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Status

Current state: Under Discussion

Discussion thread: here

JIRA: Unable to render Jira issues macro, execution error.

Released: TBD

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

Motivation

Generally speaking, applications may consist of one or more jobs, and they may want to share the data with others. In Flink, the jobs in the same application are independent and share nothing among themselves. If a Flink application involves several sequential steps, each step (as an independent job) will have to write its intermediate results to an external sink, so that its results can be used by the following step (job) as sources.

Although functionality wise this works, this programming paradigm has a few shortcomings:

In order to share a result, a sink must be provided.
Complicated applications become inefficient due to large amount of IO on intermediate result.
User experience is weakened for users using programing API (SQL users are not victims here because the temporary tables are created by the framework)

It turns out that interactive programming support is critical to the user experience on Flink in batch processing scenarios. The following code gives an example:

Interactive Programming in Batch Processing

Table a = ….
Table b = a.select(...).filter(...)
int c = b.max()
int d = b.min() // recompute table b from table a
Table c = b.select((‘f1 - min)/(max - min)).filter(...)
c.print() // recompute table b from table a
...
If (b.count() > 10) { // recompute table b from table a
  b.select(UDF1(...)).print()// recompute table b from table a
} else {
  b.select(UDF2(...)).print()
}

In the above code, because b is not cached, it will be computed from scratch multiple times whenever referred later in the program.

To address the above issues, we propose to add support for interactive programming in Flink Table API.

Public Interfaces

1. Add the following two new methods to the Flink Table class.

cache() and invalidateCache() API

  /**
    * Cache this table to builtin table service or the specified customized table service.
    *
    * This method provides a hint to Flink that the current table maybe reused later so a
    * cache should be created to avoid regenerating this table.
    *
    * The following code snippet gives an example of how this method could be used.
    *
    * {{{
    *   val t = tEnv.fromCollection(data).as('country, 'color, 'count)
    *
    *   val t1 = t.filter('count < 100).cache()
    *   // t1 is cached after it is computed for the first time.
    *   val x = t1.collect().size
    *
    *   // When t1 is used again to compute t2, it may not be re-computed.
    *   val t2 = t1.groupBy('country).select('country, 'count.sum as 'sum)
    *   val res2 = t2.collect()
    *   res2.foreach(println)
    *
    *   // Similarly when t1 is used again to compute t3, it may not be re-computed.
    *   val t3 = t1.groupBy('color).select('color, 'count.avg as 'avg)
    *   val res3 = t3.collect()
    *   res3.foreach(println)
    *
    * }}}
    *
    * @note Flink optimizer may decide to not use the cache if doing that will accelerate the
    * processing, or if the cache is no longer available for reasons such as the cache has
    * been invalidated.
    * @note The table cache could be create lazily. That means the cache may be created at 
    * the first time when the cached table is computed.
    * @note The table cache will be cleared when the user program exits.
    *
    * @return the current table with a cache hint. The original table reference is not modified
    *               by the execution of this method.
    */
  def cache(): Table


  /**
    * Manually invalidate the cache of this table to release the physical resources. Users are
    * not required to invoke this method to release physical resource unless they want to. The
    * table caches are cleared when user program exits.
    *
    * @note After invalidated, the cache may be re-created if this table is used again.
    */
  def invalidateCache(): Unit

2. Add a close method to the TableEnvironment

TableEnvironment.close()

/**
  * Close and clean up the table environment.
  */
void close();

Proposed Changes

Cache intermediate results

As mentioned in the motivation section. The key idea of the FLIP is to allow the intermediate process results to be cached, so later references to that result does not result in duplicate computation. To achieve that, we need to introduce Cached Tables.

The cached tables are tables whose contents are saved by Flink as the user application runs. A cached Table can be created in two ways:

Explicit caching

Users can call cache() method on a table to explicitly tell Flink to cache a Table.
The cache() method returns a new Table object with a flag set.
The cache() method does not execute eagerly. Instead, the table will be cached when the DAG that contains the cached table runs.

Auto caching

As the application runs, if Flink can save an intermediate result with little cost, Flink will do that, even users did not call cache() explicitly. Such case typically occurs at shuffle boundaries.
Auto caching will be enabled by default but could be turned off by users.

Semantic of cache() method

The semantic of the cache() method is a little different depending on whether auto caching is enabled or not.

When auto caching is enabled (default behavior)

Auto caching is enabled (default)

TableEnvironment tEnv = ...
Table t1 = ...
Table t2 = t1.cache()
...
tEnv.execute() // t1 is cached.
Table t3 = t1.select(...) // cache will be used.
Table t4 = t2.select(...) // cache will be used.

When auto caching is disabled

Auto caching is disabled

TableEnvironment tEnv = ...
Table t1 = ...
Table t2 = t1.cache()
...
tEnv.execute() // t1 is cached.
Table t3 = t1.select(...) // cache will NOT be used. <----- difference
Table t4 = t2.select(...) // cache will be used.

Scope of the cached result

The cached tables are available to the user application running in the same Flink cluster using the same TableEnvironment.

Release the cached results

The cached intermediate results will consume some resources and needs to be released eventually. The cached result will be released in two cases.

User application exits

When TableEnvironment is closed, the resources consumed by the cached tables will also be released. This usually happens when user application exits.

Explicit invalidateCache() invocation

Sometimes users may want to release the resource used by a cached table before the application exits. In this case, users can call invalidateCache() on a table. This will immediately release the resources used to cache that table.

Implementation Details

To let the feature available out of the box, a default file system based cache service will be provided. This section describes the implementation details of the default table service.

Although the implementation details are transparent to the users, there are some changes to Flink internals to make it work.

Default Cache Service

The architecture is illustrated below:

Each cached table consists of two pieces of information:

Table metadata - name, location, etc.
Table contents - the actual contents of the table

Default table service stores the metadata in client (e.g. TableEnvironment) and saves the actual contents in the cache service instances running inside the Task Managers.

The end to end process is the following:

When a DAG of JOB_1 is submitted, the TableEnvironment (planner) checks the DAG to see if any node should be cached.
If there is a node to be cached, the TableEnvironment (CacheServiceManager) will first start a Cache Service job (if the job has not started). To start a cache service job,
1. The TableServiceManager opens a ServerSocket and listens on that port.
2. The TableServiceManager submits a Cache Service job.
  1. The Cache Service job contains only source nodes which runs Cache Server code.
  2. The parallelism of the Cache Service job equals to the number of TMs so that each TM has one Cache Service instance running in it.
3. Each Cache Service instance in the TM will open a server socket and register their HOST:PORT information back to the port opened by TableServiceManager in step 2a.
The CacheServiceManager waits until it collects all the HOST:PORT pairs from the Table Service Instances.
The TableEnvironment adds a CacheServiceSink to each cached node (a node with a corresponding Cached Table) in the DAG.
1. The CacheServiceSink acts as a normal sink, as if user called writeToSink(CacheServiceSink).
2. Each cached node will be assigned a intermediate result UUID. The UUID is used to tag the when the CacheServiceSink writes the intermediate result to the Cache Service instances.
3. Since there will be multiple Cache Service instances, the intermediate result will be partitioned and stored in a distributed manner.
The TableEnvironment submits the modified DAG to the Flink cluster.
After the JOB_1 finishes, the CacheServiceManager stores the [LogicalNode → intermediate result UUID] in memory.
Later on, if JOB_2 is submitted and the DAG contains a node that has already been cached (a mapping to intermediate result UUID is found), TableEnvironment replaces the node and the connected ancestor nodes with a CacheServiceSource.
1. The CacheServiceSource has the [HOST:PORT] pairs of all the Cache Service instances.
2. The CacheServiceSource will be reading from the Cache Service instances using the UUID corresponding to the cached intermediate results.
When invalidateCache() is invoked, the CacheServiceManager will directly make an RPC call to each Cache Service instance to delete the corresponding intermediate result. It will also remove the LogicalNode to intermedate result UUID mapping.
When the application exits, the CacheServiceManager will be closed on TableEnvironment exits. It will stop the Cache Service job, so all the cached tables will be released.

Failover

If a Cache Service instance fails, since Cache Service is a Flink job. The failed subtask will be brought up again. However, all the intermediate result which has a partition on the failed subtask will become unavailable.

In this case, the CacheServiceSource / CacheServiceSink will throw an exception and the job will fail. As a result, CacheServiceManager invalidates the caches that are impacted. The TableEnvironment will resubmit the original DAG without using the cache. Note that because there is no cache available, the TableEnvironment (planner) will add a CacheServiceSink to the cached nodes, therefore the cache will be recreated after the execution of the original DAG.

The above process is transparent to the users.

Impact to optimization

When users explicitly caches a table, the DAG is implicitly changed. This is because the optimizer may decide to change the cached node if it were not explicitly cached. As of now, when a node has more than one downstream nodes in the DAG, that node will be computed multiple times. This could be solved by introducing partial optimization for subgraphs, which is a feature available in Blink but not in Flink yet. This is an existing problem in Flink and this FLIP does not attempt to address that.

Integration with external shuffle service

To achieve auto caching, the cache service needs to be integrated with shuffle service. This is sort of a natural move. Inherently, external shuffle service and cache service share a lot of similarity.

Compatibility, Deprecation, and Migration Plan

What impact (if any) will there be on existing users?
If we are changing behavior how will we phase out the older behavior?
If we need special migration tools, describe them here.
When will we remove the existing behavior?

Test Plan

Describe in few sentences how the FLIP will be tested. We are mostly interested in system tests (since unit-tests are specific to implementation details). How will we know that the implementation works as expected? How will we know nothing broke?

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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FLIP-36: Support Interactive Programming in Flink