sjwiesman commented on a change in pull request #8330: [FLINK-12388][docs]
Update the production readiness checklist
URL: https://github.com/apache/flink/pull/8330#discussion_r282937987
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File path: docs/ops/production_ready.md
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@@ -22,79 +22,54 @@ specific language governing permissions and limitations
under the License.
-->
+The production readiness checklist provides an overview of configuration
options that should be carefully considered before bringing an Apache Flink job
into production.
+While the Flink community has attempted to provide sensible defaults for each
configuration, it is important to review this list and ensure the options
chosen are sufficient for your needs.
+
* ToC
{:toc}
-## Production Readiness Checklist
-
-Purpose of this production readiness checklist is to provide a condensed
overview of configuration options that are
-important and need **careful considerations** if you plan to bring your Flink
job into **production**. For most of these options
-Flink provides out-of-the-box defaults to make usage and adoption of Flink
easier. For many users and scenarios, those
-defaults are good starting points for development and completely sufficient
for "one-shot" jobs.
-
-However, once you are planning to bring a Flink application to production the
requirements typically increase. For example,
-you want your job to be (re-)scalable and to have a good upgrade story for
your job and new Flink versions.
-
-In the following, we present a collection of configuration options that you
should check before your job goes into production.
-
-### Set maximum parallelism for operators explicitly
-
-Maximum parallelism is a configuration parameter that is newly introduced in
Flink 1.2 and has important implications
-for the (re-)scalability of your Flink job. This parameter, which can be set
on a per-job and/or per-operator granularity,
-determines the maximum parallelism to which you can scale operators. It is
important to understand that (as of now) there
-is **no way to change** this parameter after your job has been started, except
for restarting your job completely
-from scratch (i.e. with a new state, and not from a previous
checkpoint/savepoint). Even if Flink would provide some way
-to change maximum parallelism for existing savepoints in the future, you can
already assume that for large states this is
-likely a long running operation that you want to avoid. At this point, you
might wonder why not just to use a very high
-value as default for this parameter. The reason behind this is that high
maximum parallelism can have some impact on your
-application's performance and even state sizes, because Flink has to maintain
certain metadata for its ability to rescale which
-can increase with the maximum parallelism. In general, you should choose a max
parallelism that is high enough to fit your
-future needs in scalability, but keeping it as low as possible can give
slightly better performance. In particular,
-a maximum parallelism higher that 128 will typically result in slightly bigger
state snapshots from the keyed backends.
+### Set An Explicit Max Parallelism
-Notice that maximum parallelism must fulfill the following conditions:
+The max parallelism, set on a per-job and per-operator granularity, determines
the maximum parallelism to which a stateful operator can scale.
+There is currently **no way to change** the maximum parallelism of an operator
after a job has started without discarding that operators state.
+The reason maximum parallelism exists, versus allowing stateful operators to
be infinitely scalable, is that it has some impact on your application's
performance and state size.
+Flink has to maintain specific metadata for its ability to rescale state which
grows linearly with max parallelism.
+In general, you should choose max parallelism that is high enough to fit your
future needs in scalability, while keeping it low enough to maintain reasonable
performance.
-`0 < parallelism <= max parallelism <= 2^15`
+{% panel **Note:** Maximum parallelism must fulfill the following conditions:
`0 < parallelism <= max parallelism <= 2^15` %}
-You can set the maximum parallelism by `setMaxParallelism(int
maxparallelism)`. By default, Flink will choose the maximum
-parallelism as a function of the parallelism when the job is first started:
+You can explicitly set maximum parallelism by using `setMaxParallelism(int
maxparallelism)`.
+If no max parallelism is set Flink will decide using a function of the
operators parallelism when the job is first started:
- `128` : for all parallelism <= 128.
- `MIN(nextPowerOfTwo(parallelism + (parallelism / 2)), 2^15)` : for all
parallelism > 128.
-### Set UUIDs for operators
+### Set UUIDs For All Operators
-As mentioned in the documentation for [savepoints]({{ site.baseurl
}}/ops/state/savepoints.html), users should set uids for
-operators. Those operator uids are important for Flink's mapping of operator
states to operators which, in turn, is
-essential for savepoints. By default operator uids are generated by traversing
the JobGraph and hashing certain operator
-properties. While this is comfortable from a user perspective, it is also very
fragile, as changes to the JobGraph (e.g.
-exchanging an operator) will result in new UUIDs. To establish a stable
mapping, we need stable operator uids provided
-by the user through `setUid(String uid)`.
+As mentioned in the documentation for [savepoints]({{ site.baseurl
}}/ops/state/savepoints.html), users should set uids for each operator in their
`DataStream`.
+Uids are necessary for Flink's mapping of operator states to operators which,
in turn, is essential for savepoints.
+By default, operator uids are generated by traversing the JobGraph and hashing
specific operator properties.
+While this is comfortable from a user perspective, it is also very fragile, as
changes to the JobGraph (e.g., exchanging an operator) results in new UUIDs.
+To establish a stable mapping, we need stable operator uids provided by the
user through `setUid(String uid)`.
-### Choice of state backend
+### Choose The Right State Backend
-Currently, Flink has the limitation that it can only restore the state from a
savepoint for the same state backend that
-took the savepoint. For example, this means that we can not take a savepoint
with a memory state backend, then change
-the job to use a RocksDB state backend and restore. While we are planning to
make backends interoperable in the near
-future, they are not yet. This means you should carefully consider which
backend you use for your job before going to
-production.
+Currently, Flink's savepoint binary format is state backend specific.
+A savepoint taken with one state backend cannot be restored using another, and
you should carefully consider which backend you use before going to production.
-In general, we recommend using RocksDB because this is currently the only
state backend that supports large states (i.e.
-state that exceeds the available main memory) and asynchronous snapshots. From
our experience, asynchronous snapshots are
-very important for large states because they do not block the operators and
Flink can write the snapshots without stopping
-stream processing. However, RocksDB can have worse performance than, for
example, the memory-based state backends. If
-you are sure that your state will never exceed main memory and blocking the
stream processing to write it is not an issue,
-you **could consider** to not use the RocksDB backends. However, at this
point, we **strongly recommend** using RocksDB
-for production.
+In general, we recommend avoiding `MemoryStateBackend` in production because
it stores its snapshots inside the JobManager as opposed to persistent disk.
+When deciding between `FsStateBackend` and `RocksDB`, it is a choice between
performance and scalability.
+`FsStateBackend` is very fast as each state access and update operates on
objects on the Java heap; however, state size is limited by available memory
within the cluster.
+On the other hand, `RocksDB` can scale based on available disk space and is
the only state backend to support incremental snapshots.
+However, each state access and update requires (de-)serialization and
potentially reading from disk which leads to average performance that is an
order of magnitude slower than the memory state backends.
Review comment:
I think it should be clear from the context but this sentence could probably
be worded better :)
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