Known issues and limitations

Learn about the known issues in Flink and SQL Stream Builder, the impact or changes to the functionality, and the workaround in Cloudera Streaming Analytics 1.7.0.

SQL Stream Builder

CSA-3536: SSB jobs sometimes fail to stop correctly
This issue manifests itself as a job continuing to run after clicking the Stop button on Compose window of the Streaming SQL Console.
You can cancel the job from Flink Dashboard in case you cannot stop it on the Streaming SQL Console:
  1. Reload the window of your browser.
  2. Click Flink Dashboard from the main menu.
  3. Select the Cluster link for the job that failed to stop.
  4. Select the job from the list.
  5. Select Cancel Job at the top right of the page.
CSA-3529: Filtering ARRAY typed columns do not work for Materialized Views
ARRAY typed columns are treated as STRING when setting filters for a Materialized View query which leads to incorrect results.
CSA-3507: IN and NOT IN operators cannot be used for Materialized Views
Using IN and NOT IN operators when filtering Materialized Views throws an error and cannot be used.
CSA-2016: Deleting table from other teams
There is a limitation when using the Streaming SQL Console for deleting tables. It is not possible to delete a table that belongs to another team using the Delete button on the User Interface.
Use DROP TABLE statement from the SQL window.
CSA-1454: Timezone settings can cause unexpected behavior in Kafka tables
You must consider the timezone settings of your environment when using timestamps in a Kafka table as it can affect the results of your query. When the timestamp in a query is identified with from_unixtime, it returns the results based on the timezone of the system. If the timezone is not set in UTC+0, the timestamp of the query results will shift in time and will not be correct.
Change your local timezone settings to UTC+0.
CSA-1231: Big numbers are incorrectly represented on the Streaming SQL Console UI
The issue impacts the following scenarios in Streaming SQL Console:
  • When having integers bigger than 253-1 among your values, the Input transformations and User Defined Functions are considered unsafe and produce incorrect results as these numbers will lose precision during parsing.
  • When having integers bigger than 253-1 among your values, sampling to the Streaming SQL Console UI produces incorrect results as these numbers will lose precision during parsing.


In Cloudera Streaming Analytics, the following SQL API features are in preview:
  • Match recognize
  • Top-N
  • Stream-Table join (without rowtime input)
DataStream conversion limitations
  • Converting between Tables and POJO DataStreams is currently not supported in CSA.
  • 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
    • 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: true.