Cloudera Docs

Known Issues in Cloudera Streaming Analytics

Learn about the known issues in Cloudera Streaming Analytics clusters, the impact or changes to the functionality, and the workaround.

CSA-6232 - Setting execution target in SQL statement does not work properly
In , using a SET statement to set execution.target to yarn-per-job in the SQL Editor does not work as expected on 1.13.0 and later. SSB can create both a session cluster and a per-job YARN application for the same job. On secure clusters, this can cause Kerberos authentication errors.
Configure per-job (production) execution in Job Settings by selecting PROD - Job runs in isolated Cluster at Execution Mode. Do not rely on SET 'execution.target' = 'yarn-per-job' in the SQL Editor. For more information, see Executing SQL jobs in production mode.
CSA-5733 - Chart or diagram type dashboard widgets do not work when the label field is the same as the data field
When creating a diagram type widget in , setting the label and data fields to the same value causes the graph to disappear.
None.
CSA-5732 - MV widget not fetching mv data when authenticated via spnego
When using with SPNEGO authentication, creating a Materialized View widget fails with a Data Source Error.
Authenticate through Knox Gateway instead of SPNEGO.

Flink

In Cloudera Streaming Analytics, the following SQL API features are in preview:
  • Match recognize
  • Top-N
  • Stream-Table join (without rowtime input)
Third-party dependencies upgraded in might cause Flink jobs to fail

After upgrading , Flink jobs might fail due to upgraded third-party dependencies. For example, this could happen with awssdk when its version changes in a newer release such as 7.3.2.

Verify your application's dependency versions against the -supported versions before upgrading to a newer version of . For more information, see Updating Flink job dependencies.
DataStream conversion limitations
  • Converting between Tables and POJO DataStreams is currently not supported in .
  • Object arrays are not supported for Tuple conversion.
  • The java.time class conversions for Tuple DataStreams are only supported by using explicit TypeInformation: LegacyInstantTypeInfo, LocalTimeTypeInfo.getInfoFor(LocalDate/LocalDateTime/LocalTime.class).
  • Only java.sql.Timestamp is supported for rowtime conversion, java.time.LocalDateTime is not supported.
Kudu catalog limitations
  • CREATE TABLE
    • Primary keys can only be set by the kudu.primary-key-columns property. Using the PRIMARY KEY constraint is not yet possible.
    • Range partitioning is not supported.
  • When getting a table through the catalog, NOT NULL and PRIMARY KEY constraints are ignored. All columns are described as being nullable, and not being primary keys.
  • Kudu tables cannot be altered through the catalog other than simply renaming them.
Schema Registry catalog limitations
  • Currently, the Schema Registry catalog / format only supports reading messages with the latest enabled schema for any given Kafka topic at the time when the SQL query was compiled.
  • No time-column and watermark support for Registry tables.
  • No CREATE TABLE support. Schemas have to be registered directly in the SchemaRegistry to be accessible through the catalog.
  • The catalog is read-only. It does not support table deletions or modifications.
  • By default, it is assumed that Kafka message values contain the schema id as a prefix, because this is the default behaviour for the SchemaRegistry Kafka producer format. To consume messages with schema written in the header, the following property must be set for the Registry client: store.schema.version.id.in.header: true.