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Current state"Accepted"

Discussion thread:

JIRA: FLINK-7594 - Getting issue details... STATUS

Released: 1.5

Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).


SQL is undoubtedly the most widely used language for data analytics. It is declarative and can be optimized and efficiently executed by most query processors. The necessity to apply those concepts also to stream processors is a logical consequence for making streaming accessible to a broader audience and enable faster development without exact knowledge of the underlying runtime.


Although Flink’s Table & SQL API allows to declare queries in the SQL language. A SQL query needs to be embedded within a table program that is written either in Java or Scala. The table program needs to be packaged with Maven before it can be submitted to a cluster. This limits the usage of Flink to mostly Java/Scala programmers.

In a long-term view, with the SQL Client proposed in this document we want to:

  • make Flink accessible to non-programmers such as data scientists

  • allow to develop Flink applications without an IDE and without knowledge about Maven

  • make it possible to integrate other tools by submitting queries to Flink via JDBC or REST interfaces

  • enable new use cases by offering job results via JDBC or REST interface and supplying regular result snapshots to dashboards

The goal of this FLIP is to have an initial minimum viable product for using Flink with SQL without an IDE. We will use this product to incrementally refine the requirements based on the feedback from users and contributors. Further FLIPs and design documents might follow in order to define REST/JDBC capabilities or materialized view semantics.

Public Interfaces

  • A new Maven module “flink-sql-client” with the SQL client

  • A new binary file for executing the SQL client in embedded mode

  • New default configuration files and library directory

Proposed Changes

General Architecture

The SQL Client can be executed in two modes: a gateway and embedded mode. In this FLIP we mostly focus on the embedded mode but also consider a later gateway conceptually.

  • In gateway mode, a dedicated SQL gateway process offers APIs to which other applications can connect to. A REST API and JDBC API allow to submit queries, cancel jobs, and retrieve results. Both REST and JDBC connect to a common executor that is responsible for communicating with Flink and external catalogs. The executor also keeps state about currently running sessions. The optional SQL CLI client connects to the REST API of the gateway and allows for managing queries via console.

  • In embedded mode, the SQL CLI is tightly coupled with the executor in a common process. This allows for playing around with Flink quickly and submit jobs without having to start an additional components.

The communication to Flink happens through the ClusterClient. By using the ClusterClient we can ensure that the SQL client will run Flink on YARN, Mesos, and other supported environments without additional effort.

For the embedded mode, a general communication flow would like:

  • The user adds catalog information to the configuration files.

  • The user starts CLI client with custom JAR files and configuration (--jar, --context).

  • The user enters SQL query and submits it.

  • The executor submits Flink job with all involved JAR files.

  • If a query is a SELECT query, the executor materializes the result such that it can be pulled by the CLI client.

  • If a query is a INSERT INTO query, the executor submits the Flink job.

  • The user can stop the running query and submit a new one.

  • Exiting the CLI would also stop the running SELECT query but not INSERT INTO queries.

Gateway Mode


Embedded Mode



Independent of the execution mode, the SQL client can be configured globally (sql-defaults.conf) and/or for every CLI session (sql-context.conf). The configuration specifies settings that would programmatically be applied to a ExecutionEnvironment/StreamExecutionEnvironment and TableEnvironment. It contains catalog information as well as job specific parameters.

The global configuration is located in a dedicated file and is read during startup. Global configuration applies to all CLI sessions. A context configuration file can be specified when starting the CLI client and is attached to any query executed in the current CLI session. Thus, context properties might overwrite global default properties. In future versions, the configuration might also be refined by using DDL statements such as:

DDL statements will have highest priority and overwrite the properties specified in configuration files.

Here are some properties that might need to be defined in the long run:

  • Pre-registered table sources

    • Name

    • Source

      • Type (e.g. Kafka, Custom)

      • Properties (e.g. topic, connection info, custom TableSource class)

    • Encoding

      • Type (e.g. Avro, JSON)

      • Schema (e.g. Avro class, JSON field names/types)

    • Rowtime descriptor/Proctime

      • Watermark strategy and Watermark properties

      • Time attribute info

    • Bucketization

    • Statistics

  • Pre-registered table sinks

    • Name

    • Sink

      • Type (e.g. Kafka, Custom)

      • Properties (e.g. destination path, output types)

  • External catalogs

    • Name

    • Properties (e.g. connection info, credentials, ExternalCatalog class)

  • User-defined functions

    • Name

    • Parameters (e.g. constructor parameters for a TableFunction)

    • Class

  • [Optional] User-defined types

    • Name

    • Field names and types

  • [Optional] User-defined local variables (@local_variable)

    • Name

    • Value

  • Job parameters

    • Batch or streaming execution

    • Parallelism

    • Maximum parallelism

    • State Backend + parameters

    • Auto watermark interval

    • Restart strategy

    • Query Config (e.g. min/max retention time)

  • [Separate configuration?] SQL client parameters

    • Gateway properties
      (e.g. database properties, server credentials)

    • CLI Client properties
      (e.g. timeouts, credentials for authenticating to gateway, result serving speed)

Result Retrieval


In the future, we can use different options for retrieving materialized results both for debugging purposes and long-running maintained views. The retrieval is managed by the executor.

There are three materialization modes:


  • Internal mode (for small results):
    collect() into Heap/Derby/... (we decide internally how this is done)
    We use DataSet/DataStream collect in order to retrieve results. They are materialized in the executor and deleted once pulled by the CLI client.

  • External mode (for larger results):
    automatic TableSink into Kafka, HDFS, DBMS, etc. (the content is accessible for the executor)
    Flink writes larger results into external systems by using pre-defined TableSinks. The executor knows about the format and location and can access the information there.

  • Custom mode (for any result):
    custom TableSink into arbitrary systems
    Flink writes any result into a destination that is determined by the user using INSERT INTO. The results are not accessible by the executor and can thus not be served via REST or CLI console.

The supported materialization mode also depends on the query type:

Query Type

Internal Mode

External Mode*


collect() -> Heap/Database

File table sink

Append Stream

collect() -> Heap/Database

Kafka/file table sink

Retract/Upsert Stream

collect() -> Heap/Database

(Compacted Kafka)/Cassandra table sink

We might use usual heap space at the beginning. The internal database can be any JDBC database. External materialization modes (*) are not included in the first version. In the future, Kafka would be read by general Kafka utility functions. Files as well with support for different file systems.

Result Maintenance

While batch queries have bounded results, streaming queries are potentially never-ending and, therefore, require special treatment for keeping the results up to date and consistent. The streaming query can be considered as a view and the running streaming application as the view maintenance. Results might need to be supplied to systems that were not made for streaming queries, e.g., Java applications that read from a JDBC API. In those cases, every requested result set must be a snapshot (materialized view) of the running query at a point in time. The snapshot must be immutable until all result rows have been consumed by the application or a new result set is requested. We distinguish between two types of results that will require different materialization semantics: a materialized view and a materialized result stream.

Materialized View

A consistent materialized view of results for production use cases. Materialized views are not part of this FLIP but might be added in future versions. It requires another design document for the DDL statement and execution but here are some properties we aim for:


  • intended for long running materialization queries that are updated periodically (e.g. every hour or on successful checkpoints)

  • retractions are not visible directly, only the materialized result

  • a result can be accessed by JDBC connections or the REST API (e.g. for powering dashboards)

  • materialization operators can cooperate with Flink's checkpointing (e.g. only checkpointed results are exposed through the APIs)

  • a user can specify different parameters for how and how often the view is maintained
    (see create_mv_refresh)

  • runs detached from the CLI client

Materialized Result Stream

A materialized stream of results for getting immediate insights into the running SQL query.


  • indended for debugging during query creation and initial show cases

  • retractions are shown as streams of deletion and insertion

  • no guarantees about checkpointed results

  • the executor abstracts the underlying representation and supplies the interfaces for accessing the materialized stream in a FIFO fashion

  • only one running query per CLI session

  • cancelled if cancelled in CLI or CLI is closed

We focus on simple SELECT queries first that are materialized on the heap of the executor (internal materialization mode).

Compatibility, Deprecation, and Migration Plan

 No compatibility changes or other deprecation necessary.

Implementation Plan

1. Basic Embedded SQL Client 

Add the basic features to play around with Flink's streaming SQL.

  • Add CLI component that reads the configuration files
    • "Pre-registered table sources"
    • "Job parameters"
  • Add executor for retrieving pre-flight information and corresponding CLI SQL parser
  • Add streaming append query submission to executor

    • Submit jars and run SELECT query using the ClusterClient

    • Collect results on heap and serve them on the CLI side (Internal Mode with SELECT)

    • EXECUTE (for executing a SQL statement stored in a local file)

2. Full Embedded SQL Client

Add important features to fully use the SQL client for a variety of use cases.

  • Add support for streaming retract/upsert queries
  • Add support for batch queries
  • Add user-defined function support

    • Support scalar, aggregate, and table functions
    • Submit corresponding jars to the cluster
  • Add CLI query history
  • Add CLI query code completion/highlighting
  • Add support for INSERT INTO
    • Read configuration about "Pre-registered table sinks"
    • Allow submission without collect()

3. Discuss/design further features

Discuss and prioritize other features that are not part of this FLIP. 

  • Introduce gateway mode with REST API

  • Add support for catalogs

  • Allow creating Materialized Views

  • Support other materialization backends

  • Create a JDBC API

  • Further SQL DDL statements:



Rejected Alternatives

If there are alternative ways of accomplishing the same thing, what were they? The purpose of this section is to motivate why the design is the way it is and not some other way.

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