.1.9 from CDH 6.x, under certain conditions you cannot insert data into a table

Under the following conditions, after upgrading from CDH 6.x to CDH 7.1.9 you cannot insert data into a table from Impala:

• On CDH 6.x, you created a database with Impala in a user specified HDFS location.
• Using Hive, you then created a table in the database.

Under these conditions, the database and table are stored in the user-specified HDFS directory. After upgrading, the HDFS directory of the table is read-only for Impala. Consequently, from Impala you cannot insert new data into the table because Impala does not have write permission on the HDFS directory.

Workaround: To resolve this issue, use either one of the following workarounds:

• Using the Ranger Web UI, in the policy repository cm_hdfs, grant the user 'impala' write permission on the directory where the table resides.
• Enter the following command to grant write permission to user 'impala' on the HDFS directory where the table resides.

  hdfs dfs -setfacl -m default:user:impala:rwx <HDFS directory>

Impala cannot update table if the 'external.table.purge' property is not set to true

Impala cannot update a table using DDL statements if the 'external.table.purge' property is FALSE. ALTER TABLE statements return success with no changes to the table.

ALTER TABLE statements should be issued twice if "external.table.purge" was FALSE initially.

Impala's known limitation when querying compacted tables

When the compaction process deletes the files for a table from the underlying HDFS location, the Impala service does not detect the changes as the compactions does not allocate new write ids. When the same table is queried from Impala it throws a 'File does not exist' exception that looks something like this:

Query Status: Disk I/O error on <node>:22000: Failed to open HDFS file hdfs://nameservice1/warehouse/tablespace/managed/hive/<database>/<table>/xxxxx
Error(2): No such file or directory Root cause: RemoteException: File does not exist: /warehouse/tablespace/managed/hive/<database>/<table>/xxxx

Use the REFRESH/INVALIDATE statements on the affected table to overcome the 'File does not exist' exception.

CDPD-28431: Intermittent errors could be potentially encountered when Impala UI is accessed from multiple Knox nodes.

You must use a single Knox node to access Impala UI.

Impala api calls via knox require configuration if the knox customized kerberos principal name is a default service user name

To access impala api calls via knox, if the knox customized kerberos principal name is a default service user name, then configure "authorized_proxy_user_config" by clicking Clusters->impala->configuration. Include the knox customized kerberos principal name in the comma separated list of values <knox_custom_kerberos_principal_name>=*" where <knox_custom_kerberos_principal_name> is the value of the Kerberos Principal in the Knox service. Select Clusters>Knox>Configuration and search for Kerberos Principal to display this value.

CDPD-21828: Multiple permission assignment through grant is not working

None

Problem configuring masking on tables using Ranger

The following Knowledge Base article describes the behavior when we configure masking on tables using Ranger. This configuration works for Hive, but breaks queries in some scenarios for Impala.

For a workaround, see the following Knowledge Base article: ERROR: "AnalysisException: No matching function with signature: mask(FLOAT)" when Impala jobs fail with the following error with signature: mask(FLOAT)

IMPALA-11871: INSERT statement does not respect Ranger policies for HDFS

In a cluster with Ranger auth (and with legacy catalog mode), even if you provide RWX to cm_hdfs -> all-path for the user impala, inserting into a table whose HDFS POSIX permissions happen to exclude impala access will result in "AnalysisException: Unable to INSERT into target table (default.t1) because Impala does not have WRITE access to HDFS location: hdfs://XXXXXXXXXXXX"

IMPALA-532: Impala should tolerate bad locale settings

If the LC_* environment variables specify an unsupported locale, Impala does not start.

Add LC_ALL="C" to the environment settings for both the Impala daemon and the Statestore daemon.

Avro Scanner fails to parse some schemas

The default value in Avro schema must match type of first union type, e.g. if the default value is null, then the first type in the UNION must be "null".

Swap the order of the fields in the schema specification. For example, use ["null", "string"] instead of ["string", "null"]. Note that the files written with the problematic schema must be rewritten with the new schema because Avro files have embedded schemas.

IMPALA-691: Process mem limit does not account for the JVM's memory usage

Some memory allocated by the JVM used internally by Impala is not counted against the memory limit for the impalad daemon.

To monitor overall memory usage, use the top command, or add the memory figures in the Impala web UI /memz tab to JVM memory usage shown on the /metrics tab.

IMPALA-1024: Impala BE cannot parse Avro schema that contains a trailing semi-colon

If an Avro table has a schema definition with a trailing semicolon, Impala encounters an error when the table is queried.

Remove trailing semicolon from the Avro schema.

IMPALA-1652: Incorrect results with basic predicate on CHAR typed column

When comparing a CHAR column value to a string literal, the literal value is not blank-padded and so the comparison might fail when it should match.

Use the RPAD() function to blank-pad literals compared with CHAR columns to the expected length.

IMPALA-1821: Casting scenarios with invalid/inconsistent results

Using a CAST() function to convert large literal values to smaller types, or to convert special values such as NaN or Inf, produces values not consistent with other database systems. This could lead to unexpected results from queries.

None

IMPALA-2005: A failed CTAS does not drop the table if the insert fails

If a CREATE TABLE AS SELECT operation successfully creates the target table but an error occurs while querying the source table or copying the data, the new table is left behind rather than being dropped.

Drop the new table manually after a failed CREATE TABLE AS SELECT

IMPALA-2422: % escaping does not work correctly when occurs at the end in a LIKE clause

If the final character in the RHS argument of a LIKE operator is an escaped \% character, it does not match a % final character of the LHS argument.

None

IMPALA-2603: Crash: impala::Coordinator::ValidateCollectionSlots

A query could encounter a serious error if includes multiple nested levels of INNER JOIN clauses involving subqueries.

None

IMPALA-3094: Incorrect result due to constant evaluation in query with outer join

An OUTER JOIN query could omit some expected result rows due to a constant such as FALSE in another join clause. For example:

explain SELECT 1 FROM alltypestiny a1
  INNER JOIN alltypesagg a2 ON a1.smallint_col = a2.year AND false
  RIGHT JOIN alltypes a3 ON a1.year = a1.bigint_col;
+-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-+
| Explain String                                          |
+-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-+
| Estimated Per-Host Requirements: Memory=1.00KB VCores=1 |
|                                                         |
| 00:EMPTYSET                                             |
+-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-+

          

IMPALA-3509: Breakpad minidumps can be very large when the thread count is high

The size of the breakpad minidump files grows linearly with the number of threads. By default, each thread adds 8 KB to the minidump size. Minidump files could consume significant disk space when the daemons have a high number of threads.

Add -\-minidump_size_limit_hint_kb=size to set a soft upper limit on the size of each minidump file. If the minidump file would exceed that limit, Impala reduces the amount of information for each thread from 8 KB to 2 KB. (Full thread information is captured for the first 20 threads, then 2 KB per thread after that.) The minidump file can still grow larger than the "hinted" size. For example, if you have 10,000 threads, the minidump file can be more than 20 MB.

IMPALA-4978: Impala requires FQDN from hostname command on Kerberized clusters

The method Impala uses to retrieve the host name while constructing the Kerberos principal is the gethostname() system call. This function might not always return the fully qualified domain name, depending on the network configuration. If the daemons cannot determine the FQDN, Impala does not start on a Kerberized cluster.

Test if a host is affected by checking whether the output of the hostname command includes the FQDN. On hosts where hostname, only returns the short name, pass the command-line flag ‑‑hostname=fully_qualified_domain_name in the startup options of all Impala-related daemons.

IMPALA-6671: Metadata operations block read-only operations on unrelated tables

Metadata operations that change the state of a table, like COMPUTE STATS or ALTER RECOVER PARTITIONS, may delay metadata propagation of unrelated unloaded tables triggered by statements like DESCRIBE or SELECT queries.

None

IMPALA-7072: Impala does not support Heimdal Kerberos

None

OPSAPS-46641: A single parameter exists in Cloudera Manager for specifying the Impala Daemon Load Balancer. Because BDR and Hue need to use different ports when connecting to the load balancer, it is not possible to configure the load balancer value so that BDR and Hue will work correctly in the same cluster.

The workaround is to use the load balancer configuration either without a port specification, or with the Beeswax port: this will configure BDR. To configure Hue use the "Hue Server Advanced Configuration Snippet (Safety Valve) for impalad_flags" to specify the the load balancer address with the HiveServer2 port.

CDPD-28139: Set spark.hadoop.hive.stats.autogather to false by default

As an Impala user, if you submit a query against a table containing data ingested using Spark and you are concerned about the quality of the query plan, you must run COMPUTE STATS against such a table in any case after an ETL operation because numRows created by Spark could be incorrect. Also, use other stats computed by COMPUTE STATS, e.g., Number of Distinct Values (NDV) and NULL count for good selectivity estimates.

For example, when a user ingests data from a file into a partition of an existing table using Spark, if spark.hadoop.hive.stats.autogather is not set to false explicitly, numRows associated with this partition would be 0 even though there is at least one row in the file. To avoid this, the workaround is to set "spark.hadoop.hive.stats.autogather=false" in the "Spark Client Advanced Configuration Snippet (Safety Valve) for spark-conf/spark-defaults.conf" in Spark's CM Configuration section.