This documentation is for an out-of-date version of Apache Flink. We recommend you use the latest stable version.
Upgrading Applications and Flink Versions #
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.
DataStream API #
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
Important: As of 1.3.x this also applies to operators that are part of a chain.
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. Users may enforce best practices by calling
ExecutionConfig#disableAutoGeneratedUIDs which will fail the job submission if any operator does not contain a custom unique ID.
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.
|Operator||Data Type of Internal Operator State|
|ReduceFunction[IOT]||IOT (Input and 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. When restoring from a savepoint taken since 1.3.x it is possible to modify chains while preserving state consistency. It is possible a break the chain such that a stateful operator is moved out of the chain. It is also possible to append or inject a new or existing stateful operator into a chain, or to modify the operator order within a chain. However, when upgrading a savepoint to 1.3.x it is paramount that the topology did not change in regards to chaining. All operators that are part of a chain should be assigned an ID as described in the Matching Operator State section above.
Table API & SQL #
Due to the declarative nature of Table API & SQL programs, the underlying operator topology and state representation are mostly determined and optimized by the table planner.
Be aware that any change to both the query and the Flink version could lead to state incompatibility.
Every new major-minor Flink version (e.g.
1.13) might introduce new optimizer rules or more
specialized runtime operators that change the execution plan. However, the community tries to keep patch
versions state-compatible (e.g.
See the table state management section for more information.
Upgrading the Flink Framework Version #
This section describes the general way of upgrading Flink across versions and migrating your jobs between the versions.
In a nutshell, this procedure consists of 2 fundamental steps:
- Take a savepoint in the previous, old Flink version for the jobs you want to migrate.
- Resume your jobs under the new Flink version 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 across Flink versions: in-place upgrade and shadow copy upgrade.
For in-place update, after taking savepoints, you need to:
- Stop/cancel all running jobs.
- Shutdown the cluster that runs the old Flink version.
- Upgrade Flink to the newer version on the cluster.
- Restart the cluster under the new version.
For shadow copy, you need to:
- Before resuming from the savepoint, setup a new installation of the new Flink version besides your old Flink installation.
- Resume from the savepoints with the new Flink installation.
- If everything runs ok, stop and shutdown the old Flink 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.
Before starting the migration, please check that the jobs you are trying to migrate are following the best practices 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
If you run into a case where this is not working, you can manually add the generated legacy vertex ids from previous
Flink versions 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 two hard preconditions for job migration that will make migration fail:
We do not support migration for state in RocksDB that was checkpointed using
semi-asynchronousmode. In case your old job was using this mode, you can still change your job to use
fully-asynchronousmode before taking the savepoint that is used as the basis for the migration.
Another important precondition is that all the savepoint data must be accessible from the new installation under the same (absolute) path. This also includes access to any additional files that are referenced from inside the savepoint file (the output from state backend snapshots), including, but not limited to additional referenced savepoints from modifications with the State Processor API.
STEP 1: Stop the existing job with a savepoint #
The first major step in version migration is taking a savepoint and stopping your job running on the old Flink version.
You can do this with the command:
$ bin/flink stop [--savepointPath :savepointPath] :jobId
For more details, please read the savepoint documentation.
STEP 2: Update your cluster to the new Flink version. #
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, …).
If you are unfamiliar with installing Flink in your cluster, please read the deployment and cluster setup documentation.
STEP 3: Resume the job under the new Flink version from savepoint. #
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]
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||1.3.x||1.4.x||1.5.x||1.6.x||1.7.x||1.8.x||1.9.x||1.10.x||1.11.x||1.12.x||1.13.x||1.14.x||Limitations|
|1.1.x||O||O||O||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.|
When migrating from Flink 1.2.x to Flink 1.3.x+, changing parallelism at the same
time is not supported. Users have to first take a savepoint after migrating to Flink 1.3.x+, and then change
Savepoints created for CEP applications cannot be restored in 1.4.x+.
Savepoints from Flink 1.2 that contain a Scala TraversableSerializer are not compatible with Flink 1.8 anymore because of an update in this serializer. You can get around this restriction by first upgrading to a version between Flink 1.3 and Flink 1.7 and then updating to Flink 1.8.
|1.3.x||O||O||O||O||O||O||O||O||O||O||O||O||Migrating from Flink 1.3.0 to Flink 1.4.[0,1] will fail if the savepoint contains Scala case classes. Users have to directly migrate to 1.4.2+ instead.|
|1.5.x||O||O||O||O||O||O||O||O||O||O||There is a known issue with resuming broadcast state created with 1.5.x in versions 1.6.x up to 1.6.2, and 1.7.0: FLINK-11087. Users upgrading to 1.6.x or 1.7.x series need to directly migrate to minor versions higher than 1.6.2 and 1.7.0, respectively.|
|1.13.x||O||O||Don't upgrade from 1.12.x to 1.13.x with an unaligned checkpoint. Please use a savepoint for migrating.|