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The following commit(s) were added to refs/heads/asf-site by this push:
new 1efbf9b6b Announcement blogpost for the 1.16 release
1efbf9b6b is described below
commit 1efbf9b6b9a7827632783073c8706751cdfdcff3
Author: godfreyhe <[email protected]>
AuthorDate: Fri Sep 30 14:50:48 2022 +0800
Announcement blogpost for the 1.16 release
---
_config.yml | 45 ++--
_posts/2022-10-28-1.16-announcement.md | 403 +++++++++++++++++++++++++++++++++
content/q/gradle-quickstart.sh | 2 +-
content/q/sbt-quickstart.sh | 2 +-
img/rocksdb_rescaling_benchmark.png | Bin 0 -> 450221 bytes
q/gradle-quickstart.sh | 2 +-
q/quickstart-scala.sh | 2 +-
q/quickstart.sh | 2 +-
q/sbt-quickstart.sh | 2 +-
9 files changed, 431 insertions(+), 29 deletions(-)
diff --git a/_config.yml b/_config.yml
index 57cbc3441..a971ce731 100644
--- a/_config.yml
+++ b/_config.yml
@@ -9,8 +9,8 @@ url: https://flink.apache.org
DOCS_BASE_URL: https://nightlies.apache.org/flink/
-FLINK_VERSION_STABLE: 1.15.2
-FLINK_VERSION_STABLE_SHORT: "1.15"
+FLINK_VERSION_STABLE: 1.16.0
+FLINK_VERSION_STABLE_SHORT: "1.16"
FLINK_ISSUES_URL: https://issues.apache.org/jira/browse/FLINK
FLINK_GITHUB_URL: https://github.com/apache/flink
@@ -73,6 +73,22 @@ FLINK_TABLE_STORE_GITHUB_REPO_NAME: flink-table-store
# md1_url:
https://repo.maven.apache.org/maven2/org/apache/flink/flink-metrics-prometheus_2.12/1.7.1/flink-metrics-prometheus_2.12-1.7.1.jar.sha1
flink_releases:
+ - version_short: "1.16"
+ binary_release:
+ name: "Apache Flink 1.16.0"
+ scala_212:
+ id: "1160-download_212"
+ url:
"https://www.apache.org/dyn/closer.lua/flink/flink-1.16.0/flink-1.16.0-bin-scala_2.12.tgz";
+ asc_url:
"https://downloads.apache.org/flink/flink-1.16.0/flink-1.16.0-bin-scala_2.12.tgz.asc";
+ sha512_url:
"https://downloads.apache.org/flink/flink-1.16.0/flink-1.16.0-bin-scala_2.12.tgz.sha512";
+ source_release:
+ name: "Apache Flink 1.16.0"
+ id: "1160-download-source"
+ url:
"https://www.apache.org/dyn/closer.lua/flink/flink-1.16.0/flink-1.16.0-src.tgz";
+ asc_url:
"https://downloads.apache.org/flink/flink-1.16.0/flink-1.16.0-src.tgz.asc";
+ sha512_url:
"https://downloads.apache.org/flink/flink-1.16.0/flink-1.16.0-src.tgz.sha512";
+ release_notes_url:
"https://nightlies.apache.org/flink/flink-docs-release-1.16/release-notes/flink-1.16";
+
- version_short: "1.15"
binary_release:
name: "Apache Flink 1.15.2"
@@ -88,27 +104,6 @@ flink_releases:
asc_url:
"https://downloads.apache.org/flink/flink-1.15.2/flink-1.15.2-src.tgz.asc";
sha512_url:
"https://downloads.apache.org/flink/flink-1.15.2/flink-1.15.2-src.tgz.sha512";
release_notes_url:
"https://nightlies.apache.org/flink/flink-docs-release-1.15/release-notes/flink-1.15";
- -
- version_short: "1.14"
- binary_release:
- name: "Apache Flink 1.14.6"
- scala_211:
- id: "1146-download_211"
- url:
"https://www.apache.org/dyn/closer.lua/flink/flink-1.14.6/flink-1.14.6-bin-scala_2.11.tgz";
- asc_url:
"https://downloads.apache.org/flink/flink-1.14.6/flink-1.14.6-bin-scala_2.11.tgz.asc";
- sha512_url:
"https://downloads.apache.org/flink/flink-1.14.6/flink-1.14.6-bin-scala_2.11.tgz.sha512";
- scala_212:
- id: "1146-download_212"
- url:
"https://www.apache.org/dyn/closer.lua/flink/flink-1.14.6/flink-1.14.6-bin-scala_2.12.tgz";
- asc_url:
"https://downloads.apache.org/flink/flink-1.14.6/flink-1.14.6-bin-scala_2.12.tgz.asc";
- sha512_url:
"https://downloads.apache.org/flink/flink-1.14.6/flink-1.14.6-bin-scala_2.12.tgz.sha512";
- source_release:
- name: "Apache Flink 1.14.6"
- id: "1146-download-source"
- url:
"https://www.apache.org/dyn/closer.lua/flink/flink-1.14.6/flink-1.14.6-src.tgz";
- asc_url:
"https://downloads.apache.org/flink/flink-1.14.6/flink-1.14.6-src.tgz.asc";
- sha512_url:
"https://downloads.apache.org/flink/flink-1.14.6/flink-1.14.6-src.tgz.sha512";
- release_notes_url:
"https://nightlies.apache.org/flink/flink-docs-release-1.14/release-notes/flink-1.14";
flink_statefun_releases:
-
@@ -244,6 +239,10 @@ component_releases:
release_archive:
flink:
+ -
+ version_short: "1.16"
+ version_long: 1.16.0
+ release_date: 2022-10-28
-
version_short: "1.15"
version_long: 1.15.2
diff --git a/_posts/2022-10-28-1.16-announcement.md
b/_posts/2022-10-28-1.16-announcement.md
new file mode 100644
index 000000000..eff11a596
--- /dev/null
+++ b/_posts/2022-10-28-1.16-announcement.md
@@ -0,0 +1,403 @@
+---
+layout: post
+title: "Announcing the Release of Apache Flink 1.16"
+subtitle: ""
+date: 2022-10-28T08:00:00.000Z
+categories: news
+authors:
+- godfreyhe:
+ name: "Godfrey He"
+ twitter: "godfreyhe"
+
+---
+
+Apache Flink continues to grow at a rapid pace and is one of the most active
+communities in Apache. Flink 1.16 had over 230 contributors enthusiastically
participating,
+with 19 FLIPs and 900+ issues completed, bringing a lot of exciting features
to the community.
+
+Flink has become the leading role and factual standard of stream processing,
+and the concept of the unification of stream and batch data processing is
gradually gaining recognition
+and is being successfully implemented in more and more companies. Previously,
+the integrated stream and batch concept placed more emphasis on a unified API
and
+a unified computing framework. This year, based on this, Flink proposed
+the next development direction of [Flink-Streaming
Warehouse](https://www.alibabacloud.com/blog/more-than-computing-a-new-era-led-by-the-warehouse-architecture-of-apache-flink_598821)
(Streamhouse),
+which further upgraded the scope of stream-batch integration: it truly
completes not only
+the unified computation but also unified storage, thus realizing unified
real-time analysis.
+
+In 1.16, the Flink community has completed many improvements for both batch
and stream processing:
+
+- For batch processing, all-round improvements in ease of use, stability and
performance
+ have been completed. 1.16 is a milestone version of Flink batch processing
and an important
+ step towards maturity.
+ - Ease of use: with the introduction of SQL Gateway and full compatibility
with Hive Server2,
+ users can submit Flink SQL jobs and Hive SQL jobs very easily, and it is
also easy to
+ connect to the original Hive ecosystem.
+ - Functionality: Join hints let Flink SQL users manually specify join
strategies
+ to avoid unreasonable execution plans. The compatibility of Hive SQL has
reached 94%,
+ and users can migrate from Hive to Flink at a very low cost.
+ - Stability: Propose a speculative execution mechanism to reduce the long
tail sub-tasks of
+ a job and improve the stability. Improve HashJoin and introduce failure
rollback mechanism
+ to avoid join failure.
+ - Performance: Introduce dynamic partition pruning to reduce the Scan I/O
and improve join
+ processing for the star-schema queries. There is 30% improvement in the
TPC-DS benchmark.
+ We can use hybrid shuffle mode to improve resource usage and processing
performance.
+- For stream processing, there are a number of significant improvements:
+ - Changelog State Backend provides users with second or even millisecond
checkpoints to
+ dramatically improve the fault tolerance experience, while providing a
smaller end-to-end
+ latency experience for transactional Sink jobs.
+ - Lookup join is widely used in stream processing. Slow lookup speed, low
throughput and
+ delay update are resolved through common cache mechanism, asynchronous io
and retriable lookup.
+ These features are very useful, solving the pain points that users often
complain about,
+ and supporting richer scenarios.
+ - From the first day of the birth of Flink SQL, there were some
non-deterministic operations
+ that could cause incorrect results or exceptions, which caused great
distress to users.
+ In 1.16, we spent a lot of effort to solve most of the problems, and we
will continue to
+ improve in the future.
+
+With the further refinement of the integration of stream and batch, and the
continuous iteration of
+the Flink Table Store ([0.2 has been
released](https://flink.apache.org/news/2022/08/29/release-table-store-0.2.0.html)),
+the Flink community is pushing the Streaming warehouse from concept to reality
and maturity step by step.
+
+# Understanding Streaming Warehouses
+
+To be precise, a streaming warehouse is to make data warehouse streaming,
which allows the data
+for each layer in the whole warehouse to flow in real-time. The goal is to
realize
+a Streaming Service with end-to-end real-time performance through a unified
API and computing framework.
+Please refer to [the
article](https://www.alibabacloud.com/blog/more-than-computing-a-new-era-led-by-the-warehouse-architecture-of-apache-flink_598821)
+for more details.
+
+# Batch processing
+
+Flink is a unified stream batch processing engine, stream processing has
become the leading role
+thanks to our long-term investment. We’re also putting more effort to improve
batch processing
+to make it an excellent computing engine. This makes the overall experience of
stream batch
+unification smoother.
+
+## SQL Gateway
+
+The feedback from various channels indicates that [SQL
Gateway](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/dev/table/sql-gateway/overview/)
+is a highly anticipated feature for users, especially for batch users. This
function finally
+completed (see
[FLIP-91](https://cwiki.apache.org/confluence/display/FLINK/FLIP-91%3A+Support+SQL+Gateway)
for design)
+in 1.16. SQL Gateway is an extension and enhancement to SQL Client,
+supporting multi-tenancy and pluggable API protocols (Endpoints), solving the
problem that SQL Client
+can only serve a single user and cannot be integrated with external services
or components.
+Currently SQL Gateway has support for REST API and HiveServer2 Protocol and
users can link to
+SQL Gateway via cURL, Postman, HTTP clients in various programming languages
to submit stream jobs,
+batch jobs and even OLAP jobs. For HiveServer2 Endpoint, please refer to the
Hive Compatibility
+for more details.
+
+## Hive Compatibility
+
+To reduce the cost of migrating Hive to Flink, we introduce HiveServer2
Endpoint and
+Hive Syntax Improvements in this version:
+
+[The HiveServer2
Endpoint](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/dev/table/hive-compatibility/hiveserver2/)
+allows users to interact with SQL Gateway with Hive JDBC/Beeline and migrate
with Flink into
+the Hive ecosystem (DBeaver, Apache Superset, Apache DolphinScheduler, and
Apache Zeppelin).
+When users connect to the HiveServer2 endpoint, the SQL Gateway registers the
Hive Catalog,
+switches to Hive Dialect, and uses batch execution mode to execute jobs. With
these steps,
+users can have the same experience as HiveServer2.
+
+[Hive
syntax](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/dev/table/hive-compatibility/hive-dialect/overview/)
+is already the factual standard for big data processing. Flink has improved
compatibility
+with Hive syntax and added support for several Hive syntaxes commonly used in
production.
+Hive syntax compatibility can help users migrate existing Hive SQL tasks to
Flink,
+and it is convenient for users who are familiar with Hive syntax to use Hive
syntax to
+write SQL to query tables registered in Flink. The compatibility is measured
using
+the Hive qtest suite which contains more than 12K SQL cases. Until now, for
Hive 2.3,
+the compatibility has reached 94.1% for whole hive queries, and has reached
97.3%
+if ACID queries are excluded.
+
+## Join Hints for Flink SQL
+
+The join hint is a common solution in the industry to improve the shortcomings
of the optimizer
+by manually modifying the execution plans. Join is the most widely used
operator in batch jobs,
+and Flink supports a variety of join strategies. Missing statistics or a poor
cost model of
+the optimizer can lead to the wrong choice of a join strategy, which will
cause slow execution
+or even job failure. By specifying a [Join
Hint](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/dev/table/sql/queries/hints/#join-hints),
+the optimizer will choose the join strategy specified by the user whenever
possible.
+It could avoid various shortcomings of the optimizer and ensure the production
availability of
+the batch job.
+
+## Adaptive Hash Join
+
+For batch jobs, the hash join operators may fail if the input data is severely
skewed,
+it's a very bad experience for users. To solve this, we introduce adaptive
Hash-Join
+which can automatically fall back to Sort-Merge-Join once the Hash-Join fails
in runtime.
+This mechanism ensures that the Hash-Join is always successful and improves
the stability
+by gracefully degrading from a Hash-Join to a more robust Sort-Merge-Join.
+
+## Speculative Execution for Batch Job
+
+Speculative execution is introduced in Flink 1.16 to mitigate batch job
slowness
+which is caused by problematic nodes. A problematic node may have hardware
problems,
+accident I/O busy, or high CPU load. These problems may make the hosted tasks
run
+much slower than tasks on other nodes, and affect the overall execution time
of a batch job.
+
+When speculative execution is enabled, Flink will keep detecting slow tasks.
+Once slow tasks are detected, the nodes that the slow tasks locate in will be
identified
+as problematic nodes and get blocked via the blocklist mechanism
([FLIP-224](https://cwiki.apache.org/confluence/display/FLINK/FLIP-224%3A+Blocklist+Mechanism)).
+The scheduler will create new attempts for the slow tasks and deploy them to
nodes
+that are not blocked, while the existing attempts will keep running.
+The new attempts process the same input data and produce the same data as the
original attempt.
+Once any attempt finishes first, it will be admitted as the only finished
attempt of the task,
+and the remaining attempts of the task will be canceled.
+
+Most existing sources can work with speculative execution
([FLIP-245](https://cwiki.apache.org/confluence/display/FLINK/FLIP-245%3A+Source+Supports+Speculative+Execution+For+Batch+Job)).
+Only if a source uses `SourceEvent`, it must implement
`SupportsHandleExecutionAttemptSourceEvent` interface to support
+speculative execution. Sinks do not support speculative execution yet so that
+speculative execution will not happen on sinks at the moment.
+
+The Web UI & REST API are also improved
([FLIP-249](https://cwiki.apache.org/confluence/display/FLINK/FLIP-249%3A+Flink+Web+UI+Enhancement+for+Speculative+Execution))
+to display multiple concurrent attempts of tasks and blocked task managers.
+
+## Hybrid Shuffle Mode
+
+We have introduced a new [Hybrid
Shuffle](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/ops/batch/batch_shuffle)
+Mode for batch executions. It combines the advantages of blocking shuffle and
+pipelined shuffle (in streaming mode).
+ - Like blocking shuffle, it does not require upstream and downstream tasks to
run simultaneously,
+ which allows executing a job with little resources.
+ - Like pipelined shuffle, it does not require downstream tasks to be executed
+ after upstream tasks finish, which reduces the overall execution time of
the job when
+ given sufficient resources.
+ - It adapts to custom preferences between persisting less data and restarting
less tasks on failures,
+ by providing different spilling strategies.
+
+Note: This feature is experimental and by default not activated.
+
+## Further improvements of blocking shuffle
+
+We further improved the blocking shuffle usability and performance in this
version,
+including adaptive network buffer allocation, sequential IO optimization and
result partition reuse
+which allows multiple consumer vertices reusing the same physical result
partition to reduce disk IO
+and storage space. These optimizations can achieve an overall 7% performance
gain for
+the TPC-DS test with a scale of 10 TB. In addition, two more compression
algorithms (LZO and ZSTD)
+with higher compression ratio were introduced which can further reduce the
storage space with
+some CPU cost compared to the default LZ4 compression algorithm.
+
+## Dynamic Partition Pruning
+
+For batch jobs, partitioned tables are more widely used than non-partitioned
tables in
+production environments. Currently Flink has support for static partition
pruning,
+where the optimizer pushes down the partition field related filter conditions
in the WHERE clause
+into the Source Connector during the optimization phase, thus reducing
unnecessary partition scan IO.
+The [star-schema](https://en.wikipedia.org/wiki/Star_schema) is the simplest
of
+the most commonly used data mart patterns. We have found that many user jobs
cannot use
+static partition pruning because partition pruning information can only be
determined in execution,
+which requires [dynamic partition pruning
techniques](https://cwiki.apache.org/confluence/display/FLINK/FLIP-248%3A+Introduce+dynamic+partition+pruning),
+where partition pruning information is collected at runtime based on data from
other related tables,
+thus reducing unnecessary partition scan IO for partitioned tables. The use of
dynamic partition pruning
+has been validated with the 10 TB dataset TPC-DS to improve performance by up
to 30%.
+
+# Stream Processing
+
+In 1.16, we have made improvements in Checkpoints, SQL, Connector and other
fields,
+so that stream computing can continue to lead.
+
+## Generalized incremental checkpoint
+
+Changelog state backend aims at making checkpoint intervals shorter and more
predictable,
+this release is prod-ready and is dedicated to adapting changelog state
backend to
+the existing state backends and improving the usability of changelog state
backend:
+ - Support state migration
+ - Support local recovery
+ - Introduce file cache to optimize restoring
+ - Support switch based on checkpoint
+ - Improve the monitoring experience of changelog state backend
+ - expose changelog's metrics
+ - expose changelog's configuration to webUI
+
+Table 1: The comparison between Changelog Enabled / Changelog Disabled on
value state
+(see [this
blog](https://flink.apache.org/2022/05/30/changelog-state-backend.html) for
more details)
+
+| Percentile | End to End Duration | Checkpointed Data Size<sup>*<sup> | Full
Checkpoint Data Size<sup>*<sup> |
+|-------------|---------------------|-----------------------------------|--------------------------------------|
+| 50% | 311ms / 5s | 14.8MB / 3.05GB |
24.2GB / 18.5GB |
+| 90% | 664ms / 6s | 23.5MB / 4.52GB |
25.2GB / 19.3GB |
+| 99% | 1s / 7s | 36.6MB / 5.19GB |
25.6GB / 19.6GB |
+| 99.9% | 1s / 10s | 52.8MB / 6.49GB |
25.7GB / 19.8GB |
+
+## RocksDB rescaling improvement & rescaling benchmark
+
+Rescaling is a frequent operation for cloud services built on Apache Flink,
this release leverages
+[deleteRange](https://rocksdb.org/blog/2018/11/21/delete-range.html) to
optimize the rescaling of
+Incremental RocksDB state backend. deleteRange is used to avoid massive
scan-and-delete operations,
+for upscaling with a large number of states that need to be deleted,
+the speed of restoring can be almost [increased by 2~10
times](https://github.com/apache/flink/pull/19033#issuecomment-1072267658).
+
+<center>
+<img src="{{ site.baseurl }}/img/rocksdb_rescaling_benchmark.png"
style="width:90%;margin:15px">
+</center>
+
+## Improve monitoring experience and usability of state backend
+
+This release also improves the monitoring experience and usability of state
backend.
+Previously, RocksDB's log was located in its own DB folder, which made
debugging RocksDB not so easy.
+This release lets RocksDB's log stay in Flink's log directory by default.
RocksDB statistics-based
+metrics are introduced to help debug the performance at the database level,
e.g. total block cache hit/miss
+count within the DB.
+
+## Support overdraft buffer
+
+A new concept of [overdraft network
buffer](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/deployment/memory/network_mem_tuning/#overdraft-buffers)
+is introduced to mitigate the effects of uninterruptible blocking a subtask
thread during back pressure,
+which can be turned on through the
[taskmanager.network.memory.max-overdraft-buffers-per-gate](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/deployment/config/#taskmanager-network-memory-max-overdraft-buffers-per-gate)
+configuration parameter.
+Starting from 1.16.0, Flink subtask can request by default up to 5 extra
(overdraft) buffers over
+the regular configured amount. This change can slightly increase memory
consumption of
+the Flink Job but vastly reduce the checkpoint duration of the unaligned
checkpoint.
+If the subtask is back pressured by downstream subtasks and the subtask
requires more than
+a single network buffer to finish what it's currently doing, overdraft buffer
comes into play.
+Read more about this in [the
documentation](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/deployment/memory/network_mem_tuning/#overdraft-buffers).
+
+## Timeout aligned to unaligned checkpoint barrier in the output buffers of an
upstream subtask
+
+This release updates the timing of switching from Aligned Checkpoint (AC) to
Unaligned Checkpoint (UC).
+With UC enabled, if
[execution.checkpointing.aligned-checkpoint-timeout](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/ops/state/checkpointing_under_backpressure/#aligned-checkpoint-timeout)
+is configured, each checkpoint will still begin as an AC, but when the global
checkpoint duration
+exceeds the aligned-checkpoint-timeout, if the AC has not been completed,
+then the checkpoint will be switched to unaligned.
+
+Previously, the switch of one subtask needs to wait for all barriers from
upstream.
+If the back pressure is severe, the downstream subtask may not receive all
barriers
+within
[checkpointing-timeout](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/deployment/config/#execution-checkpointing-timeout),
+causing the checkpoint to fail.
+
+In this release, if the barrier cannot be sent from the output buffer to the
downstream task
+within the `execution.checkpointing.aligned-checkpoint-timeout`, Flink lets
upstream subtasks
+switch to UC first to send barriers to downstream, thereby decreasing
+the probability of checkpoint timeout during back pressure.
+More details can be found in [this
documentation](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/ops/state/checkpointing_under_backpressure/#aligned-checkpoint-timeout).
+
+## Non-Determinism In Stream Processing
+
+Users often complain about the high cost of understanding stream processing.
+One of the pain points is the non-determinisim in stream processing (usually
not intuitive)
+which may cause wrong results or errors. These pain points have been around
since the
+early days of Flink SQL.
+
+For complex streaming jobs, now it's possible to detect and resolve potential
correctness issues
+before running. If the problems can’t be resolved completely, a detailed
message could prompt users to
+adjust the SQL so as to avoid introducing non-deterministic problems. More
details can be found
+in [the
documentation](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/dev/table/concepts/determinism).
+
+## Enhanced Lookup Join
+
+Lookup join is widely used in stream processing, and we have introduced
several improvements:
+- Adds a unified abstraction for lookup source cache and
+ [related
metrics](https://cwiki.apache.org/confluence/display/FLINK/FLIP-221%3A+Abstraction+for+lookup+source+cache+and+metric)
+ to speed up lookup queries
+- Introduces the configurable asynchronous mode (ALLOW_UNORDERED) via
+ [job
configuration](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/dev/table/config/#table-exec-async-lookup-output-mode)
+ or [lookup
hint](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/dev/table/sql/queries/hints/#lookup)
+ to significantly improve query throughput without compromising correctness.
+- [Retryable lookup
mechanism](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/hints/#3-enable-delayed-retry-strategy-for-lookup)
+ gives users more tools to solve the delayed updates issue in external
systems.
+
+## Retry Support For Async I/O
+
+Introduces a built-in retry mechanism for [asynchronous
I/O](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/dev/datastream/operators/asyncio/#retry-support)
+that is transparent to the user's existing code, allowing flexibility to meet
+the user's retry and exception handling needs.
+
+# PyFlink
+
+In Flink 1.15, we have introduced a new execution mode 'thread' mode in
+which the user-defined Python functions will be executed in the JVM via JNI
instead of
+in a separate Python process. However, it's only supported for Python scalar
functions in
+the Table API & SQL in Flink 1.15. In this release, we have provided more
comprehensive support for it.
+It has also been supported in the Python DataStream API and also Python table
functions
+in the Table API & SQL.
+
+We are also continuing to fill in the last few missing features in Python API.
In this release,
+we have provided more comprehensive support for Python DataStream API and
supported features
+such as side output, broadcast state, etc and have also finalized the
windowing support.
+We have also added support for more connectors and formats in the Python
DataStream API,
+e.g. added support for connectors elasticsearch, kinesis, pulsar, hybrid
source, etc and
+formats orc, parquet, etc. With all these features added, the Python API
should have aligned
+most notable features in the Java & Scala API and users should be able to
develop most
+kinds of Flink jobs using Python language smoothly.
+
+# Others
+
+## New SQL Syntax
+
+In 1.16, we extend more DDL syntaxes which could help users to better use SQL:
+- [USING
JAR](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/dev/table/sql/create/#create-function)
+ supports dynamic loading of UDF jar to help platform developers to easily
manage UDF.
+- [CREATE TABLE AS
SELECT](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/dev/table/sql/create/#as-select_statement)
(CTAS)
+ supports users to create new tables based on existing tables and queries.
+- [ANALYZE
TABLE](https://nightlies.apache.org/flink/flink-docs-release-1.16/zh/docs/dev/table/sql/analyze)
+ supports users to manually generate table statistics so that the optimizer
could
+ generate better execution plans.
+
+## Cache in DataStream for Interactive Programming
+
+Supports caching the result of a transformation via
[DataStream#cache](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/dev/datastream/operators/overview/#cache).
+The cached intermediate result is generated lazily at the first time the
intermediate result is
+computed so that the result can be reused by later jobs. If the cache is lost,
+it will be recomputed using the original transformations. Currently only batch
mode is supported.
+This feature is very useful for ML and interactive programming in Python.
+
+## History Server & Completed Jobs Information Enhancement
+
+We have enhanced the experiences of viewing completed jobs’ information in
this release.
+- JobManager / HistoryServer WebUI now provides detailed execution time
metrics,
+ including duration tasks spent in each execution state and the accumulated
+ busy / idle / back-pressured time during running.
+- JobManager / HistoryServer WebUI now provides aggregation of major SubTask
metrics,
+ grouped by Task or TaskManager.
+- JobManager / HistoryServer WebUI now provides more environmental
information,
+ including environment variables, JVM options and classpath.
+- HistoryServer now supports browsing logs[6] from external log archiving
services.
+
+## Protobuf format
+
+Flink now supports the Protocol Buffers (Protobuf) format. This allows you to
use this format directly
+in your Table API or SQL applications.
+
+## Introduce configurable RateLimitingStrategy for Async Sink
+
+The Async Sink was implemented in 1.15 to allow users to easily implement
+their own custom asynchronous sinks. We have now extended it to support
+a configurable RateLimitingStrategy. This means sink implementers can now
customize
+how their Async Sink behaves when requests fail, depending on the specific
sink.
+If no RateLimitingStrategy is specified, it will default to the current
default of AIMDRateLimitingStrategy.
+
+# Upgrade Notes
+
+We aim to make upgrades as smooth as possible, but some of the changes require
users to
+adjust some parts of the program when upgrading to Apache Flink 1.16. Please
take a look at
+the release notes for a list of adjustments to make and issues to check during
upgrades.
+
+# List of Contributors
+
+The Apache Flink community would like to thank each one of the contributors
that have made this release possible:
+
+1996fanrui, Ada Wang, Ada Wong, Ahmed Hamdy, Aitozi, Alexander Fedulov,
Alexander Preuß, Alexander Trushev,
+Andriy Redko, Anton Kalashnikov, Arvid Heise, Ben Augarten, Benchao Li,
BiGsuw, Biao Geng, Bobby Richard,
+Brayno, CPS794, Cheng Pan, Chengkai Yang, Chesnay Schepler, Danny Cranmer,
David N Perkins, Dawid Wysakowicz,
+Dian Fu, DingGeGe, EchoLee5, Etienne Chauchot, Fabian Paul, Ferenc Csaky,
Francesco Guardiani, Gabor Somogyi,
+Gen Luo, Gyula Fora, Haizhou Zhao, Hangxiang Yu, Hao Wang, Hong Liang Teoh,
Hong Teoh, Hongbo Miao, HuangXingBo,
+Ingo Bürk, Jacky Lau, Jane Chan, Jark Wu, Jay Li, Jia Liu, Jie Wang, Jin, Jing
Ge, Jing Zhang, Jingsong Lee,
+Jinhu Wu, Joe Moser, Joey Pereira, Jun He, JunRuiLee, Juntao Hu, JustDoDT, Kai
Chen, Krzysztof Chmielewski,
+Krzysztof Dziolak, Kyle Dong, LeoZhang, Levani Kokhreidze, Lihe Ma, Lijie
Wang, Liu Jiangang, Luning Wang,
+Marios Trivyzas, Martijn Visser, MartijnVisser, Mason Chen, Matthias Pohl,
Metehan Yıldırım, Michael, Mingde Peng,
+Mingliang Liu, Mulavar, Márton Balassi, Nie yingping, Niklas Semmler, Paul
Lam, Paul Lin, Paul Zhang, PengYuan,
+Piotr Nowojski, Qingsheng Ren, Qishang Zhong, Ran Tao, Robert Metzger, Roc
Marshal, Roman Boyko, Roman Khachatryan,
+Ron, Ron Cohen, Ruanshubin, Rudi Kershaw, Rufus Refactor, Ryan Skraba,
Sebastian Mattheis, Sergey, Sergey Nuyanzin,
+Shengkai, Shubham Bansal, SmirAlex, Smirnov Alexander, SteNicholas, Steven van
Rossum, Suhan Mao, Tan Yuxin,
+Tartarus0zm, TennyZhuang, Terry Wang, Thesharing, Thomas Weise, Timo Walther,
Tom, Tony Wei, Weijie Guo, Wencong Liu,
+WencongLiu, Xintong Song, Xuyang, Yangze Guo, Yi Tang, Yu Chen, Yuan Huang,
Yubin Li, Yufan Sheng, Yufei Zhang,
+Yun Gao, Yun Tang, Yuxin Tan, Zakelly, Zhanghao Chen, Zhu Zhu, Zichen Liu,
Zili Sun, acquachen, bgeng777, billyrrr,
+bzhao, caoyu, chenlei677, chenzihao, chenzihao5, coderap, cphe, davidliu,
dependabot[bot], dkkb, dusukang, empcl,
+eyys, fanrui, fengjiankun, fengli, fredia, gabor.g.somogyi, godfreyhe,
gongzhongqiang, harker2015, hongli,
+huangxingbo, huweihua, jayce, jaydonzhou, jiabao.sun, kevin.cyj, kurt, lidefu,
lijiewang.wlj, liliwei, lincoln lee,
+lincoln.lil, littleeleventhwolf, liufangqi, liujia10, liujiangang, liujingmao,
liuyongvs, liuzhuang2017, longwang,
+lovewin99, luoyuxia, mans2singh, maosuhan, mayue.fight, mayuehappy,
nieyingping, pengmide, pengmingde, polaris6,
+pvary, qinjunjerry, realdengziqi, root, shammon, shihong90, shuiqiangchen,
slinkydeveloper, snailHumming, snuyanzin,
+suxinglee, sxnan, tison, trushev, tsreaper, unknown, wangfeifan, wangyang0918,
wangzhiwu, wenbingshen, xiangqiao123,
+xuyang, yangjf2019, yangjunhan, yangsanity, yangxin, ylchou, yuchengxin,
yunfengzhou-hub, yuxia Luo, yuzelin,
+zhangchaoming, zhangjingcun, zhangmang, zhangzhengqi3, zhaoweinan,
zhengyunhong.zyh, zhenyu xing, zhouli,
+zhuanshenbsj1, zhuzhu.zz, zoucao, zp, 周磊, 饶紫轩,, 鲍健昕 愚鲤, 帝国阿三
\ No newline at end of file
diff --git a/content/q/gradle-quickstart.sh b/content/q/gradle-quickstart.sh
index 2bd860919..b6e2f6502 100755
--- a/content/q/gradle-quickstart.sh
+++ b/content/q/gradle-quickstart.sh
@@ -41,7 +41,7 @@ function mkPackage() {
defaultProjectName="quickstart"
defaultOrganization="org.myorg.quickstart"
defaultVersion="0.1-SNAPSHOT"
-defaultFlinkVersion="${1:-1.15.2}"
+defaultFlinkVersion="${1:-1.16.0}"
# flink-docs-master/docs/dev/datastream/project-configuration/#gradle
# passes the scala version prefixed with a _, e.g.: _2.12
scalaBinaryVersionFromCmdArg="${2/_/}"
diff --git a/content/q/sbt-quickstart.sh b/content/q/sbt-quickstart.sh
index b92a7639c..b9e8a1013 100755
--- a/content/q/sbt-quickstart.sh
+++ b/content/q/sbt-quickstart.sh
@@ -42,7 +42,7 @@ defaultProjectName="Flink Project"
defaultOrganization="org.example"
defaultVersion="0.1-SNAPSHOT"
defaultScalaVersion="2.12.7"
-defaultFlinkVersion="1.15.2"
+defaultFlinkVersion="1.16.0"
echo "This script creates a Flink project using Scala and SBT."
diff --git a/img/rocksdb_rescaling_benchmark.png
b/img/rocksdb_rescaling_benchmark.png
new file mode 100644
index 000000000..616aa8e6d
Binary files /dev/null and b/img/rocksdb_rescaling_benchmark.png differ
diff --git a/q/gradle-quickstart.sh b/q/gradle-quickstart.sh
index 2bd860919..b6e2f6502 100755
--- a/q/gradle-quickstart.sh
+++ b/q/gradle-quickstart.sh
@@ -41,7 +41,7 @@ function mkPackage() {
defaultProjectName="quickstart"
defaultOrganization="org.myorg.quickstart"
defaultVersion="0.1-SNAPSHOT"
-defaultFlinkVersion="${1:-1.15.2}"
+defaultFlinkVersion="${1:-1.16.0}"
# flink-docs-master/docs/dev/datastream/project-configuration/#gradle
# passes the scala version prefixed with a _, e.g.: _2.12
scalaBinaryVersionFromCmdArg="${2/_/}"
diff --git a/q/quickstart-scala.sh b/q/quickstart-scala.sh
index 7be5b47b5..924fdf2df 100755
--- a/q/quickstart-scala.sh
+++ b/q/quickstart-scala.sh
@@ -24,7 +24,7 @@ PACKAGE=quickstart
mvn archetype:generate
\
-DarchetypeGroupId=org.apache.flink \
-DarchetypeArtifactId=flink-quickstart-scala \
- -DarchetypeVersion=${1:-1.15.2}
\
+ -DarchetypeVersion=${1:-1.16.0}
\
-DgroupId=org.myorg.quickstart \
-DartifactId=$PACKAGE
\
-Dversion=0.1
\
diff --git a/q/quickstart.sh b/q/quickstart.sh
index dae94f831..8a4abe060 100755
--- a/q/quickstart.sh
+++ b/q/quickstart.sh
@@ -24,7 +24,7 @@ PACKAGE=quickstart
mvn archetype:generate
\
-DarchetypeGroupId=org.apache.flink \
-DarchetypeArtifactId=flink-quickstart-java \
- -DarchetypeVersion=${1:-1.15.2}
\
+ -DarchetypeVersion=${1:-1.16.0}
\
-DgroupId=org.myorg.quickstart \
-DartifactId=$PACKAGE
\
-Dversion=0.1
\
diff --git a/q/sbt-quickstart.sh b/q/sbt-quickstart.sh
index b92a7639c..b9e8a1013 100755
--- a/q/sbt-quickstart.sh
+++ b/q/sbt-quickstart.sh
@@ -42,7 +42,7 @@ defaultProjectName="Flink Project"
defaultOrganization="org.example"
defaultVersion="0.1-SNAPSHOT"
defaultScalaVersion="2.12.7"
-defaultFlinkVersion="1.15.2"
+defaultFlinkVersion="1.16.0"
echo "This script creates a Flink project using Scala and SBT."
