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[flink-web] branch asf-site updated (3b5e54b -> 2419879)
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from 3b5e54b Rebuild website
new 6d611d7 [blog] Add blogpost about Temporal Tables and Temporal Table Joins.
new 2419879 Rebuild website
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Summary of changes:
_posts/2019-05-14-temporal-tables.md | 111 ++++++++
content/2019/05/14/temporal-tables.html | 316 +++++++++++++++++++++
content/blog/feed.xml | 112 ++++++++
content/blog/index.html | 40 +--
content/blog/page2/index.html | 42 +--
content/blog/page3/index.html | 45 +--
content/blog/page4/index.html | 43 ++-
content/blog/page5/index.html | 40 +--
content/blog/page6/index.html | 42 +--
content/blog/page7/index.html | 40 ++-
content/blog/page8/index.html | 45 +--
content/blog/{page2 => page9}/index.html | 164 ++---------
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.../2019-05-13-temporal-tables/TemporalTables4.png | Bin 0 -> 106890 bytes
content/index.html | 6 +-
content/zh/index.html | 6 +-
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.../2019-05-13-temporal-tables/TemporalTables2.png | Bin 0 -> 35586 bytes
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create mode 100644 content/2019/05/14/temporal-tables.html
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create mode 100644 content/img/blog/2019-05-13-temporal-tables/TemporalTables1.png
create mode 100644 content/img/blog/2019-05-13-temporal-tables/TemporalTables2.png
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[flink-web] 01/02: [blog] Add blogpost about Temporal Tables and
Temporal Table Joins.
Posted by fh...@apache.org.
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fhueske pushed a commit to branch asf-site
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commit 6d611d737ebd36f57da6e7a9c80d3201bc0ed2e1
Author: Marta Paes Moreira <ma...@gmail.com>
AuthorDate: Mon May 13 15:23:07 2019 +0200
[blog] Add blogpost about Temporal Tables and Temporal Table Joins.
This closes #213.
---
_posts/2019-05-14-temporal-tables.md | 111 +++++++++++++++++++++
.../2019-05-13-temporal-tables/TemporalTables1.png | Bin 0 -> 25109 bytes
.../2019-05-13-temporal-tables/TemporalTables2.png | Bin 0 -> 35586 bytes
.../2019-05-13-temporal-tables/TemporalTables3.png | Bin 0 -> 21276 bytes
.../2019-05-13-temporal-tables/TemporalTables4.png | Bin 0 -> 106890 bytes
5 files changed, 111 insertions(+)
diff --git a/_posts/2019-05-14-temporal-tables.md b/_posts/2019-05-14-temporal-tables.md
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@@ -0,0 +1,111 @@
+---
+layout: post
+title: "Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL"
+date: 2019-05-14T12:00:00.000Z
+authors:
+- morsapaes:
+ name: "Marta Paes"
+ twitter: "morsapaes"
+
+excerpt: Apache Flink natively supports temporal table joins since the 1.7 release for straightforward temporal data handling. In this blog post, we provide an overview of how this new concept can be leveraged for effective point-in-time analysis in streaming scenarios.
+---
+
+Figuring out how to manage and model temporal data for effective point-in-time analysis was a longstanding battle, dating as far back as the early 80’s, that culminated with the introduction of temporal tables in the SQL standard in 2011. Up to that point, users were doomed to implement this as part of the application logic, often hurting the length of the development lifecycle as well as the maintainability of the code. And, although there isn’t a single, commonly accepted definition of [...]
+
+<center>
+<img src="{{ site.baseurl }}/img/blog/2019-05-13-temporal-tables/TemporalTables1.png" width="500px" alt="Taxi Fares and Conversion Rates"/>
+</center>
+<br>
+
+**For example:** given a stream with Taxi Fare events tied to the local currency of the ride location, we might want to convert the fare price to a common currency for further processing. As conversion rates excel at fluctuating over time, each Taxi Fare event would need to be matched to the rate that was valid at the time the event occurred in order to produce a reliable result.
+
+## Modelling Temporal Data with Flink
+
+In the 1.7 release, Flink has introduced the concept of **temporal tables** into its streaming SQL and Table API: parameterized views on append-only tables — or, any table that only allows records to be inserted, never updated or deleted — that are interpreted as a changelog and keep data closely tied to time context, so that it can be interpreted as valid only within a specific period of time. Transforming a stream into a temporal table requires:
+
+* Defining a **primary key** and a **versioning field** that can be used to keep track of the changes that happen over time;
+
+* Exposing the stream as a **temporal table function** that maps each point in time to a static relation.
+
+Going back to our example use case, a temporal table is just what we need to model the conversion rate data such as to make it useful for point-in-time querying. Temporal table functions are implemented as an extension of Flink’s generic [table function](https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/table/udfs.html#table-functions) class and can be defined in the same straightforward way to be used with the Table API or SQL parser.
+
+```java
+import org.apache.flink.table.functions.TemporalTableFunction;
+
+(...)
+
+// Get the stream and table environments.
+StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
+StreamTableEnvironment tEnv = StreamTableEnvironment.getTableEnvironment(env);
+
+// Provide a sample static data set of the rates history table.
+List <Tuple2<String, Long>>ratesHistoryData =new ArrayList<>();
+
+ratesHistoryData.add(Tuple2.of("USD", 102L));
+ratesHistoryData.add(Tuple2.of("EUR", 114L));
+ratesHistoryData.add(Tuple2.of("YEN", 1L));
+ratesHistoryData.add(Tuple2.of("EUR", 116L));
+ratesHistoryData.add(Tuple2.of("USD", 105L));
+
+// Create and register an example table using the sample data set.
+DataStream<Tuple2<String, Long>> ratesHistoryStream = env.fromCollection(ratesHistoryData);
+
+Table ratesHistory = tEnv.fromDataStream(ratesHistoryStream, "r_currency, r_rate, r_proctime.proctime");
+
+tEnv.registerTable("RatesHistory", ratesHistory);
+
+// Create and register the temporal table function "rates".
+// Define "r_proctime" as the versioning field and "r_currency" as the primary key.
+TemporalTableFunction rates = ratesHistory.createTemporalTableFunction("r_proctime", "r_currency");
+
+tEnv.registerFunction("Rates", rates);
+
+(...)
+```
+
+What does this **Rates** function do, in practice? Imagine we would like to check what the conversion rates looked like at a given time — say, 11:00. We could simply do something like:
+
+```sql
+SELECT * FROM Rates('11:00');
+```
+
+<center>
+<img src="{{ site.baseurl }}/img/blog/2019-05-13-temporal-tables/TemporalTables2.png" width="650px" alt="Point-in-time Querying"/>
+</center>
+<br>
+
+Even though Flink does not yet support querying temporal table functions with a constant time attribute parameter, these functions can be used to cover a much more interesting scenario: temporal table joins.
+
+## Streaming Joins using Temporal Tables
+
+Temporal tables reach their full potential when used in combination — erm, joined — with streaming data, for instance to power applications that must continuously whitelist against a reference dataset that changes over time for auditing or regulatory compliance. While efficient joins have long been an enduring challenge for query processors due to computational cost and resource consumption, joins over streaming data carry some additional challenges:
+
+* The **unbounded** nature of streams means that inputs are continuously evaluated and intermediate join results can consume memory resources indefinitely. Flink gracefully manages its memory consumption out-of-the-box (even for heavier cases where joins require spilling to disk) and supports time-windowed joins to bound the amount of data that needs to be kept around as state;
+* Streaming data might be **out-of-order** and **late**, so it is not possible to enforce an ordering upfront and time handling requires some thinking to avoid unnecessary outputs and retractions.
+
+In the particular case of temporal data, time-windowed joins are not enough (well, at least not without getting into some expensive tweaking): sooner or later, each reference record will fall outside of the window and be wiped from state, no longer being considered for future join results. To address this limitation, Flink has introduced support for temporal table joins to cover time-varying relations.
+
+<center>
+<img src="{{ site.baseurl }}/img/blog/2019-05-13-temporal-tables/TemporalTables3.png" width="500px" alt="Temporal Table Join between Taxi Fares and Conversion Rates"/>
+</center>
+<br>
+
+Each record from the append-only table on the probe side (```Taxi Fare```) is joined with the version of the record from the temporal table on the build side (```Conversion Rate```) that most closely matches the probe side record time attribute (```time```) for the same value of the primary key (```currency```). Remember the temporal table function (```Rates```) we registered earlier? It can now be used to express this join as a simple SQL statement that would otherwise require a heavier [...]
+
+<center>
+<img src="{{ site.baseurl }}/img/blog/2019-05-13-temporal-tables/TemporalTables4.png" width="700px" alt="Regular Join vs. Temporal Table Join"/>
+</center>
+<br>
+
+Temporal table joins support both [processing](https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/table/streaming/joins.html#processing-time-temporal-joins) and [event time](https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/table/streaming/joins.html#event-time-temporal-joins) semantics and effectively limit the amount of data kept in state while also allowing records on the build side to be arbitrarily old, as opposed to time-windowed joins. Probe-side records [...]
+
+* Narrowing the **scope** of the join: only the time-matching version of ```ratesHistory``` is visible for a given ```taxiFare.time```;
+* Pruning **unneeded records** from state: for cases using event time, records between current time and the [watermark](https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/event_time.html#event-time-and-watermarks) delay are persisted for both the probe and build side. These are discarded as soon as the watermark arrives and the results are emitted — allowing the join operation to move forward in time and the build table to “refresh” its version in state.
+
+## Conclusion
+
+All this means it is now possible to express continuous stream enrichment in relational and time-varying terms using Flink without dabbling into syntactic patchwork or compromising performance. In other words: stream time-travelling minus the flux capacitor. Extending this syntax to batch processing for enriching historic data with proper (event) time semantics is also part of the Flink roadmap!
+
+If you'd like to get some **hands-on practice in joining streams with Flink SQL** (and Flink SQL in general), checkout this [free training for Flink SQL](https://github.com/ververica/sql-training/wiki). The training environment is based on Docker and set up in just a few minutes.
+
+Subscribe to the [Apache Flink mailing lists](https://flink.apache.org/community.html#mailing-lists) to stay up-to-date with the latest developments in this space.
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[flink-web] 02/02: Rebuild website
Posted by fh...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
fhueske pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/flink-web.git
commit 2419879e486c24c1467e35d6c859c2689bc91fff
Author: Fabian Hueske <fh...@apache.org>
AuthorDate: Tue May 14 14:50:44 2019 +0200
Rebuild website
---
content/2019/05/14/temporal-tables.html | 316 +++++++++++++++++++++
content/blog/feed.xml | 112 ++++++++
content/blog/index.html | 40 +--
content/blog/page2/index.html | 42 +--
content/blog/page3/index.html | 45 +--
content/blog/page4/index.html | 43 ++-
content/blog/page5/index.html | 40 +--
content/blog/page6/index.html | 42 +--
content/blog/page7/index.html | 40 ++-
content/blog/page8/index.html | 45 +--
content/blog/{page2 => page9}/index.html | 164 ++---------
.../2019-05-13-temporal-tables/TemporalTables1.png | Bin 0 -> 25109 bytes
.../2019-05-13-temporal-tables/TemporalTables2.png | Bin 0 -> 35586 bytes
.../2019-05-13-temporal-tables/TemporalTables3.png | Bin 0 -> 21276 bytes
.../2019-05-13-temporal-tables/TemporalTables4.png | Bin 0 -> 106890 bytes
content/index.html | 6 +-
content/zh/index.html | 6 +-
17 files changed, 660 insertions(+), 281 deletions(-)
diff --git a/content/2019/05/14/temporal-tables.html b/content/2019/05/14/temporal-tables.html
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--- /dev/null
+++ b/content/2019/05/14/temporal-tables.html
@@ -0,0 +1,316 @@
+<!DOCTYPE html>
+<html lang="en">
+ <head>
+ <meta charset="utf-8">
+ <meta http-equiv="X-UA-Compatible" content="IE=edge">
+ <meta name="viewport" content="width=device-width, initial-scale=1">
+ <!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags -->
+ <title>Apache Flink: Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</title>
+ <link rel="shortcut icon" href="/favicon.ico" type="image/x-icon">
+ <link rel="icon" href="/favicon.ico" type="image/x-icon">
+
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+
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+ <h1>Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</h1>
+
+ <article>
+ <p>14 May 2019 Marta Paes (<a href="https://twitter.com/morsapaes">@morsapaes</a>)</p>
+
+<p>Figuring out how to manage and model temporal data for effective point-in-time analysis was a longstanding battle, dating as far back as the early 80’s, that culminated with the introduction of temporal tables in the SQL standard in 2011. Up to that point, users were doomed to implement this as part of the application logic, often hurting the length of the development lifecycle as well as the maintainability of the code. And, although there isn’t a single, commonly accepted definition [...]
+
+<center>
+<img src="/img/blog/2019-05-13-temporal-tables/TemporalTables1.png" width="500px" alt="Taxi Fares and Conversion Rates" />
+</center>
+<p><br /></p>
+
+<p><strong>For example:</strong> given a stream with Taxi Fare events tied to the local currency of the ride location, we might want to convert the fare price to a common currency for further processing. As conversion rates excel at fluctuating over time, each Taxi Fare event would need to be matched to the rate that was valid at the time the event occurred in order to produce a reliable result.</p>
+
+<h2 id="modelling-temporal-data-with-flink">Modelling Temporal Data with Flink</h2>
+
+<p>In the 1.7 release, Flink has introduced the concept of <strong>temporal tables</strong> into its streaming SQL and Table API: parameterized views on append-only tables — or, any table that only allows records to be inserted, never updated or deleted — that are interpreted as a changelog and keep data closely tied to time context, so that it can be interpreted as valid only within a specific period of time. Transforming a stream into a temporal table requires:</p>
+
+<ul>
+ <li>
+ <p>Defining a <strong>primary key</strong> and a <strong>versioning field</strong> that can be used to keep track of the changes that happen over time;</p>
+ </li>
+ <li>
+ <p>Exposing the stream as a <strong>temporal table function</strong> that maps each point in time to a static relation.</p>
+ </li>
+</ul>
+
+<p>Going back to our example use case, a temporal table is just what we need to model the conversion rate data such as to make it useful for point-in-time querying. Temporal table functions are implemented as an extension of Flink’s generic <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/table/udfs.html#table-functions">table function</a> class and can be defined in the same straightforward way to be used with the Table API or SQL parser.</p>
+
+<div class="highlight"><pre><code class="language-java"><span class="kn">import</span> <span class="nn">org.apache.flink.table.functions.TemporalTableFunction</span><span class="o">;</span>
+
+<span class="o">(...)</span>
+
+<span class="c1">// Get the stream and table environments.</span>
+<span class="n">StreamExecutionEnvironment</span> <span class="n">env</span> <span class="o">=</span> <span class="n">StreamExecutionEnvironment</span><span class="o">.</span><span class="na">getExecutionEnvironment</span><span class="o">();</span>
+<span class="n">StreamTableEnvironment</span> <span class="n">tEnv</span> <span class="o">=</span> <span class="n">StreamTableEnvironment</span><span class="o">.</span><span class="na">getTableEnvironment</span><span class="o">(</span><span class="n">env</span><span class="o">);</span>
+
+<span class="c1">// Provide a sample static data set of the rates history table.</span>
+<span class="n">List</span> <span class="o"><</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">>></span><span class="n">ratesHistoryData</span> <span class="o">=</span><span class="k">new</span> <span class="n">ArrayList</span><span class="o"><>();</span>
+
+<span class="n">ratesHistoryData</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Tuple2</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">"USD"</span><span class="o">,</span> <span class="mi">102L</span><span class="o">));</span>
+<span class="n">ratesHistoryData</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Tuple2</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">"EUR"</span><span class="o">,</span> <span class="mi">114L</span><span class="o">));</span>
+<span class="n">ratesHistoryData</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Tuple2</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">"YEN"</span><span class="o">,</span> <span class="mi">1L</span><span class="o">));</span>
+<span class="n">ratesHistoryData</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Tuple2</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">"EUR"</span><span class="o">,</span> <span class="mi">116L</span><span class="o">));</span>
+<span class="n">ratesHistoryData</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Tuple2</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">"USD"</span><span class="o">,</span> <span class="mi">105L</span><span class="o">));</span>
+
+<span class="c1">// Create and register an example table using the sample data set.</span>
+<span class="n">DataStream</span><span class="o"><</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">>></span> <span class="n">ratesHistoryStream</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="na">fromCollection</span><span class="o">(</span><span class="n">ratesHistoryData</span><span class="o">);</span>
+
+<span class="n">Table</span> <span class="n">ratesHistory</span> <span class="o">=</span> <span class="n">tEnv</span><span class="o">.</span><span class="na">fromDataStream</span><span class="o">(</span><span class="n">ratesHistoryStream</span><span class="o">,</span> <span class="s">"r_currency, r_rate, r_proctime.proctime"</span><span class="o">);</span>
+
+<span class="n">tEnv</span><span class="o">.</span><span class="na">registerTable</span><span class="o">(</span><span class="s">"RatesHistory"</span><span class="o">,</span> <span class="n">ratesHistory</span><span class="o">);</span>
+
+<span class="c1">// Create and register the temporal table function "rates".</span>
+<span class="c1">// Define "r_proctime" as the versioning field and "r_currency" as the primary key.</span>
+<span class="n">TemporalTableFunction</span> <span class="n">rates</span> <span class="o">=</span> <span class="n">ratesHistory</span><span class="o">.</span><span class="na">createTemporalTableFunction</span><span class="o">(</span><span class="s">"r_proctime"</span><span class="o">,</span> <span class="s">"r_currency"</span><span class="o">);</span>
+
+<span class="n">tEnv</span><span class="o">.</span><span class="na">registerFunction</span><span class="o">(</span><span class="s">"Rates"</span><span class="o">,</span> <span class="n">rates</span><span class="o">);</span>
+
+<span class="o">(...)</span></code></pre></div>
+
+<p>What does this <strong>Rates</strong> function do, in practice? Imagine we would like to check what the conversion rates looked like at a given time — say, 11:00. We could simply do something like:</p>
+
+<div class="highlight"><pre><code class="language-sql"><span class="k">SELECT</span> <span class="o">*</span> <span class="k">FROM</span> <span class="n">Rates</span><span class="p">(</span><span class="s1">'11:00'</span><span class="p">);</span></code></pre></div>
+
+<center>
+<img src="/img/blog/2019-05-13-temporal-tables/TemporalTables2.png" width="650px" alt="Point-in-time Querying" />
+</center>
+<p><br /></p>
+
+<p>Even though Flink does not yet support querying temporal table functions with a constant time attribute parameter, these functions can be used to cover a much more interesting scenario: temporal table joins.</p>
+
+<h2 id="streaming-joins-using-temporal-tables">Streaming Joins using Temporal Tables</h2>
+
+<p>Temporal tables reach their full potential when used in combination — erm, joined — with streaming data, for instance to power applications that must continuously whitelist against a reference dataset that changes over time for auditing or regulatory compliance. While efficient joins have long been an enduring challenge for query processors due to computational cost and resource consumption, joins over streaming data carry some additional challenges:</p>
+
+<ul>
+ <li>The <strong>unbounded</strong> nature of streams means that inputs are continuously evaluated and intermediate join results can consume memory resources indefinitely. Flink gracefully manages its memory consumption out-of-the-box (even for heavier cases where joins require spilling to disk) and supports time-windowed joins to bound the amount of data that needs to be kept around as state;</li>
+ <li>Streaming data might be <strong>out-of-order</strong> and <strong>late</strong>, so it is not possible to enforce an ordering upfront and time handling requires some thinking to avoid unnecessary outputs and retractions.</li>
+</ul>
+
+<p>In the particular case of temporal data, time-windowed joins are not enough (well, at least not without getting into some expensive tweaking): sooner or later, each reference record will fall outside of the window and be wiped from state, no longer being considered for future join results. To address this limitation, Flink has introduced support for temporal table joins to cover time-varying relations.</p>
+
+<center>
+<img src="/img/blog/2019-05-13-temporal-tables/TemporalTables3.png" width="500px" alt="Temporal Table Join between Taxi Fares and Conversion Rates" />
+</center>
+<p><br /></p>
+
+<p>Each record from the append-only table on the probe side (<code>Taxi Fare</code>) is joined with the version of the record from the temporal table on the build side (<code>Conversion Rate</code>) that most closely matches the probe side record time attribute (<code>time</code>) for the same value of the primary key (<code>currency</code>). Remember the temporal table function (<code>Rates</code>) we registered earlier? It can now be used to express this join as a simple SQL statement [...]
+
+<center>
+<img src="/img/blog/2019-05-13-temporal-tables/TemporalTables4.png" width="700px" alt="Regular Join vs. Temporal Table Join" />
+</center>
+<p><br /></p>
+
+<p>Temporal table joins support both <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/table/streaming/joins.html#processing-time-temporal-joins">processing</a> and <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/table/streaming/joins.html#event-time-temporal-joins">event time</a> semantics and effectively limit the amount of data kept in state while also allowing records on the build side to be arbitrarily old, as opposed to time-windowed [...]
+
+<ul>
+ <li>Narrowing the <strong>scope</strong> of the join: only the time-matching version of <code>ratesHistory</code> is visible for a given <code>taxiFare.time</code>;</li>
+ <li>Pruning <strong>unneeded records</strong> from state: for cases using event time, records between current time and the <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/event_time.html#event-time-and-watermarks">watermark</a> delay are persisted for both the probe and build side. These are discarded as soon as the watermark arrives and the results are emitted — allowing the join operation to move forward in time and the build table to “refresh” its version in [...]
+</ul>
+
+<h2 id="conclusion">Conclusion</h2>
+
+<p>All this means it is now possible to express continuous stream enrichment in relational and time-varying terms using Flink without dabbling into syntactic patchwork or compromising performance. In other words: stream time-travelling minus the flux capacitor. Extending this syntax to batch processing for enriching historic data with proper (event) time semantics is also part of the Flink roadmap!</p>
+
+<p>If you’d like to get some <strong>hands-on practice in joining streams with Flink SQL</strong> (and Flink SQL in general), checkout this <a href="https://github.com/ververica/sql-training/wiki">free training for Flink SQL</a>. The training environment is based on Docker and set up in just a few minutes.</p>
+
+<p>Subscribe to the <a href="https://flink.apache.org/community.html#mailing-lists">Apache Flink mailing lists</a> to stay up-to-date with the latest developments in this space.</p>
+
+ </article>
+ </div>
+
+ <div class="row">
+ <div id="disqus_thread"></div>
+ <script type="text/javascript">
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+ <div class="footer text-center col-sm-12">
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diff --git a/content/blog/feed.xml b/content/blog/feed.xml
index 5ea6436..59b7950 100644
--- a/content/blog/feed.xml
+++ b/content/blog/feed.xml
@@ -7,6 +7,118 @@
<atom:link href="https://flink.apache.org/blog/feed.xml" rel="self" type="application/rss+xml" />
<item>
+<title>Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</title>
+<description><p>Figuring out how to manage and model temporal data for effective point-in-time analysis was a longstanding battle, dating as far back as the early 80’s, that culminated with the introduction of temporal tables in the SQL standard in 2011. Up to that point, users were doomed to implement this as part of the application logic, often hurting the length of the development lifecycle as well as the maintainability of the code. And, although there isn’t a single, commonly [...]
+
+<center>
+<img src="/img/blog/2019-05-13-temporal-tables/TemporalTables1.png" width="500px" alt="Taxi Fares and Conversion Rates" />
+</center>
+<p><br /></p>
+
+<p><strong>For example:</strong> given a stream with Taxi Fare events tied to the local currency of the ride location, we might want to convert the fare price to a common currency for further processing. As conversion rates excel at fluctuating over time, each Taxi Fare event would need to be matched to the rate that was valid at the time the event occurred in order to produce a reliable result.</p>
+
+<h2 id="modelling-temporal-data-with-flink">Modelling Temporal Data with Flink</h2>
+
+<p>In the 1.7 release, Flink has introduced the concept of <strong>temporal tables</strong> into its streaming SQL and Table API: parameterized views on append-only tables — or, any table that only allows records to be inserted, never updated or deleted — that are interpreted as a changelog and keep data closely tied to time context, so that it can be interpreted as valid only within a specific period of time. Transforming a stream into a temporal table requires:</p>
+
+<ul>
+ <li>
+ <p>Defining a <strong>primary key</strong> and a <strong>versioning field</strong> that can be used to keep track of the changes that happen over time;</p>
+ </li>
+ <li>
+ <p>Exposing the stream as a <strong>temporal table function</strong> that maps each point in time to a static relation.</p>
+ </li>
+</ul>
+
+<p>Going back to our example use case, a temporal table is just what we need to model the conversion rate data such as to make it useful for point-in-time querying. Temporal table functions are implemented as an extension of Flink’s generic <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/table/udfs.html#table-functions">table function</a> class and can be defined in the same straightforward way to be used with the Table API or SQL pars [...]
+
+<div class="highlight"><pre><code class="language-java"><span class="kn">import</span> <span class="nn">org.apache.flink.table.functions.TemporalTableFunction</span><span class="o">;</span>
+
+<span class="o">(...)</span>
+
+<span class="c1">// Get the stream and table environments.</span>
+<span class="n">StreamExecutionEnvironment</span> <span class="n">env</span> <span class="o">=</span> <span class="n">StreamExecutionEnvironment</span><span class="o">.</span><span class="na">getExecutionEnvironment</span><span class="o">();</span>
+<span class="n">StreamTableEnvironment</span> <span class="n">tEnv</span> <span class="o">=</span> <span class="n">StreamTableEnvironment</span><span class="o">.</span><span class="na">getTableEnvironment</span><span class="o">(</span><span class="n">env</span><span class="o">);</span>
+
+<span class="c1">// Provide a sample static data set of the rates history table.</span>
+<span class="n">List</span> <span class="o">&lt;</span><span class="n">Tuple2</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">&gt;&gt;</span><span class="n">ratesHistoryData</span> <span class= [...]
+
+<span class="n">ratesHistoryData</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Tuple2</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">&quot;USD&quot;</span><span class="o"> [...]
+<span class="n">ratesHistoryData</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Tuple2</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">&quot;EUR&quot;</span><span class="o"> [...]
+<span class="n">ratesHistoryData</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Tuple2</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">&quot;YEN&quot;</span><span class="o"> [...]
+<span class="n">ratesHistoryData</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Tuple2</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">&quot;EUR&quot;</span><span class="o"> [...]
+<span class="n">ratesHistoryData</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Tuple2</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="s">&quot;USD&quot;</span><span class="o"> [...]
+
+<span class="c1">// Create and register an example table using the sample data set.</span>
+<span class="n">DataStream</span><span class="o">&lt;</span><span class="n">Tuple2</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">&gt;&gt;</span> <span class="n">ratesHistoryStream</span> <spa [...]
+
+<span class="n">Table</span> <span class="n">ratesHistory</span> <span class="o">=</span> <span class="n">tEnv</span><span class="o">.</span><span class="na">fromDataStream</span><span class="o">(</span><span class="n">ratesHistoryStream</span><span class="o">,</span> <span class="s&qu [...]
+
+<span class="n">tEnv</span><span class="o">.</span><span class="na">registerTable</span><span class="o">(</span><span class="s">&quot;RatesHistory&quot;</span><span class="o">,</span> <span class="n">ratesHistory</span><span class="o">);</span>
+
+<span class="c1">// Create and register the temporal table function &quot;rates&quot;.</span>
+<span class="c1">// Define &quot;r_proctime&quot; as the versioning field and &quot;r_currency&quot; as the primary key.</span>
+<span class="n">TemporalTableFunction</span> <span class="n">rates</span> <span class="o">=</span> <span class="n">ratesHistory</span><span class="o">.</span><span class="na">createTemporalTableFunction</span><span class="o">(</span><span class="s">&quot;r_proctime&quot;</span><span class="o"&g [...]
+
+<span class="n">tEnv</span><span class="o">.</span><span class="na">registerFunction</span><span class="o">(</span><span class="s">&quot;Rates&quot;</span><span class="o">,</span> <span class="n">rates</span><span class="o">);</span>
+
+<span class="o">(...)</span></code></pre></div>
+
+<p>What does this <strong>Rates</strong> function do, in practice? Imagine we would like to check what the conversion rates looked like at a given time — say, 11:00. We could simply do something like:</p>
+
+<div class="highlight"><pre><code class="language-sql"><span class="k">SELECT</span> <span class="o">*</span> <span class="k">FROM</span> <span class="n">Rates</span><span class="p">(</span><span class="s1">&#39;11:00&#39;</span><span class="p">);</span></code></pre></div>
+
+<center>
+<img src="/img/blog/2019-05-13-temporal-tables/TemporalTables2.png" width="650px" alt="Point-in-time Querying" />
+</center>
+<p><br /></p>
+
+<p>Even though Flink does not yet support querying temporal table functions with a constant time attribute parameter, these functions can be used to cover a much more interesting scenario: temporal table joins.</p>
+
+<h2 id="streaming-joins-using-temporal-tables">Streaming Joins using Temporal Tables</h2>
+
+<p>Temporal tables reach their full potential when used in combination — erm, joined — with streaming data, for instance to power applications that must continuously whitelist against a reference dataset that changes over time for auditing or regulatory compliance. While efficient joins have long been an enduring challenge for query processors due to computational cost and resource consumption, joins over streaming data carry some additional challenges:</p>
+
+<ul>
+ <li>The <strong>unbounded</strong> nature of streams means that inputs are continuously evaluated and intermediate join results can consume memory resources indefinitely. Flink gracefully manages its memory consumption out-of-the-box (even for heavier cases where joins require spilling to disk) and supports time-windowed joins to bound the amount of data that needs to be kept around as state;</li>
+ <li>Streaming data might be <strong>out-of-order</strong> and <strong>late</strong>, so it is not possible to enforce an ordering upfront and time handling requires some thinking to avoid unnecessary outputs and retractions.</li>
+</ul>
+
+<p>In the particular case of temporal data, time-windowed joins are not enough (well, at least not without getting into some expensive tweaking): sooner or later, each reference record will fall outside of the window and be wiped from state, no longer being considered for future join results. To address this limitation, Flink has introduced support for temporal table joins to cover time-varying relations.</p>
+
+<center>
+<img src="/img/blog/2019-05-13-temporal-tables/TemporalTables3.png" width="500px" alt="Temporal Table Join between Taxi Fares and Conversion Rates" />
+</center>
+<p><br /></p>
+
+<p>Each record from the append-only table on the probe side (<code>Taxi Fare</code>) is joined with the version of the record from the temporal table on the build side (<code>Conversion Rate</code>) that most closely matches the probe side record time attribute (<code>time</code>) for the same value of the primary key (<code>currency</code>). Remember the temporal table function (<code>Rates</code>) we registered earlier? [...]
+
+<center>
+<img src="/img/blog/2019-05-13-temporal-tables/TemporalTables4.png" width="700px" alt="Regular Join vs. Temporal Table Join" />
+</center>
+<p><br /></p>
+
+<p>Temporal table joins support both <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/table/streaming/joins.html#processing-time-temporal-joins">processing</a> and <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/table/streaming/joins.html#event-time-temporal-joins">event time</a> semantics and effectively limit the amount of data kept in state while also allowing records on the build side t [...]
+
+<ul>
+ <li>Narrowing the <strong>scope</strong> of the join: only the time-matching version of <code>ratesHistory</code> is visible for a given <code>taxiFare.time</code>;</li>
+ <li>Pruning <strong>unneeded records</strong> from state: for cases using event time, records between current time and the <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/event_time.html#event-time-and-watermarks">watermark</a> delay are persisted for both the probe and build side. These are discarded as soon as the watermark arrives and the results are emitted — allowing the join operation to move forward in time and the [...]
+</ul>
+
+<h2 id="conclusion">Conclusion</h2>
+
+<p>All this means it is now possible to express continuous stream enrichment in relational and time-varying terms using Flink without dabbling into syntactic patchwork or compromising performance. In other words: stream time-travelling minus the flux capacitor. Extending this syntax to batch processing for enriching historic data with proper (event) time semantics is also part of the Flink roadmap!</p>
+
+<p>If you’d like to get some <strong>hands-on practice in joining streams with Flink SQL</strong> (and Flink SQL in general), checkout this <a href="https://github.com/ververica/sql-training/wiki">free training for Flink SQL</a>. The training environment is based on Docker and set up in just a few minutes.</p>
+
+<p>Subscribe to the <a href="https://flink.apache.org/community.html#mailing-lists">Apache Flink mailing lists</a> to stay up-to-date with the latest developments in this space.</p>
+</description>
+<pubDate>Tue, 14 May 2019 14:00:00 +0200</pubDate>
+<link>https://flink.apache.org/2019/05/14/temporal-tables.html</link>
+<guid isPermaLink="true">/2019/05/14/temporal-tables.html</guid>
+</item>
+
+<item>
<title>When Flink & Pulsar Come Together</title>
<description><p>The open source data technology frameworks <a href="https://flink.apache.org/">Apache Flink</a> and <a href="https://pulsar.apache.org/en/">Apache Pulsar</a> can integrate in different ways to provide elastic data processing at large scale. I recently gave a talk at <a href="https://www.flink-forward.org/">Flink Forward</a> San Francisco 2019 and presented some of the integrations between the two fram [...]
diff --git a/content/blog/index.html b/content/blog/index.html
index df60f88..70a446b 100644
--- a/content/blog/index.html
+++ b/content/blog/index.html
@@ -159,6 +159,19 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></h2>
+
+ <p>14 May 2019
+ Marta Paes (<a href="https://twitter.com/morsapaes">@morsapaes</a>)</p>
+
+ <p>Apache Flink natively supports temporal table joins since the 1.7 release for straightforward temporal data handling. In this blog post, we provide an overview of how this new concept can be leveraged for effective point-in-time analysis in streaming scenarios.</p>
+
+ <p><a href="/2019/05/14/temporal-tables.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></h2>
<p>03 May 2019
@@ -287,21 +300,6 @@ for more details.</p>
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2018/12/26/release-1.5.6.html">Apache Flink 1.5.6 Released</a></h2>
-
- <p>26 Dec 2018
- </p>
-
- <p><p>The Apache Flink community released the sixth and last bugfix version of the Apache Flink 1.5 series.</p>
-
-</p>
-
- <p><a href="/news/2018/12/26/release-1.5.6.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -312,7 +310,7 @@ for more details.</p>
</li>
<li>
- <span class="page_number ">Page: 1 of 8</span>
+ <span class="page_number ">Page: 1 of 9</span>
</li>
<li>
@@ -334,6 +332,16 @@ for more details.</p>
<ul id="markdown-toc">
+ <li><a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></li>
diff --git a/content/blog/page2/index.html b/content/blog/page2/index.html
index 02b76e4..c221556 100644
--- a/content/blog/page2/index.html
+++ b/content/blog/page2/index.html
@@ -159,6 +159,21 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2018/12/26/release-1.5.6.html">Apache Flink 1.5.6 Released</a></h2>
+
+ <p>26 Dec 2018
+ </p>
+
+ <p><p>The Apache Flink community released the sixth and last bugfix version of the Apache Flink 1.5 series.</p>
+
+</p>
+
+ <p><a href="/news/2018/12/26/release-1.5.6.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2018/12/22/release-1.6.3.html">Apache Flink 1.6.3 Released</a></h2>
<p>22 Dec 2018
@@ -295,21 +310,6 @@ Please check the <a href="https://issues.apache.org/jira/secure/ReleaseNote.jspa
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2018/07/31/release-1.5.2.html">Apache Flink 1.5.2 Released</a></h2>
-
- <p>31 Jul 2018
- </p>
-
- <p><p>The Apache Flink community released the second bugfix version of the Apache Flink 1.5 series.</p>
-
-</p>
-
- <p><a href="/news/2018/07/31/release-1.5.2.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -320,7 +320,7 @@ Please check the <a href="https://issues.apache.org/jira/secure/ReleaseNote.jspa
</li>
<li>
- <span class="page_number ">Page: 2 of 8</span>
+ <span class="page_number ">Page: 2 of 9</span>
</li>
<li>
@@ -342,6 +342,16 @@ Please check the <a href="https://issues.apache.org/jira/secure/ReleaseNote.jspa
<ul id="markdown-toc">
+ <li><a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></li>
diff --git a/content/blog/page3/index.html b/content/blog/page3/index.html
index 12e5b7a..e5d3b10 100644
--- a/content/blog/page3/index.html
+++ b/content/blog/page3/index.html
@@ -159,6 +159,21 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2018/07/31/release-1.5.2.html">Apache Flink 1.5.2 Released</a></h2>
+
+ <p>31 Jul 2018
+ </p>
+
+ <p><p>The Apache Flink community released the second bugfix version of the Apache Flink 1.5 series.</p>
+
+</p>
+
+ <p><a href="/news/2018/07/31/release-1.5.2.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2018/07/12/release-1.5.1.html">Apache Flink 1.5.1 Released</a></h2>
<p>12 Jul 2018
@@ -289,24 +304,6 @@ for more detail.</p>
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2017/11/22/release-1.4-and-1.5-timeline.html">Looking Ahead to Apache Flink 1.4.0 and 1.5.0</a></h2>
-
- <p>22 Nov 2017
- Stephan Ewen (<a href="https://twitter.com/StephanEwen">@StephanEwen</a>), Aljoscha Krettek (<a href="https://twitter.com/aljoscha">@aljoscha</a>), & Mike Winters (<a href="https://twitter.com/wints">@wints</a>)</p>
-
- <p><p>The Apache Flink 1.4.0 release is on track to happen in the next couple of weeks, and for all of the
-readers out there who haven’t been following the release discussion on <a href="http://flink.apache.org/community.html#mailing-lists">Flink’s developer mailing
-list</a>, we’d like to provide some details on
-what’s coming in Flink 1.4.0 as well as a preview of what the Flink community will save for 1.5.0.</p>
-
-</p>
-
- <p><a href="/news/2017/11/22/release-1.4-and-1.5-timeline.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -317,7 +314,7 @@ what’s coming in Flink 1.4.0 as well as a preview of what the Flink community
</li>
<li>
- <span class="page_number ">Page: 3 of 8</span>
+ <span class="page_number ">Page: 3 of 9</span>
</li>
<li>
@@ -339,6 +336,16 @@ what’s coming in Flink 1.4.0 as well as a preview of what the Flink community
<ul id="markdown-toc">
+ <li><a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></li>
diff --git a/content/blog/page4/index.html b/content/blog/page4/index.html
index f505a29..e326d8d 100644
--- a/content/blog/page4/index.html
+++ b/content/blog/page4/index.html
@@ -159,6 +159,24 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2017/11/22/release-1.4-and-1.5-timeline.html">Looking Ahead to Apache Flink 1.4.0 and 1.5.0</a></h2>
+
+ <p>22 Nov 2017
+ Stephan Ewen (<a href="https://twitter.com/StephanEwen">@StephanEwen</a>), Aljoscha Krettek (<a href="https://twitter.com/aljoscha">@aljoscha</a>), & Mike Winters (<a href="https://twitter.com/wints">@wints</a>)</p>
+
+ <p><p>The Apache Flink 1.4.0 release is on track to happen in the next couple of weeks, and for all of the
+readers out there who haven’t been following the release discussion on <a href="http://flink.apache.org/community.html#mailing-lists">Flink’s developer mailing
+list</a>, we’d like to provide some details on
+what’s coming in Flink 1.4.0 as well as a preview of what the Flink community will save for 1.5.0.</p>
+
+</p>
+
+ <p><a href="/news/2017/11/22/release-1.4-and-1.5-timeline.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2017/08/05/release-1.3.2.html">Apache Flink 1.3.2 Released</a></h2>
<p>05 Aug 2017
@@ -288,19 +306,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2017/02/06/release-1.2.0.html">Announcing Apache Flink 1.2.0</a></h2>
-
- <p>06 Feb 2017 by Robert Metzger
- </p>
-
- <p><p>The Apache Flink community is excited to announce the 1.2.0 release.</p></p>
-
- <p><a href="/news/2017/02/06/release-1.2.0.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -311,7 +316,7 @@
</li>
<li>
- <span class="page_number ">Page: 4 of 8</span>
+ <span class="page_number ">Page: 4 of 9</span>
</li>
<li>
@@ -333,6 +338,16 @@
<ul id="markdown-toc">
+ <li><a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></li>
diff --git a/content/blog/page5/index.html b/content/blog/page5/index.html
index e1b1eb3..2571139 100644
--- a/content/blog/page5/index.html
+++ b/content/blog/page5/index.html
@@ -159,6 +159,19 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2017/02/06/release-1.2.0.html">Announcing Apache Flink 1.2.0</a></h2>
+
+ <p>06 Feb 2017 by Robert Metzger
+ </p>
+
+ <p><p>The Apache Flink community is excited to announce the 1.2.0 release.</p></p>
+
+ <p><a href="/news/2017/02/06/release-1.2.0.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2016/12/21/release-1.1.4.html">Apache Flink 1.1.4 Released</a></h2>
<p>21 Dec 2016
@@ -290,21 +303,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2016/04/22/release-1.0.2.html">Flink 1.0.2 Released</a></h2>
-
- <p>22 Apr 2016
- </p>
-
- <p><p>Today, the Flink community released Flink version <strong>1.0.2</strong>, the second bugfix release of the 1.0 series.</p>
-
-</p>
-
- <p><a href="/news/2016/04/22/release-1.0.2.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -315,7 +313,7 @@
</li>
<li>
- <span class="page_number ">Page: 5 of 8</span>
+ <span class="page_number ">Page: 5 of 9</span>
</li>
<li>
@@ -337,6 +335,16 @@
<ul id="markdown-toc">
+ <li><a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></li>
diff --git a/content/blog/page6/index.html b/content/blog/page6/index.html
index a8bf38f..a7f5054 100644
--- a/content/blog/page6/index.html
+++ b/content/blog/page6/index.html
@@ -159,6 +159,21 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2016/04/22/release-1.0.2.html">Flink 1.0.2 Released</a></h2>
+
+ <p>22 Apr 2016
+ </p>
+
+ <p><p>Today, the Flink community released Flink version <strong>1.0.2</strong>, the second bugfix release of the 1.0 series.</p>
+
+</p>
+
+ <p><a href="/news/2016/04/22/release-1.0.2.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2016/04/14/flink-forward-announce.html">Flink Forward 2016 Call for Submissions Is Now Open</a></h2>
<p>14 Apr 2016 by Aljoscha Krettek (<a href="https://twitter.com/">@aljoscha</a>)
@@ -286,21 +301,6 @@
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2015/11/16/release-0.10.0.html">Announcing Apache Flink 0.10.0</a></h2>
-
- <p>16 Nov 2015
- </p>
-
- <p><p>The Apache Flink community is pleased to announce the availability of the 0.10.0 release. The community put significant effort into improving and extending Apache Flink since the last release, focusing on data stream processing and operational features. About 80 contributors provided bug fixes, improvements, and new features such that in total more than 400 JIRA issues could be resolved.</p>
-
-</p>
-
- <p><a href="/news/2015/11/16/release-0.10.0.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -311,7 +311,7 @@
</li>
<li>
- <span class="page_number ">Page: 6 of 8</span>
+ <span class="page_number ">Page: 6 of 9</span>
</li>
<li>
@@ -333,6 +333,16 @@
<ul id="markdown-toc">
+ <li><a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></li>
diff --git a/content/blog/page7/index.html b/content/blog/page7/index.html
index c8c20b5..0ce07c5 100644
--- a/content/blog/page7/index.html
+++ b/content/blog/page7/index.html
@@ -159,6 +159,21 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2015/11/16/release-0.10.0.html">Announcing Apache Flink 0.10.0</a></h2>
+
+ <p>16 Nov 2015
+ </p>
+
+ <p><p>The Apache Flink community is pleased to announce the availability of the 0.10.0 release. The community put significant effort into improving and extending Apache Flink since the last release, focusing on data stream processing and operational features. About 80 contributors provided bug fixes, improvements, and new features such that in total more than 400 JIRA issues could be resolved.</p>
+
+</p>
+
+ <p><a href="/news/2015/11/16/release-0.10.0.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2015/09/16/off-heap-memory.html">Off-heap Memory in Apache Flink and the curious JIT compiler</a></h2>
<p>16 Sep 2015 by Stephan Ewen (<a href="https://twitter.com/">@stephanewen</a>)
@@ -301,19 +316,6 @@ release is a preview release that contains known issues.</p>
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html">Peeking into Apache Flink's Engine Room</a></h2>
-
- <p>13 Mar 2015 by Fabian Hüske (<a href="https://twitter.com/">@fhueske</a>)
- </p>
-
- <p>Joins are prevalent operations in many data processing applications. Most data processing systems feature APIs that make joining data sets very easy. However, the internal algorithms for join processing are much more involved – especially if large data sets need to be efficiently handled. In this blog post, we cut through Apache Flink’s layered architecture and take a look at its internals with a focus on how it handles joins.</p>
-
- <p><a href="/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -324,7 +326,7 @@ release is a preview release that contains known issues.</p>
</li>
<li>
- <span class="page_number ">Page: 7 of 8</span>
+ <span class="page_number ">Page: 7 of 9</span>
</li>
<li>
@@ -346,6 +348,16 @@ release is a preview release that contains known issues.</p>
<ul id="markdown-toc">
+ <li><a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></li>
diff --git a/content/blog/page8/index.html b/content/blog/page8/index.html
index 9e45979..b9984bb 100644
--- a/content/blog/page8/index.html
+++ b/content/blog/page8/index.html
@@ -159,6 +159,19 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html">Peeking into Apache Flink's Engine Room</a></h2>
+
+ <p>13 Mar 2015 by Fabian Hüske (<a href="https://twitter.com/">@fhueske</a>)
+ </p>
+
+ <p>Joins are prevalent operations in many data processing applications. Most data processing systems feature APIs that make joining data sets very easy. However, the internal algorithms for join processing are much more involved – especially if large data sets need to be efficiently handled. In this blog post, we cut through Apache Flink’s layered architecture and take a look at its internals with a focus on how it handles joins.</p>
+
+ <p><a href="/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2015/03/02/february-2015-in-flink.html">February 2015 in the Flink community</a></h2>
<p>02 Mar 2015
@@ -300,24 +313,6 @@ and offers a new API including definition of flexible windows.</p>
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2014/08/26/release-0.6.html">Apache Flink 0.6 available</a></h2>
-
- <p>26 Aug 2014
- </p>
-
- <p><p>We are happy to announce the availability of Flink 0.6. This is the
-first release of the system inside the Apache Incubator and under the
-name Flink. Releases up to 0.5 were under the name Stratosphere, the
-academic and open source project that Flink originates from.</p>
-
-</p>
-
- <p><a href="/news/2014/08/26/release-0.6.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -328,11 +323,11 @@ academic and open source project that Flink originates from.</p>
</li>
<li>
- <span class="page_number ">Page: 8 of 8</span>
+ <span class="page_number ">Page: 8 of 9</span>
</li>
<li>
- <span>Next</span>
+ <a href="/blog/page9" class="next">Next</a>
</li>
</ul>
@@ -350,6 +345,16 @@ academic and open source project that Flink originates from.</p>
<ul id="markdown-toc">
+ <li><a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></li>
diff --git a/content/blog/page2/index.html b/content/blog/page9/index.html
similarity index 81%
copy from content/blog/page2/index.html
copy to content/blog/page9/index.html
index 02b76e4..a28fdbf 100644
--- a/content/blog/page2/index.html
+++ b/content/blog/page9/index.html
@@ -159,153 +159,19 @@
<!-- Blog posts -->
<article>
- <h2 class="blog-title"><a href="/news/2018/12/22/release-1.6.3.html">Apache Flink 1.6.3 Released</a></h2>
+ <h2 class="blog-title"><a href="/news/2014/08/26/release-0.6.html">Apache Flink 0.6 available</a></h2>
- <p>22 Dec 2018
+ <p>26 Aug 2014
</p>
- <p><p>The Apache Flink community released the third bugfix version of the Apache Flink 1.6 series.</p>
+ <p><p>We are happy to announce the availability of Flink 0.6. This is the
+first release of the system inside the Apache Incubator and under the
+name Flink. Releases up to 0.5 were under the name Stratosphere, the
+academic and open source project that Flink originates from.</p>
</p>
- <p><a href="/news/2018/12/22/release-1.6.3.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
- <article>
- <h2 class="blog-title"><a href="/news/2018/12/21/release-1.7.1.html">Apache Flink 1.7.1 Released</a></h2>
-
- <p>21 Dec 2018
- </p>
-
- <p><p>The Apache Flink community released the first bugfix version of the Apache Flink 1.7 series.</p>
-
-</p>
-
- <p><a href="/news/2018/12/21/release-1.7.1.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
- <article>
- <h2 class="blog-title"><a href="/news/2018/11/30/release-1.7.0.html">Apache Flink 1.7.0 Release Announcement</a></h2>
-
- <p>30 Nov 2018
- Till Rohrmann (<a href="https://twitter.com/stsffap">@stsffap</a>)</p>
-
- <p><p>The Apache Flink community is pleased to announce Apache Flink 1.7.0.
-The latest release includes more than 420 resolved issues and some exciting additions to Flink that we describe in the following sections of this post.
-Please check the <a href="https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12315522&version=12343585">complete changelog</a> for more details.</p>
-
-</p>
-
- <p><a href="/news/2018/11/30/release-1.7.0.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
- <article>
- <h2 class="blog-title"><a href="/news/2018/10/29/release-1.6.2.html">Apache Flink 1.6.2 Released</a></h2>
-
- <p>29 Oct 2018
- </p>
-
- <p><p>The Apache Flink community released the second bugfix version of the Apache Flink 1.6 series.</p>
-
-</p>
-
- <p><a href="/news/2018/10/29/release-1.6.2.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
- <article>
- <h2 class="blog-title"><a href="/news/2018/10/29/release-1.5.5.html">Apache Flink 1.5.5 Released</a></h2>
-
- <p>29 Oct 2018
- </p>
-
- <p><p>The Apache Flink community released the fifth bugfix version of the Apache Flink 1.5 series.</p>
-
-</p>
-
- <p><a href="/news/2018/10/29/release-1.5.5.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
- <article>
- <h2 class="blog-title"><a href="/news/2018/09/20/release-1.6.1.html">Apache Flink 1.6.1 Released</a></h2>
-
- <p>20 Sep 2018
- </p>
-
- <p><p>The Apache Flink community released the first bugfix version of the Apache Flink 1.6 series.</p>
-
-</p>
-
- <p><a href="/news/2018/09/20/release-1.6.1.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
- <article>
- <h2 class="blog-title"><a href="/news/2018/09/20/release-1.5.4.html">Apache Flink 1.5.4 Released</a></h2>
-
- <p>20 Sep 2018
- </p>
-
- <p><p>The Apache Flink community released the fourth bugfix version of the Apache Flink 1.5 series.</p>
-
-</p>
-
- <p><a href="/news/2018/09/20/release-1.5.4.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
- <article>
- <h2 class="blog-title"><a href="/news/2018/08/21/release-1.5.3.html">Apache Flink 1.5.3 Released</a></h2>
-
- <p>21 Aug 2018
- </p>
-
- <p><p>The Apache Flink community released the third bugfix version of the Apache Flink 1.5 series.</p>
-
-</p>
-
- <p><a href="/news/2018/08/21/release-1.5.3.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
- <article>
- <h2 class="blog-title"><a href="/news/2018/08/09/release-1.6.0.html">Apache Flink 1.6.0 Release Announcement</a></h2>
-
- <p>09 Aug 2018
- Till Rohrmann (<a href="https://twitter.com/stsffap">@stsffap</a>)</p>
-
- <p><p>The Apache Flink community is proud to announce the 1.6.0 release. Over the past 2 months, the Flink community has worked hard to resolve more than 360 issues. Please check the <a href="https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12315522&version=12342760">complete changelog</a> for more details.</p>
-
-</p>
-
- <p><a href="/news/2018/08/09/release-1.6.0.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
- <article>
- <h2 class="blog-title"><a href="/news/2018/07/31/release-1.5.2.html">Apache Flink 1.5.2 Released</a></h2>
-
- <p>31 Jul 2018
- </p>
-
- <p><p>The Apache Flink community released the second bugfix version of the Apache Flink 1.5 series.</p>
-
-</p>
-
- <p><a href="/news/2018/07/31/release-1.5.2.html">Continue reading »</a></p>
+ <p><a href="/news/2014/08/26/release-0.6.html">Continue reading »</a></p>
</article>
<hr>
@@ -316,15 +182,15 @@ Please check the <a href="https://issues.apache.org/jira/secure/ReleaseNote.jspa
<ul class="pager">
<li>
- <a href="/blog" class="previous">Previous</a>
+ <a href="/blog/page8" class="previous">Previous</a>
</li>
<li>
- <span class="page_number ">Page: 2 of 8</span>
+ <span class="page_number ">Page: 9 of 9</span>
</li>
<li>
- <a href="/blog/page3" class="next">Next</a>
+ <span>Next</span>
</li>
</ul>
@@ -342,6 +208,16 @@ Please check the <a href="https://issues.apache.org/jira/secure/ReleaseNote.jspa
<ul id="markdown-toc">
+ <li><a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></li>
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diff --git a/content/index.html b/content/index.html
index 601e099..4c7ab41 100644
--- a/content/index.html
+++ b/content/index.html
@@ -448,6 +448,9 @@
<dl>
+ <dt> <a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></dt>
+ <dd>Apache Flink natively supports temporal table joins since the 1.7 release for straightforward temporal data handling. In this blog post, we provide an overview of how this new concept can be leveraged for effective point-in-time analysis in streaming scenarios.</dd>
+
<dt> <a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></dt>
<dd>Apache Flink and Apache Pulsar are distributed data processing systems. When combined, they offer elastic data processing at large scale. This post describes how Pulsar and Flink can work together to provide a seamless developer experience.</dd>
@@ -467,9 +470,6 @@ for more details.</p>
<dt> <a href="/features/2019/03/11/prometheus-monitoring.html">Flink and Prometheus: Cloud-native monitoring of streaming applications</a></dt>
<dd>This blog post describes how developers can leverage Apache Flink's built-in metrics system together with Prometheus to observe and monitor streaming applications in an effective way.</dd>
-
- <dt> <a href="/news/2019/03/06/ffsf-preview.html">What to expect from Flink Forward San Francisco 2019</a></dt>
- <dd>The third annual Flink Forward conference in San Francisco is just a few weeks away. Let's see what Flink Forward SF 2019 has in store for the Apache Flink and stream processing communities. This post covers some of its highlights!</dd>
</dl>
diff --git a/content/zh/index.html b/content/zh/index.html
index 8c98651..027ccec 100644
--- a/content/zh/index.html
+++ b/content/zh/index.html
@@ -446,6 +446,9 @@
<dl>
+ <dt> <a href="/2019/05/14/temporal-tables.html">Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL</a></dt>
+ <dd>Apache Flink natively supports temporal table joins since the 1.7 release for straightforward temporal data handling. In this blog post, we provide an overview of how this new concept can be leveraged for effective point-in-time analysis in streaming scenarios.</dd>
+
<dt> <a href="/2019/05/03/pulsar-flink.html">When Flink & Pulsar Come Together</a></dt>
<dd>Apache Flink and Apache Pulsar are distributed data processing systems. When combined, they offer elastic data processing at large scale. This post describes how Pulsar and Flink can work together to provide a seamless developer experience.</dd>
@@ -465,9 +468,6 @@ for more details.</p>
<dt> <a href="/features/2019/03/11/prometheus-monitoring.html">Flink and Prometheus: Cloud-native monitoring of streaming applications</a></dt>
<dd>This blog post describes how developers can leverage Apache Flink's built-in metrics system together with Prometheus to observe and monitor streaming applications in an effective way.</dd>
-
- <dt> <a href="/news/2019/03/06/ffsf-preview.html">What to expect from Flink Forward San Francisco 2019</a></dt>
- <dd>The third annual Flink Forward conference in San Francisco is just a few weeks away. Let's see what Flink Forward SF 2019 has in store for the Apache Flink and stream processing communities. This post covers some of its highlights!</dd>
</dl>