アプリケーションとFlinkバージョンのアップグレード

Flink DataStream programs are typically designed to run for long periods of time such as weeks, months, or even years. As with all long-running services, Flink streaming applications need to be maintained, which includes fixing bugs, implementing improvements, or migrating an application to a Flink cluster of a later version.

This document describes how to update a Flink streaming application and how to migrate a running streaming application to a different Flink cluster.

Restarting Streaming Applications

The line of action for upgrading a streaming application or migrating an application to a different cluster is based on Flink’s Savepoint feature. A savepoint is a consistent snapshot of the state of an application at a specific point in time.

There are two ways of taking a savepoint from a running streaming application.

  • Taking a savepoint and continue processing. > ./bin/flink savepoint <jobID> [pathToSavepoint] It is recommended to periodically take savepoints in order to be able to restart an application from a previous point in time.

  • Taking a savepoint and stopping the application as a single action. > ./bin/flink cancel -s [pathToSavepoint] <jobID> This means that the application is canceled immediately after the savepoint completed, i.e., no other checkpoints are taken after the savepoint.

Given a savepoint taken from an application, the same or a compatible application (see Application State Compatibility section below) can be started from that savepoint. Starting an application from a savepoint means that the state of its operators is initialized with the operator state persisted in the savepoint. This is done by starting an application using a savepoint. > ./bin/flink run -d -s [pathToSavepoint] ~/application.jar

The operators of the started application are initialized with the operator state of the original application (i.e., the application the savepoint was taken from) at the time when the savepoint was taken. The started application continues processing from exactly this point on.

Note: Even though Flink consistently restores the state of an application, it cannot revert writes to external systems. This can be an issue if you resume from a savepoint that was taken without stopping the application. In this case, the application has probably emitted data after the savepoint was taken. The restarted application might (depending on whether you changed the application logic or not) emit the same data again. The exact effect of this behavior can be very different depending on the SinkFunction and storage system. Data that is emitted twice might be OK in case of idempotent writes to a key-value store like Cassandra but problematic in case of appends to a durable log such as Kafka. In any case, you should carefully check and test the behavior of a restarted application.

Application State Compatibility

When upgrading an application in order to fix a bug or to improve the application, usually the goal is to replace the application logic of the running application while preserving its state. We do this by starting the upgraded application from a savepoint which was taken from the original application. However, this does only work if both applications are state compatible, meaning that the operators of upgraded application are able to initialize their state with the state of the operators of original application.

In this section, we discuss how applications can be modified to remain state compatible.

Matching Operator State

When an application is restarted from a savepoint, Flink matches the operator state stored in the savepoint to stateful operators of the started application. The matching is done based on operator IDs, which are also stored in the savepoint. Each operator has a default ID that is derived from the operator’s position in the application’s operator topology. Hence, an unmodified application can always be restarted from one of its own savepoints. However, the default IDs of operators are likely to change if an application is modified. Therefore, modified applications can only be started from a savepoint if the operator IDs have been explicitly specified. Assigning IDs to operators is very simple and done using the uid(String) method as follows:

val mappedEvents: DataStream[(Int, Long)] = events
  .map(new MyStatefulMapFunc()).uid(“mapper-1”)

Note: Since the operator IDs stored in a savepoint and IDs of operators in the application to start must be equal, it is highly recommended to assign unique IDs to all operators of an application that might be upgraded in the future. This advice applies to all operators, i.e., operators with and without explicitly declared operator state, because some operators have internal state that is not visible to the user. Upgrading an application without assigned operator IDs is significantly more difficult and may only be possible via a low-level workaround using the setUidHash() method.

By default all state stored in a savepoint must be matched to the operators of a starting application. However, users can explicitly agree to skip (and thereby discard) state that cannot be matched to an operator when starting a application from a savepoint. Stateful operators for which no state is found in the savepoint are initialized with their default state.

Stateful Operators and User Functions

When upgrading an application, user functions and operators can be freely modified with one restriction. It is not possible to change the data type of the state of an operator. This is important because, state from a savepoint can (currently) not be converted into a different data type before it is loaded into an operator. Hence, changing the data type of operator state when upgrading an application breaks application state consistency and prevents the upgraded application from being restarted from the savepoint.

Operator state can be either user-defined or internal.

  • User-defined operator state: In functions with user-defined operator state the type of the state is explicitly defined by the user. Although it is not possible to change the data type of operator state, a workaround to overcome this limitation can be to define a second state with a different data type and to implement logic to migrate the state from the original state into the new state. This approach requires a good migration strategy and a solid understanding of the behavior of key-partitioned state.

  • Internal operator state: Operators such as window or join operators hold internal operator state which is not exposed to the user. For these operators the data type of the internal state depends on the input or output type of the operator. Consequently, changing the respective input or output type breaks application state consistency and prevents an upgrade. The following table lists operators with internal state and shows how the state data type relates to their input and output types. For operators which are applied on a keyed stream, the key type (KEY) is always part of the state data type as well.

オペレータ Data Type of Internal Operator State
ReduceFunction[IOT] IOT (Input and output type) [, KEY]
FoldFunction[IT, OT] OT (Output type) [, KEY]
WindowFunction[IT, OT, KEY, WINDOW] IT (Input type), KEY
AllWindowFunction[IT, OT, WINDOW] IT (Input type)
JoinFunction[IT1, IT2, OT] IT1, IT2 (Type of 1. and 2. input), KEY
CoGroupFunction[IT1, IT2, OT] IT1, IT2 (Type of 1. and 2. input), KEY
Built-in Aggregations (sum, min, max, minBy, maxBy) Input Type [, KEY]

Application Topology

Besides changing the logic of one or more existing operators, applications can be upgraded by changing the topology of the application, i.e., by adding or removing operators, changing the parallelism of an operator, or modifying the operator chaining behavior.

When upgrading an application by changing its topology, a few things need to be considered in order to preserve application state consistency.

  • Adding or removing a stateless operator: This is no problem unless one of the cases below applies.
  • Adding a stateful operator: The state of the operator will be initialized with the default state unless it takes over the state of another operator.
  • Removing a stateful operator: The state of the removed operator is lost unless another operator takes it over. When starting the upgraded application, you have to explicitly agree to discard the state.
  • Changing of input and output types of operators: When adding a new operator before or behind an operator with internal state, you have to ensure that the input or output type of the stateful operator is not modified to preserve the data type of the internal operator state (see above for details).
  • Changing operator chaining: Operators can be chained together for improved performance. However, chaining can limit the ability of an application to be upgraded if a chain contains a stateful operator that is not the first operator of the chain. In such a case, it is not possible to break the chain such that the stateful operator is moved out of the chain. It is also not possible to append or inject an existing stateful operator into a chain. The chaining behavior can be changed by modifying the parallelism of a chained operator or by adding or removing explicit operator chaining instructions.

This section describes the general way of upgrading Flink framework version from version 1.1.x to 1.2.x and migrating your jobs between the two versions.

In a nutshell, this procedure consists of 2 fundamental steps:

  1. Take a savepoint in Flink 1.1.x for the jobs you want to migrate.
  2. Resume your jobs under Flink 1.2.x from the previously taken savepoints.

Besides those two fundamental steps, some additional steps can be required that depend on the way you want to change the Flink version. In this guide we differentiate two approaches to upgrade from Flink 1.1.x to 1.2.x: in-place upgrade and shadow copy upgrade.

For in-place update, after taking savepoints, you need to:

  1. Stop/cancel all running jobs.
  2. Shutdown the cluster that runs Flink 1.1.x.
  3. Upgrade Flink to 1.2.x. on the cluster.
  4. Restart the cluster under the new version.

For shadow copy, you need to:

  1. Before resuming from the savepoint, setup a new installation of Flink 1.2.x besides your old Flink 1.1.x installation.
  2. Resume from the savepoints with the new Flink 1.2.x installation.
  3. If everything runs ok, stop and shutdown the old Flink 1.1.x cluster.

In the following, we will first present the preconditions for successful job migration and then go into more detail about the steps that we outlined before.

前提条件

Before starting the migration, please check that the jobs you are trying to migrate are following the best practises for savepoints. In particular, we advise you to check that explicit uids were set for operators in your job.

This is a soft precondition, and restore should still work in case you forgot about assigning uids. If you run into a case where this is not working, you can manually add the generated legacy vertex ids from Flink 1.1 to your job using the setUidHash(String hash) call. For each operator (in operator chains: only the head operator) you must assign the 32 character hex string representing the hash that you can see in the web ui or logs for the operator.

Besides operator uids, there are currently three hard preconditions for job migration that will make migration fail:

  1. as mentioned in earlier release notes, we do not support migration for state in RocksDB that was checkpointed using semi-asynchronous mode. In case your old job was using this mode, you can still change your job to use fully-asynchronous mode before taking the savepoint that is used as the basis for the migration.

  2. The CEP operator is currently not supported for migration. If your job uses this operator you can (curently) not migrate it. We are planning to provide migration support for the CEP operator in a later bugfix release.

  3. Another important precondition is that all the savepoint data must be accessible from the new installation and reside under the same absolute path. Please notice that the savepoint data is typically not self contained in just the created savepoint file. Additional files can be referenced from inside the savepoint file (e.g. the output from state backend snapshots)! There is currently no simple way to identify and move all data that belongs to a savepoint.

First major step in job migration is taking a savepoint of your job running in Flink 1.1.x. You can do this with the command:

$ bin/flink savepoint :jobId [:targetDirectory]

For more details, please read the savepoint documentation.

In this step, we update the framework version of the cluster. What this basically means is replacing the content of the Flink installation with the new version. This step can depend on how you are running Flink in your cluster (e.g. standalone, on Mesos, …).

If you are unfamiliar with installing Flink in your cluster, please read the deployment and cluster setup documentation.

As the last step of job migration, you resume from the savepoint taken above on the updated cluster. You can do this with the command:

$ bin/flink run -s :savepointPath [:runArgs]

Again, for more details, please take a look at the savepoint documentation.

Compatibility Table

Savepoints are compatible across Flink versions as indicated by the table below:

Created with \ Resumed with 1.1.x 1.2.x
1.1.x X X
1.2.x   X
  • The maximum parallelism of a job that was migrated from Flink 1.1.x to 1.2.x is currently fixed as the parallelism of the job. This means that the parallelism can not be increased after migration. This limitation might be removed in a future bugfix release.
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