Known Issues in Apache Impala

This topic describes known issues and workarounds for using Impala in this release of Cloudera Runtime.

Queries stuck on failed HDFS calls and not timing out

In Impala 3.2 and higher, if the following error appears multiple times in a short duration while running a query, it would mean that the connection between the impalad and the HDFS NameNode is in a bad state.

"hdfsOpenFile() for <filename> at backend <hostname:port> failed to finish before the <hdfs_operation_timeout_sec> second timeout "

In Impala 3.1 and lower, the same issue would cause Impala to wait for a long time or not respond without showing the above error message.

Workaround: Restart the impalad.

Apache JIRA: HADOOP-15720

Impala should tolerate bad locale settings: If the LC_* environment variables specify an unsupported locale, Impala does not start.; Workaround: Add LC_ALL="C" to the environment settings for both the Impala daemon and the Statestore daemon.; Apache JIRA: IMPALA-532

Configuration to prevent crashes caused by thread resource limits

Impala could encounter a serious error due to resource usage under very high concurrency. The error message is similar to:


F0629 08:20:02.956413 29088 llvm-codegen.cc:111] LLVM hit fatal error: Unable to allocate section memory!
terminate called after throwing an instance of 'boost::exception_detail::clone_impl<boost::exception_detail::error_info_injector<boost::thread_resource_error> >'

Workaround: To prevent such errors, configure each host running an impalad daemon with the following settings:


            echo 2000000 > /proc/sys/kernel/threads-max
            echo 2000000 > /proc/sys/kernel/pid_max
            echo 8000000 > /proc/sys/vm/max_map_count

Add the following lines in /etc/security/limits.conf:


            impala soft nproc 262144
            impala hard nproc 262144

Apache JIRA: IMPALA-5605

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".; Workaround: 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.; Apache JIRA: IMPALA-635

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.; Workaround: 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.; Apache JIRA: IMPALA-691

Ranger audit logs for applying column masking policies missing: Impala is not producing these logs.; Workaround: None.; Apache JIRA: IMPALA-9350

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.; Workaround: Remove trailing semicolon from the Avro schema.; Apache JIRA: IMPALA-1024

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.; Workaround: Use the RPAD() function to blank-pad literals compared with CHAR columns to the expected length.; Apache JIRA: IMPALA-1652

ImpalaODBC: Can not get the value in the SQLGetData(m-x th column) after the SQLBindCol(m th column): If the ODBC SQLGetData is called on a series of columns, the function calls must follow the same order as the columns. For example, if data is fetched from column 2 then column 1, the SQLGetData call for column 1 returns NULL.; Workaround: Fetch columns in the same order they are defined in the table.; Apache JIRA: IMPALA-1792

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.; Apache JIRA: IMPALA-1821

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.; Workaround: Drop the new table manually after a failed CREATE TABLE AS SELECT; Apache JIRA: IMPALA-2005

% 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.; Apache JIRA: IMPALA-2422

Crash: impala::Coordinator::ValidateCollectionSlots: A query could encounter a serious error if includes multiple nested levels of INNER JOIN clauses involving subqueries.; Apache JIRA: IMPALA-2603

Incorrect result due to constant evaluation in query with outer join

Workaround: 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                                             |
+-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-+

Apache JIRA: IMPALA-3094

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.; Workaround: 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.; Apache JIRA: IMPALA-3509

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.; Workaround: 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.; Apache JIRA: IMPALA-4978

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.; Workaround:; Apache JIRA: IMPALA-6671

Impala does not support Heimdal Kerberos: Apache JIRA: IMPALA-7072
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.

Technical Service Bulletins

TSB-2021-485: Impala returns fewer rows from parquet tables on S3

IMPALA-10310 was an issue in Impala's Parquet page filtering code where the scanner did not reset state appropriately when transitioning from the first row group to subsequent row groups in a single split. This caused data from the subsequent row groups to be skipped incorrectly, leading to incorrect query results. This issue cannot occur when the Parquet page filtering is disabled by setting PARQUET_READ_PAGE_INDEX=false.

The issue is more likely to be encountered on S3/ADLS/ABFS/etc, because Spark is sometimes configured to write 128MB row groups and the PARQUET_OBJECT_STORE_SPLIT_SIZE is 256MB. This makes it more likely for Impala to process two row groups in a single split.

Parquet page filtering only works based on the min/max statistics, therefore the comparison operators it supports are “=”, “<”, “>”, “<=”, and “>=”. These operators are impacted by this bug. Expressions such as “!=”, 'LIKE' or the expressions including UDF do not use parquet page filtering.

The PARQUET_OBJECT_STORE_SPLIT_SIZE parameter is introduced in Impala 3.3 by IMPALA-5843. This means that older versions of Impala do not have this issue.

Upstream JIRA

Knowledge article

For the latest update on this issue see the corresponding Knowledge article: TSB 2021-485: Impala returns fewer rows from parquet tables on S3

TSB 2021-502: Impala logs the session / operation secret on most RPCs at INFO level

Impala logs contain the session / operation secret. With this information a person who has access to the Impala logs might be able to hijack other users' sessions. This means the attacker is able to execute statements for which they do not have the necessary privileges otherwise. Impala deployments where Apache Sentry or Apache Ranger authorization is enabled may be vulnerable to privilege escalation. Impala deployments where audit logging is enabled may be vulnerable to incorrect audit logging.

Restricting access to the Impala logs that expose secrets will reduce the risk of an attack. Additionally, restricting access to trusted users for the Impala deployment will also reduce the risk of an attack. Log redaction techniques can be used to redact secrets from the logs. For more information, see the Cloudera Manager documentation.

For log redaction, users can create a rule with a search pattern: secret \(string\) [=:].*And the replacement could be for example: secret=LOG-REDACTED

Upstream JIRA

IMPALA-10600

Knowledge article

For the latest update on this issue see the corresponding Knowledge article: TSB 2021-502: Impala logs the session / operation secret on most RPCs at INFO level

TSB 2021-479: Impala can return incomplete results through JDBC and ODBC clients in all CDP offerings: In CDP, we introduced a timeout on queries to Impala defaulting to 10 seconds. The timeout setting is called FETCH_ROWS_TIMEOUT_MS. Due to this setting, JDBC, ODBC, and Beeswax clients running Impala queries believe the data returned at 10 seconds is a complete dataset and present it as the final output. However, in cases where there are still results to return after this timeout has passed, when the driver closes the connection, based on the timeout, it results in a scenario where the query results are incomplete.
Upstream JIRA: IMPALA-7561

TSB 2022-543: Impala query with predicate on analytic function may produce incorrect results: Apache Impala may produce incorrect results for a query which has all of the following conditions:

There are two or more analytic functions (for example,row_number()) in an inline view

Some of the functions have partition-by expression while the others do not

There is a predicate on the inline view's output expression corresponding to the analytic function
Upstream JIRA: IMPALA-11030
Knowledge article: For the latest update on this issue, see the corresponding Knowledge article: TSB 2022-543: Impala query with predicate on analytic function may produce incorrect results