This is an automated email from the ASF dual-hosted git repository.
martijnvisser pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/flink-web.git
commit 6e8fb6ece4ca9cfe5dc0adab93352d1654ed195c
Author: MartijnVisser <martijnvis...@apache.org>
AuthorDate: Tue Apr 19 10:11:53 2022 +0200
Rebuild website
---
content/img/flink_feature_radar_3.svg | 4 +
content/img/flink_feature_radar_zh_3.svg | 4 +
content/q/gradle-quickstart.sh | 71 ++++----
content/roadmap.html | 173 +++++--------------
content/zh/roadmap.html | 286 +++++++++++--------------------
5 files changed, 186 insertions(+), 352 deletions(-)
diff --git a/content/img/flink_feature_radar_3.svg
b/content/img/flink_feature_radar_3.svg
new file mode 100644
index 000000000..08e5e4ad8
--- /dev/null
+++ b/content/img/flink_feature_radar_3.svg
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!-- Do not edit this file with editors other than diagrams.net -->
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd";>
+<svg xmlns="http://www.w3.org/2000/svg";
xmlns:xlink="http://www.w3.org/1999/xlink"; version="1.1" width="1461px"
height="1611px" viewBox="-0.5 -0.5 1461 1611" content="<mxfile
host="app.diagrams.net" modified="2022-04-19T07:50:28.330Z"
agent="5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15
(KHTML, like Gecko) Version/15.3 Safari/605.1.15"
etag="4SBsAjeUeQcoES03DVG8" version="17.4.3"
type="device"><diagram id [...]
\ No newline at end of file
diff --git a/content/img/flink_feature_radar_zh_3.svg
b/content/img/flink_feature_radar_zh_3.svg
new file mode 100644
index 000000000..08e5e4ad8
--- /dev/null
+++ b/content/img/flink_feature_radar_zh_3.svg
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!-- Do not edit this file with editors other than diagrams.net -->
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd";>
+<svg xmlns="http://www.w3.org/2000/svg";
xmlns:xlink="http://www.w3.org/1999/xlink"; version="1.1" width="1461px"
height="1611px" viewBox="-0.5 -0.5 1461 1611" content="<mxfile
host="app.diagrams.net" modified="2022-04-19T07:50:28.330Z"
agent="5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15
(KHTML, like Gecko) Version/15.3 Safari/605.1.15"
etag="4SBsAjeUeQcoES03DVG8" version="17.4.3"
type="device"><diagram id [...]
\ No newline at end of file
diff --git a/content/q/gradle-quickstart.sh b/content/q/gradle-quickstart.sh
index b977c69e2..bb17a40ab 100755
--- a/content/q/gradle-quickstart.sh
+++ b/content/q/gradle-quickstart.sh
@@ -42,7 +42,10 @@ defaultProjectName="quickstart"
defaultOrganization="org.myorg.quickstart"
defaultVersion="0.1-SNAPSHOT"
defaultFlinkVersion="${1:-1.14.4}"
-defaultScalaBinaryVersion="${2:-2.11}"
+# flink-docs-master/docs/dev/datastream/project-configuration/#gradle
+# passes the scala version prefixed with a _, e.g.: _2.12
+scalaBinaryVersionFromCmdArg="${2/_/}"
+defaultScalaBinaryVersion="${scalaBinaryVersionFromCmdArg:-2.12}"
echo "This script creates a Flink project using Java and Gradle."
@@ -100,53 +103,49 @@ rootProject.name = '${projectName}'
EOF
cat > build.gradle <<EOF
-buildscript {
- repositories {
- jcenter() // this applies only to the Gradle 'Shadow' plugin
- }
- dependencies {
- classpath 'com.github.jengelman.gradle.plugins:shadow:2.0.4'
- }
-}
-
plugins {
id 'java'
id 'application'
// shadow plugin to produce fat JARs
- id 'com.github.johnrengelman.shadow' version '2.0.4'
+ id 'com.github.johnrengelman.shadow' version '7.1.2'
}
-
-// artifact properties
-group = '${organization}'
-version = '${version}'
-mainClassName = '${organization}.StreamingJob'
-description = """Flink Quickstart Job"""
-
ext {
javaVersion = '1.8'
flinkVersion = '${flinkVersion}'
scalaBinaryVersion = '${scalaBinaryVersion}'
- slf4jVersion = '1.7.7'
- log4jVersion = '1.2.17'
+ slf4jVersion = '1.7.32'
+ log4jVersion = '2.17.1'
+ flinkVersionNew = flinkVersion.toString().replace("-SNAPSHOT", "") >=
"1.15"
}
+// artifact properties
+group = '${organization}'
+version = '${version}'
+if (flinkVersionNew) {
+ mainClassName = '${organization}.DataStreamJob'
+} else {
+ mainClassName = '${organization}.StreamingJob'
+}
+description = """Flink Quickstart Job"""
sourceCompatibility = javaVersion
targetCompatibility = javaVersion
tasks.withType(JavaCompile) {
- options.encoding = 'UTF-8'
+ options.encoding = 'UTF-8'
}
-applicationDefaultJvmArgs = ["-Dlog4j.configuration=log4j.properties"]
-
-task wrapper(type: Wrapper) {
- gradleVersion = '3.1'
-}
+applicationDefaultJvmArgs = ["-Dlog4j.configurationFile=log4j2.properties"]
// declare where to find the dependencies of your project
repositories {
- mavenCentral()$( if [[ "${flinkVersion}" == *-SNAPSHOT ]] ; then echo -e
"\n maven { url
\"https://repository.apache.org/content/repositories/snapshots/\"; }" ; else
echo ""; fi )
+ mavenCentral()
+ maven {
+ url "https://repository.apache.org/content/repositories/snapshots";
+ mavenContent {
+ snapshotsOnly()
+ }
+ }
}
// NOTE: We cannot use "compileOnly" or "shadow" configurations since then we
could not run code
@@ -160,7 +159,7 @@ configurations {
flinkShadowJar.exclude group: 'org.apache.flink', module: 'force-shading'
flinkShadowJar.exclude group: 'com.google.code.findbugs', module: 'jsr305'
flinkShadowJar.exclude group: 'org.slf4j'
- flinkShadowJar.exclude group: 'log4j'
+ flinkShadowJar.exclude group: 'org.apache.logging.log4j'
}
// declare the dependencies for your production and test code
@@ -169,17 +168,23 @@ dependencies {
// Compile-time dependencies that should NOT be part of the
// shadow jar and are provided in the lib folder of Flink
// --------------------------------------------------------------
- compile "org.apache.flink:flink-java:\${flinkVersion}"
- compile
"org.apache.flink:flink-streaming-java_\${scalaBinaryVersion}:\${flinkVersion}"
+ if (flinkVersionNew) {
+ implementation "org.apache.flink:flink-streaming-java:\${flinkVersion}"
+ implementation "org.apache.flink:flink-clients:\${flinkVersion}"
+ } else {
+ implementation
"org.apache.flink:flink-streaming-java_\${scalaBinaryVersion}:\${flinkVersion}"
+ implementation
"org.apache.flink:flink-clients_\${scalaBinaryVersion}:\${flinkVersion}"
+ }
// --------------------------------------------------------------
// Dependencies that should be part of the shadow jar, e.g.
// connectors. These must be in the flinkShadowJar configuration!
// --------------------------------------------------------------
- //flinkShadowJar
"org.apache.flink:flink-connector-kafka-0.11_\${scalaBinaryVersion}:\${flinkVersion}"
+ //flinkShadowJar "org.apache.flink:flink-connector-kafka:\${flinkVersion}"
- compile "log4j:log4j:\${log4jVersion}"
- compile "org.slf4j:slf4j-log4j12:\${slf4jVersion}"
+ runtimeOnly "org.apache.logging.log4j:log4j-slf4j-impl:\${log4jVersion}"
+ runtimeOnly "org.apache.logging.log4j:log4j-api:\${log4jVersion}"
+ runtimeOnly "org.apache.logging.log4j:log4j-core:\${log4jVersion}"
// Add test dependencies here.
// testCompile "junit:junit:4.12"
diff --git a/content/roadmap.html b/content/roadmap.html
index 1b7e7786c..7dd8314b8 100644
--- a/content/roadmap.html
+++ b/content/roadmap.html
@@ -258,18 +258,15 @@ under the License.
<li><a
href="#unified-analytics-where-batch-and-streaming-come-together-sql-and-beyond"
id="markdown-toc-unified-analytics-where-batch-and-streaming-come-together-sql-and-beyond">Unified
Analytics: Where Batch and Streaming come Together; SQL and beyond.</a> <ul>
<li><a href="#a-unified-sql-platform"
id="markdown-toc-a-unified-sql-platform">A unified SQL Platform</a></li>
<li><a href="#deep-batch--streaming-unification-for-the-datastream-api"
id="markdown-toc-deep-batch--streaming-unification-for-the-datastream-api">Deep
Batch / Streaming Unification for the DataStream API</a></li>
- <li><a href="#subsuming-dataset-with-datastream-and-table-api"
id="markdown-toc-subsuming-dataset-with-datastream-and-table-api">Subsuming
DataSet with DataStream and Table API</a></li>
</ul>
</li>
<li><a href="#applications-vs-clusters-flink-as-a-library"
id="markdown-toc-applications-vs-clusters-flink-as-a-library">Applications vs.
Clusters; “Flink as a Library”</a></li>
<li><a href="#performance" id="markdown-toc-performance">Performance</a>
<ul>
<li><a href="#faster-checkpoints-and-recovery"
id="markdown-toc-faster-checkpoints-and-recovery">Faster Checkpoints and
Recovery</a></li>
- <li><a href="#large-scale-batch-applications"
id="markdown-toc-large-scale-batch-applications">Large Scale Batch
Applications</a></li>
</ul>
</li>
- <li><a href="#python-apis" id="markdown-toc-python-apis">Python APIs</a></li>
+ <li><a href="#apache-flink-as-part-of-an-ever-evolving-data-ecosystem"
id="markdown-toc-apache-flink-as-part-of-an-ever-evolving-data-ecosystem">Apache
Flink as part of an ever evolving data ecosystem</a></li>
<li><a href="#documentation"
id="markdown-toc-documentation">Documentation</a></li>
- <li><a href="#miscellaneous-operational-tools"
id="markdown-toc-miscellaneous-operational-tools">Miscellaneous Operational
Tools</a></li>
<li><a href="#stateful-functions"
id="markdown-toc-stateful-functions">Stateful Functions</a></li>
</ul>
@@ -298,7 +295,7 @@ are approaching end-of-life. For questions, please contact
the developer mailing
<a
href="mailto:dev@flink.apache.org">dev@flink.apache.org</a></p>
<div class="row front-graphic">
- <img src="/img/flink_feature_radar_2.svg" width="700px" />
+ <img src="/img/flink_feature_radar_3.svg" width="700px" />
</div>
<h2 id="feature-stages">Feature Stages</h2>
@@ -335,39 +332,34 @@ platform, and is continuing to do so.</p>
and as continuous queries. Flink already contains an efficient unified query
engine, and a wide set of
integrations. With user feedback, those are continuously improved.</p>
-<p><strong>More Connector and Change Data Capture Support</strong></p>
+<p><strong>Going Beyond a SQL Stream/Batch Processing Engine</strong></p>
<ul>
- <li>Change-Data-Capture: Capturing a stream of data changes, directly from
databases, by attaching to the
-transaction log. The community is adding more CDC intrgrations.
- <ul>
- <li>External CDC connectors: <a
href="https://flink-packages.org/packages/cdc-connectors";>https://flink-packages.org/packages/cdc-connectors</a></li>
- <li>Background: <a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=147427289";>FLIP-105</a>
-(CDC support for SQL) and <a href="https://debezium.io/";>Debezium</a>.</li>
- </ul>
- </li>
- <li>Data Lake Connectors: Unified streaming & batch is a powerful value
proposition for Data Lakes: supporting
-same APIs, semantics, and engine for streaming real-time processing and batch
processing of historic data.
-The community is adding deeper integrations with various Data Lake systems:
- <ul>
- <li><a href="https://iceberg.apache.org/";>Apache Iceberg</a>: <a
href="https://iceberg.apache.org/flink/";>https://iceberg.apache.org/flink/</a></li>
- <li><a href="https://hudi.apache.org/";>Apache Hudi</a>: <a
href="https://hudi.apache.org/blog/apache-hudi-meets-apache-flink/";>https://hudi.apache.org/blog/apache-hudi-meets-apache-flink/</a></li>
- <li><a href="https://pinot.apache.org/";>Apache Pinot</a>: <a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=177045634";>FLIP-166</a></li>
- </ul>
- </li>
+ <li>To extend the capability of a pure stream processor and make Flink ready
for future use cases,
+<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-188%3A+Introduce+Built-in+Dynamic+Table+Storage";>FLIP-188</a>
+has been announced adding built in dynamic table storage.</li>
+ <li>The experience of updating Flink SQL based jobs has been rather
cumbersome as it could have
+lead to new job graphs making restoring from savepoints/checkpoints
impossible.
+<a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=191336489&src=contextnavpagetreemode";>FLIP-190</a>
+that already has been shipped as MVP is targeting this.</li>
</ul>
<p><strong>Platform Infrastructure</strong></p>
<ul>
- <li>To simplify the building of production SQL platforms with Flink, we are
improving the SQL client and are
-working on SQL gateway components that interface between client and cluster:
<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-163%3A+SQL+Client+Improvements";>FLIP-163</a></li>
+ <li>After <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-163%3A+SQL+Client+Improvements";>FLIP-163</a>
+the community is working again on a set of SQL Client usability improvements
+(<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-189%3A+SQL+Client+Usability+Improvements";>FLIP-189</a>)
+which is aiming at improving the user experience, when using the SQL
client.</li>
</ul>
<p><strong>Support for Common Languages, Formats, Catalogs</strong></p>
<ul>
- <li>Hive Query Compatibility: <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-152%3A+Hive+Query+Syntax+Compatibility";>FLIP-152</a></li>
+ <li>With <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-216%3A++Introduce+pluggable+dialect+and++decouple+Hive+connector";>FLIP-216</a>
+there’s now the initiative to introduce pluggable dialects on the example of
the Hive connector.
+Including so many dependencies to make dialects work has lead to an overhead
for contributors
+and users.</li>
</ul>
<p>Flink has a broad SQL coverage for batch (full TPC-DS support) and a
state-of-the-art set of supported
@@ -408,59 +400,6 @@ API in the process. See <a
href="https://cwiki.apache.org/confluence/display/FLI
</li>
</ul>
-<p><strong>DataStream Batch Execution</strong></p>
-
-<ul>
- <li>
- <p>Flink is adding a <em>batch execution mode</em> for bounded DataStream
programs. This gives users faster and simpler
-execution and recovery of their bounded streaming applications; users do not
need to worry about watermarks and
-state sizes in this execution mode: <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-140%3A+Introduce+batch-style+execution+for+bounded+keyed+streams";>FLIP-140</a></p>
-
- <p>The core batch execution mode is implemented with <a
href="https://flink.apache.org/news/2020/12/10/release-1.12.0.html#batch-execution-mode-in-the-datastream-api";>great
results</a>;
-there are ongoing improvements around aspects like broadcast state and
processing-time-timers.
-This mode requires the new unified sources and sinks that are mentioned above,
so it is limited
-to the connectors that have been ported to those new APIs.</p>
- </li>
-</ul>
-
-<p><strong>Mixing bounded/unbounded streams, and batch/streaming
execution</strong></p>
-
-<ul>
- <li>
- <p>Support checkpointing when some tasks finished & Bounded stream
programs shut down with a final
-checkpoint: <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-147%3A+Support+Checkpoints+After+Tasks+Finished";>FLIP-147</a></p>
- </li>
- <li>
- <p>There are initial discussions and designs about jobs with mixed
batch/streaming execution, so stay tuned for more
-news in that area.</p>
- </li>
-</ul>
-
-<h2 id="subsuming-dataset-with-datastream-and-table-api">Subsuming DataSet
with DataStream and Table API</h2>
-
-<p>We want to eventually drop the legacy Batch-only DataSet API, have
batch-and stream processing unified
-throughout the entire system.</p>
-
-<p>Overall Discussion: <a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=158866741";>FLIP-131</a></p>
-
-<p>The <em>DataStream API</em> supports batch-execution to efficiently execute
streaming programs on historic data
-(see above). Takes over that set of use cases.</p>
-
-<p>The <em>Table API</em> should become the default API for batch-only
applications.</p>
-
-<ul>
- <li>Add more operations to Table API, so support common data manipulation
tasks more
- easily: <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-155%3A+Introduce+a+few+convenient+operations+in+Table+API";>FLIP-155</a></li>
- <li>Make Source and Sink definitions easier in the Table API.</li>
-</ul>
-
-<p>Improve the <em>interplay between the Table API and the DataStream API</em>
to allow switching from Table API to
-DataStream API when more control over the data types and operations is
necessary.</p>
-
-<ul>
- <li>Interoperability between DataStream and Table APIs: <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-136%3A++Improve+interoperability+between+DataStream+and+Table+API";>FLIP-136</a></li>
-</ul>
-
<hr />
<h1 id="applications-vs-clusters-flink-as-a-library">Applications vs.
Clusters; “Flink as a Library”</h1>
@@ -471,19 +410,9 @@ job as a self contained application.</p>
<p>For example as a simple Kubernetes deployment; deployed and scaled like a
regular application without extra workflows.</p>
-<p>Deploy Flink jobs as self-contained Applications works for all deployment
targets since Flink 1.11.0
-(<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-85+Flink+Application+Mode";>FLIP-85</a>).</p>
-
<ul>
- <li>
- <p>Reactive Scaling lets Flink applications change their parallelism in
response to growing and shrinking
-worker pools, and makes Flink compatibel with standard auto-scalers:
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-159%3A+Reactive+Mode";>FLIP-159</a></p>
- </li>
- <li>
- <p>Kubernetes-based HA-services let Flink applications run on Kubernetes
without requiring a ZooKeeper dependency:
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-144%3A+Native+Kubernetes+HA+for+Flink";>FLIP-144</a></p>
- </li>
+ <li>There is currently a Kubernetes Operator being developed by the
community. See
+<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-212%3A+Introduce+Flink+Kubernetes+Operator";>FLIP-212</a>.</li>
</ul>
<hr />
@@ -499,45 +428,31 @@ Checkpoints and recovery are stable and have been a
reliable workhorse for years
trying to make it faster, more predictable, and to remove some confusions and
inflexibility in some areas.</p>
<ul>
- <li>Unaligned Checkpoints, to make checkpoints progress faster when
applications cause backpressure:
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-76%3A+Unaligned+Checkpoints";>FLIP-76</a>,
available
-since Flink 1.12.2.</li>
- <li>Log-based Checkpoints, for very frequent incremental checkpointing:
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-158%3A+Generalized+incremental+checkpoints";>FLIP-158</a></li>
-</ul>
-
-<h2 id="large-scale-batch-applications">Large Scale Batch Applications</h2>
-
-<p>The community is working on making large scale batch execution (parallelism
in the order of 10,000s)
-simpler (less configuration tuning required) and more performant.</p>
-
-<ul>
- <li>
- <p>Introduce a more scalable batch shuffle. First parts of this have been
merged, and ongoing efforts are
-to make the memory footprint (JVM direct memory) more predictable, see
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-148%3A+Introduce+Sort-Merge+Based+Blocking+Shuffle+to+Flink";>FLIP-148</a></p>
-
- <ul>
- <li><a
href="https://issues.apache.org/jira/browse/FLINK-20740";>FLINK-20740</a></li>
- <li><a
href="https://issues.apache.org/jira/browse/FLINK-19938";>FLINK-19938</a></li>
- </ul>
- </li>
- <li>
- <p>Make scheduler faster for higher parallelism: <a
href="https://issues.apache.org/jira/browse/FLINK-21110";>FLINK-21110</a></p>
- </li>
+ <li><a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-183%3A+Dynamic+buffer+size+adjustment";>FLIP-183</a>
+is targeting size of checkpoints by debloating the buffers. A first beta is
already available.</li>
+ <li>With <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-151%3A+Incremental+snapshots+for+heap-based+state+backend";>FLIP-151</a>
+there is an ongoing effort to implement a heap based state backend.</li>
</ul>
<hr />
-<h1 id="python-apis">Python APIs</h1>
+<h1 id="apache-flink-as-part-of-an-ever-evolving-data-ecosystem">Apache Flink
as part of an ever evolving data ecosystem</h1>
-<p>Most functionalities in the Java Table APIs and DataStream APIs are already
supported by the Python APIs.
-The community is continuously working on improvements such as improving the
checkpoint strategy for Python UDF execution
-(<a href="https://issues.apache.org/jira/browse/FLINK-18235";>FLINK-18235</a>),
introducing more connectors support in both the Python DataStream API
-and Python Table API so that the Python API can be used in for production
implementations.</p>
+<p>There is almost no use case in which Apache Flink is used on its own. It
has established itself
+as part of many data related reference architectures. In fact you’ll find the
squirrel logo covering
+several aspects. The community has added a lot of connectors and formats. With
the already mentionend
+<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface";>FLIP-27</a>
and
+<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-143%3A+Unified+Sink+API";>FLIP-143</a>
+a new default for connectors has been established.</p>
-<p>Stateful transformation functions for the Python DataStream API:
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-153%3A+Support+state+access+in+Python+DataStream+API";>FLIP-153</a></p>
+<ul>
+ <li>There are efforts to revise the formats API with
+<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-219%3A+Revised+Format+API";>FLIP-219</a></li>
+ <li>There is ongoing work on new connectors
+(e.g. <a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=177045634";>Pinot</a>)</li>
+ <li>Connectors will be hosted in an external repository going forward. See
the
+<a href="https://lists.apache.org/thread/8k1xonqt7hn0xldbky1cxfx3fzh6sj7h";>ML
thread</a></li>
+</ul>
<hr />
@@ -554,16 +469,6 @@ of the documentation.</p>
<hr />
-<h1 id="miscellaneous-operational-tools">Miscellaneous Operational Tools</h1>
-
-<ul>
- <li>Allow switching state backends with savepoints: <a
href="https://issues.apache.org/jira/browse/FLINK-20976";>FLINK-20976</a></li>
- <li>Support for Savepoints with more properties, like incremental
savepoints, etc.:
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-47%3A+Checkpoints+vs.+Savepoints";>FLIP-47</a></li>
-</ul>
-
-<hr />
-
<h1 id="stateful-functions">Stateful Functions</h1>
<p>The Stateful Functions subproject has its own roadmap published under <a
href="https://statefun.io/";>statefun.io</a>.</p>
diff --git a/content/zh/roadmap.html b/content/zh/roadmap.html
index a54a1a0be..99e378f49 100644
--- a/content/zh/roadmap.html
+++ b/content/zh/roadmap.html
@@ -253,21 +253,18 @@ under the License.
<li><a href="#section-1" id="markdown-toc-section-1">功能阶段</a></li>
</ul>
</li>
- <li><a href="#sql" id="markdown-toc-sql">一体化分析：流批一体，SQL及其他</a> <ul>
- <li><a href="#sql-1" id="markdown-toc-sql-1">统一的SQL平台</a></li>
- <li><a href="#datastream-api-"
id="markdown-toc-datastream-api-">DataStream API 流批一体深度融合</a></li>
- <li><a href="#datastream--table-api--dataset"
id="markdown-toc-datastream--table-api--dataset">使用 DataStream & Table API
取代 DataSet</a></li>
+ <li><a
href="#unified-analytics-where-batch-and-streaming-come-together-sql-and-beyond"
id="markdown-toc-unified-analytics-where-batch-and-streaming-come-together-sql-and-beyond">Unified
Analytics: Where Batch and Streaming come Together; SQL and beyond.</a> <ul>
+ <li><a href="#a-unified-sql-platform"
id="markdown-toc-a-unified-sql-platform">A unified SQL Platform</a></li>
+ <li><a href="#deep-batch--streaming-unification-for-the-datastream-api"
id="markdown-toc-deep-batch--streaming-unification-for-the-datastream-api">Deep
Batch / Streaming Unification for the DataStream API</a></li>
</ul>
</li>
<li><a href="#applications-vs-clusters-flink-as-a-library"
id="markdown-toc-applications-vs-clusters-flink-as-a-library">Applications vs.
Clusters; “Flink as a Library”</a></li>
- <li><a href="#section-2" id="markdown-toc-section-2">性能</a> <ul>
+ <li><a href="#performance" id="markdown-toc-performance">Performance</a>
<ul>
<li><a href="#faster-checkpoints-and-recovery"
id="markdown-toc-faster-checkpoints-and-recovery">Faster Checkpoints and
Recovery</a></li>
- <li><a href="#section-3" id="markdown-toc-section-3">大规模批作业</a></li>
</ul>
</li>
- <li><a href="#python-apis" id="markdown-toc-python-apis">Python APIs</a></li>
- <li><a href="#section-4" id="markdown-toc-section-4">文档</a></li>
- <li><a href="#section-5" id="markdown-toc-section-5">操作工具</a></li>
+ <li><a href="#apache-flink-as-part-of-an-ever-evolving-data-ecosystem"
id="markdown-toc-apache-flink-as-part-of-an-ever-evolving-data-ecosystem">Apache
Flink as part of an ever evolving data ecosystem</a></li>
+ <li><a href="#documentation"
id="markdown-toc-documentation">Documentation</a></li>
<li><a href="#stateful-functions"
id="markdown-toc-stateful-functions">Stateful Functions</a></li>
</ul>
@@ -295,7 +292,7 @@ under the License.
。</p>
<div class="row front-graphic">
- <img src="/img/flink_feature_radar_zh_2.svg" width="700px" />
+ <img src="/img/flink_feature_radar_zh_3.svg" width="700px" />
</div>
<h2 id="section-1">功能阶段</h2>
@@ -311,144 +308,92 @@ under the License.
<hr />
-<h1 id="sql">一体化分析：流批一体，SQL及其他</h1>
-<p>Flink的内核是流数据处理系统，Flink将批处理作为流的特例，用流的方式来执行批处理。
-Flink作为一个流式引擎，不仅能够高效执行批处理，更重要的是通过高效处理有限流的方式，
-打开了无缝流批一体处理之门。</p>
+<h1
id="unified-analytics-where-batch-and-streaming-come-together-sql-and-beyond">Unified
Analytics: Where Batch and Streaming come Together; SQL and beyond.</h1>
-<p>流批一体升级了流数据范例：它可以保证实时和离线应用语义的一致性。
-此外，有时流式处理的作业也需要离线（有限流）处理作为补充，例如，
-在出现错误或出现数据质量问题时需要重新处理数据，
-或者有的情况下启动新的作业但需要历史数据作为引导。统一的API和系统使得此类操作变得很容易。</p>
+<p>Flink is a streaming data system in its core, that executes “batch as a
special case of streaming”.
+Efficient execution of batch jobs is powerful in its own right; but even more
so, batch processing
+capabilities (efficient processing of bounded streams) open the way for a
seamless unification of
+batch and streaming applications.</p>
-<h2 id="sql-1">统一的SQL平台</h2>
+<p>Unified streaming/batch up-levels the streaming data paradigm: It gives
users consistent semantics across
+their real-time and lag-time applications. Furthermore, streaming applications
often need to be complemented
+by batch (bounded stream) processing, for example when reprocessing data after
bugs or data quality issues,
+or when bootstrapping new applications. A unified API and system make this
much easier.</p>
-<p>Flink社区一直致力于建设基于Flink的统一的流批一体SQL分析平台，并将持续在这个方向上努力。
-SQL具有非常强的跨流批的语义，并允许用户使用相同的SQL语句对即时查询（ad-hoc query）和
-连续查询（continuous query）进行分析。Flink拥有高效的统一查询引擎，以及基于此的一系列整合统一。
-根据用户反馈，我们会持续改善这些整合统一的使用体验。</p>
+<h2 id="a-unified-sql-platform">A unified SQL Platform</h2>
-<p><strong>CDC & Connectors</strong></p>
+<p>The community has been building Flink to a powerful basis for a unified
(batch and streaming) SQL analytics
+platform, and is continuing to do so.</p>
-<ul>
- <li>CDC（Change-Data-Capture）: 通过直接连接到数据库的日志来捕获数据变更。Flink社区会加强和CDC的整合
- <ul>
- <li>CDC connectors:
-<a
href="https://flink-packages.org/packages/cdc-connectors";>https://flink-packages.org/packages/cdc-connectors</a></li>
- <li>背景: <a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=147427289";>FLIP-105</a>
-(CDC对SQL的支持) & <a href="https://debezium.io/";>Debezium</a></li>
- </ul>
- </li>
- <li>数据湖Connectors: 流批一体对数据湖有很大价值，包括支持流（处理当前数据）和批（处理历史数据）的相同API，相同语义，以及相同引擎。
-目前，Flink社区正在和各种数据湖系统进行深度融合，包括：
- <ul>
- <li><a href="https://iceberg.apache.org/";>Apache Iceberg</a>:
-<a
href="https://iceberg.apache.org/flink/";>https://iceberg.apache.org/flink/</a></li>
- <li><a href="https://hudi.apache.org/";>Apache Hudi</a>:
-<a
href="https://hudi.apache.org/blog/apache-hudi-meets-apache-flink/";>https://hudi.apache.org/blog/apache-hudi-meets-apache-flink/</a></li>
- <li><a href="https://pinot.apache.org/";>Apache Pinot</a>:
-<a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=177045634";>FLIP-166</a></li>
- </ul>
- </li>
-</ul>
-
-<p><strong>SQL 平台基建</strong></p>
+<p>SQL has very strong cross-batch-streaming semantics, allowing users to use
the same queries for ad-hoc analytics
+and as continuous queries. Flink already contains an efficient unified query
engine, and a wide set of
+integrations. With user feedback, those are continuously improved.</p>
-<ul>
- <li>为了简化Flink SQL的生产实践，我们正在改进SQL客户端以及SQL Gateway中客户端和群集之间与交互相关的组件：
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-163%3A+SQL+Client+Improvements";>FLIP-163</a></li>
-</ul>
-
-<p><strong>通用语言，格式（Formats），目录（Catalogs）</strong></p>
+<p><strong>Going Beyond a SQL Stream/Batch Processing Engine</strong></p>
<ul>
- <li>Hive Query兼容性支持: <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-152%3A+Hive+Query+Syntax+Compatibility";>FLIP-152</a></li>
+ <li>To extend the capability of a pure stream processor and make Flink ready
for future use cases,
+<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-188%3A+Introduce+Built-in+Dynamic+Table+Storage";>FLIP-188</a>
+has been announced adding built in dynamic table storage.</li>
+ <li>The experience of updating Flink SQL based jobs has been rather
cumbersome as it could have
+lead to new job graphs making restoring from savepoints/checkpoints
impossible.
+<a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=191336489&src=contextnavpagetreemode";>FLIP-190</a>
+that already has been shipped as MVP is targeting this.</li>
</ul>
-<p>Flink SQL具备广泛的批处理覆盖（全面的TPC-DS支持）和最先进的流处理支持。我们也一直会努力增添更多的SQL功能和算子。</p>
-
-<h2 id="datastream-api-">DataStream API 流批一体深度融合</h2>
-
-<p><em>DataStream API</em> 是 Flink 的<em>物理层</em> API,
针对需要明确地控制数据类型，数据流，状态以及时间的应用。
-DataStream API 也在不断丰富演化以支持在有限数据上的高效地批处理。
-DataStream API 使用和流式一样的 dataflow 来执行批处理，并使用一样的算子。
-这样一来，用户使用 DataStream API 表达流和批可以保持相同级别的控制。
-我们对DataStream API融合的最终目标是可以混合并自由切换流批执行，提供流批间无缝切换体验。</p>
-
-<p><strong>统一 Sources & Sinks</strong></p>
+<p><strong>Platform Infrastructure</strong></p>
<ul>
- <li>
- <p>第一代 source 的 API 和实现要么只能用于 DataStream API 里面的流处理
-(<a
href="https://github.com/apache/flink/blob/master/flink-streaming-java/src/main/java/org/apache/flink/streaming/api/functions/source/SourceFunction.java";>SourceFunction</a>)
-；要么只能用于 DataSet API 里面的批处理
-(<a
href="https://github.com/apache/flink/blob/master/flink-core/src/main/java/org/apache/flink/api/common/io/InputFormat.java";>InputFormat</a>)
。</p>
-
- <p>因此，我们致力于构造对流和批都适用的 sources，能让用户在这两种模式下有一致的使用体验，
-并可以很容易地在流处理和批处理之间切换，执行无限流和有限流作业。
-新的 Source API 的接口已经可以使用。我们会将更多的 source connectors 迁移到这个新的模型，详见
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface";>FLIP-27</a>.</p>
- </li>
- <li>
- <p>和 source 类似，原先的 sink 及其 API 也是分别针对流
-(<a
href="https://github.com/apache/flink/blob/master/flink-streaming-java/src/main/java/org/apache/flink/streaming/api/functions/sink/SinkFunction.java";>SinkFunction</a>)
-和批 (<a
href="https://github.com/apache/flink/blob/master/flink-core/src/main/java/org/apache/flink/api/common/io/OutputFormat.java";>OutputFormat</a>)
-设计的。</p>
-
- <p>为此，我们引入了新的 sink API，可以流批一致的解决结果输出和提交 (<em>Transactions</em>) 的问题。
-新的 sink API 的第一版已经出炉，并在不断改进中，详见
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-143%3A+Unified+Sink+API";>FLIP-143</a>
。</p>
- </li>
+ <li>After <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-163%3A+SQL+Client+Improvements";>FLIP-163</a>
+the community is working again on a set of SQL Client usability improvements
+(<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-189%3A+SQL+Client+Usability+Improvements";>FLIP-189</a>)
+which is aiming at improving the user experience, when using the SQL
client.</li>
</ul>
-<p><strong>DataStream 的批处理执行模式</strong></p>
+<p><strong>Support for Common Languages, Formats, Catalogs</strong></p>
<ul>
- <li>
- <p>Flink 在 DataStream 上为有限流新增加了<em>批执行模式</em>，这可以使得用户更简单快速的执行和恢复有限流作业。
-在有限流的批执行模式下，用户无需担心 watermarks 和状态大小的问题：
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-140%3A+Introduce+batch-style+execution+for+bounded+keyed+streams";>FLIP-140</a>
。</p>
-
- <p>批执行模式的核心实现已有 <a
href="https://flink.apache.org/news/2020/12/10/release-1.12.0.html#batch-execution-mode-in-the-datastream-api";>很好的结果</a>;
-其他部分也在持续改进中，包括 broadcast state 和 processing-time-timers。
-值得注意的是，此模式的实现基于上面提到的新的的 source 和 sink，因此它只能支持已经使用新 API 的 connectors。</p>
- </li>
+ <li>With <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-216%3A++Introduce+pluggable+dialect+and++decouple+Hive+connector";>FLIP-216</a>
+there’s now the initiative to introduce pluggable dialects on the example of
the Hive connector.
+Including so many dependencies to make dialects work has lead to an overhead
for contributors
+and users.</li>
</ul>
-<p><strong>混合 有限流/无限流 & 批执行/流执行</strong></p>
-
-<ul>
- <li>
- <p>支持在部分 task 结束后还可以做 checkpoint & 支持有限流作业在结束的时候做最后一次 checkpoint ：
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-147%3A+Support+Checkpoints+After+Tasks+Finished";>FLIP-147</a></p>
- </li>
- <li>
- <p>对于混合/切换流和批的执行，我们有一些初步的设计和讨论，敬请关注。</p>
- </li>
-</ul>
+<p>Flink has a broad SQL coverage for batch (full TPC-DS support) and a
state-of-the-art set of supported
+operations in streaming. There is continuous effort to add more functions and
cover more SQL operations.</p>
-<h2 id="datastream--table-api--dataset">使用 DataStream & Table API 取代
DataSet</h2>
+<h2 id="deep-batch--streaming-unification-for-the-datastream-api">Deep Batch /
Streaming Unification for the DataStream API</h2>
-<p>我们希望最终能弃用只支持批式处理的 DataSet API，从而使用统一的流批处理贯穿整个系统。
-整体的讨论在这里: <a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=158866741";>FLIP-131</a>
。</p>
+<p>The <em>DataStream API</em> is Flink’s <em>physical</em> API, for use cases
where users need very explicit control over data
+types, streams, state, and time. This API is evolving to support efficient
batch execution on bounded data.</p>
-<p><em>DataStream API</em> 可以高效的用批的方式来执行需要处理历史数据的流作业（如上所述）。</p>
+<p>DataStream API executes the same dataflow shape in batch as in streaming,
keeping the same operators.
+That way users keep the same level of control over the dataflow, and our goal
is to mix and switch between
+batch/streaming execution in the future to make it a seamless experience.</p>
-<p><em>Table API</em> 应该是所有单批作业所使用的默认的 API 。</p>
+<p><strong>Unified Sources and Sinks</strong></p>
<ul>
<li>
- <p>Table API 增加更多操作，以方便支持常见的数据操作任务
- <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-155%3A+Introduce+a+few+convenient+operations+in+Table+API";>FLIP-155</a></p>
- </li>
- <li>
- <p>在 Table API 中，使 Source 和 Sink 更容易定义使用</p>
+ <p>The first APIs and implementations of sources were specific to either
streaming programs in the DataStream API
+(<a
href="https://github.com/apache/flink/blob/master/flink-streaming-java/src/main/java/org/apache/flink/streaming/api/functions/source/SourceFunction.java";>SourceFunction</a>),
+or to batch programs in the DataSet API (<a
href="https://github.com/apache/flink/blob/master/flink-core/src/main/java/org/apache/flink/api/common/io/InputFormat.java";>InputFormat</a>).</p>
+
+ <p>In this effort, we are creating sources that work across batch and
streaming execution. The aim is to give
+users a consistent experience across both modes, and to allow them to easily
switch between streaming and batch
+execution for their unbounded and bounded streaming applications.
+The interface for this New Source API is done and available, and we are
working on migrating more source connectors
+to this new model, see <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface";>FLIP-27</a>.</p>
</li>
<li>
- <p>DataStream API & Table API 互通性:
- <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-136%3A++Improve+interoperability+between+DataStream+and+Table+API";>FLIP-136</a></p>
+ <p>Similar to the sources, the original sink APIs are also specific to
streaming
+(<a
href="https://github.com/apache/flink/blob/master/flink-streaming-java/src/main/java/org/apache/flink/streaming/api/functions/sink/SinkFunction.java";>SinkFunction</a>)
+and batch (<a
href="https://github.com/apache/flink/blob/master/flink-core/src/main/java/org/apache/flink/api/common/io/OutputFormat.java";>OutputFormat</a>)
+APIs and execution.</p>
- <p>提升 Table API 和 DataStream API 之间互通的能力。当需要更多对数据类型和操作控制的时候，允许从 Table API
切换到 DataStream API 。</p>
+ <p>We have introduced a new API for sinks that consistently handles result
writing and committing (<em>Transactions</em>)
+across batch and streaming. The first iteration of the API exists, and we are
porting sinks and refining the
+API in the process. See <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-143%3A+Unified+Sink+API";>FLIP-143</a>.</p>
</li>
</ul>
@@ -456,103 +401,74 @@ DataStream API 使用和流式一样的 dataflow 来执行批处理，并使用
<h1 id="applications-vs-clusters-flink-as-a-library">Applications vs.
Clusters; “Flink as a Library”</h1>
-<p>这个部分的工作主要是为了使部署（长时间运行的流式）Flink 作业变得更为自然简单。
-我们希望部署一个流式作业就像启动一个独立的应用（Applications）一样简单：
-不需要首先启动集群（Clusters），再向该集群提交作业。</p>
+<p>The goal of these efforts is to make it feel natural to deploy (long
running streaming) Flink applications.
+Instead of starting a cluster and submitting a job to that cluster, these
efforts support deploying a streaming
+job as a self contained application.</p>
-<p>例如，我们期望 Flink 作业可以作为简单的Kubernetes部署，能像普通应用程序一样可以进行常规部署和扩展，而无需额外的工作流程。
-从 Flink 1.11.0 开始，Flink 支持将 Flink 作业部署为独立的应用程序
-(<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-85+Flink+Application+Mode";>FLIP-85</a>)
。</p>
+<p>For example as a simple Kubernetes deployment; deployed and scaled like a
regular application without extra workflows.</p>
<ul>
- <li>
- <p>响应式缩放功能（Reactive Scaling）可以使 Flink 作业根据资源池的增长和收缩情况更改并行度。
-这样可以自然地使 Flink 与标准自动缩放（atuo Scaler）兼容。
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-159%3A+Reactive+Mode";>FLIP-159</a></p>
- </li>
- <li>
- <p>基于 Kubernetes 的高可用性（HA）服务使 Flink 作业在 Kubernetes 上运行时无需依赖ZooKeeper：
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-144%3A+Native+Kubernetes+HA+for+Flink";>FLIP-144</a></p>
- </li>
+ <li>There is currently a Kubernetes Operator being developed by the
community. See
+<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-212%3A+Introduce+Flink+Kubernetes+Operator";>FLIP-212</a>.</li>
</ul>
<hr />
-<h1 id="section-2">性能</h1>
+<h1 id="performance">Performance</h1>
-<p>我们会持续不断的提高性能和容错恢复速度。</p>
+<p>Continuous work to keep improving performance and recovery speed.</p>
<h2 id="faster-checkpoints-and-recovery">Faster Checkpoints and Recovery</h2>
-<p>Flink 社区正在致力于提升做检查点（checkpointing）和容错恢复（recovery）的速度。
-Flink的容错机制多年来运行非常稳定，但是我们还是想让整个容错过程更快并且更可预测，提升易用性。</p>
-
-<ul>
- <li>
- <p>Unaligned Checkpoints，解决反压情况下 Checkpoint 做不出来的问题，从 Flink 1.12.2 版本开始可用：
- <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-76%3A+Unaligned+Checkpoints";>FLIP-76</a></p>
- </li>
- <li>
- <p>Log-based Checkpoints, 可以做高频增量 Checkpoints，加快 checkpoint：
- <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-158%3A+Generalized+incremental+checkpoints";>FLIP-158</a></p>
- </li>
-</ul>
-
-<h2 id="section-3">大规模批作业</h2>
-
-<p>Flink 社区也在致力于简化大规模批作业（并行度量级在10,000左右）的部署运行，所需的配置调整更少并使之有更好的性能。</p>
+<p>The community is continuously working on improving checkpointing and
recovery speed.
+Checkpoints and recovery are stable and have been a reliable workhorse for
years. We are still
+trying to make it faster, more predictable, and to remove some confusions and
inflexibility in some areas.</p>
<ul>
- <li>
- <p>为批处理引入更具扩展性的 batch shuffle。Batch shuffle 的第一部分已经合并入社区代码，
- 剩下的部分可以使内存占用量（JVM直接内存）更可预测，请参阅
- <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-148%3A+Introduce+Sort-Merge+Based+Blocking+Shuffle+to+Flink";>FLIP-148</a></p>
-
- <ul>
- <li><a
href="https://issues.apache.org/jira/browse/FLINK-20740";>FLINK-20740</a></li>
- <li><a
href="https://issues.apache.org/jira/browse/FLINK-19938";>FLINK-19938</a></li>
- </ul>
- </li>
- <li>
- <p>更快调度高并发作业：<a
href="https://issues.apache.org/jira/browse/FLINK-21110";>FLINK-21110</a></p>
- </li>
+ <li><a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-183%3A+Dynamic+buffer+size+adjustment";>FLIP-183</a>
+is targeting size of checkpoints by debloating the buffers. A first beta is
already available.</li>
+ <li>With <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-151%3A+Incremental+snapshots+for+heap-based+state+backend";>FLIP-151</a>
+there is an ongoing effort to implement a heap based state backend.</li>
</ul>
<hr />
-<h1 id="python-apis">Python APIs</h1>
+<h1 id="apache-flink-as-part-of-an-ever-evolving-data-ecosystem">Apache Flink
as part of an ever evolving data ecosystem</h1>
-<p>Python DataStream API 对状态访问的支持：
-<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-153%3A+Support+state+access+in+Python+DataStream+API";>FLIP-153</a></p>
-
-<hr />
-
-<h1 id="section-4">文档</h1>
-
-<p>我们也正在简化文档结构，以方便更直观的导航和阅读</p>
+<p>There is almost no use case in which Apache Flink is used on its own. It
has established itself
+as part of many data related reference architectures. In fact you’ll find the
squirrel logo covering
+several aspects. The community has added a lot of connectors and formats. With
the already mentionend
+<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface";>FLIP-27</a>
and
+<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-143%3A+Unified+Sink+API";>FLIP-143</a>
+a new default for connectors has been established.</p>
<ul>
- <li>Flink 文档迁移（Jekyll to Hugo）:
- <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-157+Migrate+Flink+Documentation+from+Jekyll+to+Hugo";>FLIP-157</a></li>
- <li>文档重构: <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-42%3A+Rework+Flink+Documentation";>FLIP-42</a></li>
- <li>SQL 文档: <a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=127405685";>FLIP-60</a></li>
+ <li>There are efforts to revise the formats API with
+<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-219%3A+Revised+Format+API";>FLIP-219</a></li>
+ <li>There is ongoing work on new connectors
+(e.g. <a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=177045634";>Pinot</a>)</li>
+ <li>Connectors will be hosted in an external repository going forward. See
the
+<a href="https://lists.apache.org/thread/8k1xonqt7hn0xldbky1cxfx3fzh6sj7h";>ML
thread</a></li>
</ul>
<hr />
-<h1 id="section-5">操作工具</h1>
+<h1 id="documentation">Documentation</h1>
+
+<p>There are various dedicated efforts to simplify the maintenance and
structure (more intuitive navigation/reading)
+of the documentation.</p>
<ul>
- <li>允许使用 savepoint 来切换后端状态存储（state backends）: <a
href="https://issues.apache.org/jira/browse/FLINK-20976";>FLINK-20976</a></li>
- <li>支持 savepoint 的其他的一些属性，例如增量 savepoint 等:
- <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-47%3A+Checkpoints+vs.+Savepoints";>FLIP-47</a></li>
+ <li>Docs Tech Stack: <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-157+Migrate+Flink+Documentation+from+Jekyll+to+Hugo";>FLIP-157</a></li>
+ <li>General Docs Structure: <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-42%3A+Rework+Flink+Documentation";>FLIP-42</a></li>
+ <li>SQL Docs: <a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=127405685";>FLIP-60</a></li>
</ul>
<hr />
<h1 id="stateful-functions">Stateful Functions</h1>
-<p>Stateful Functions 子项目有其单独的规划路线图，请参考 <a
href="https://statefun.io/";>statefun.io</a> 。</p>
+<p>The Stateful Functions subproject has its own roadmap published under <a
href="https://statefun.io/";>statefun.io</a>.</p>
</div>
