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Posted to commits@apex.apache.org by th...@apache.org on 2016/09/29 02:56:32 UTC
apex-site git commit: from d3047a863e02e79c58b5f07c0b04d572df5a2a89
Repository: apex-site
Updated Branches:
refs/heads/asf-site 78858fe9c -> 90c853881
from d3047a863e02e79c58b5f07c0b04d572df5a2a89
Project: http://git-wip-us.apache.org/repos/asf/apex-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/apex-site/commit/90c85388
Tree: http://git-wip-us.apache.org/repos/asf/apex-site/tree/90c85388
Diff: http://git-wip-us.apache.org/repos/asf/apex-site/diff/90c85388
Branch: refs/heads/asf-site
Commit: 90c853881f03ea3ab0bd5902acbf34b25218a61e
Parents: 78858fe
Author: Thomas Weise <th...@apache.org>
Authored: Wed Sep 28 19:56:20 2016 -0700
Committer: Thomas Weise <th...@apache.org>
Committed: Wed Sep 28 19:56:20 2016 -0700
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content/docs.html | 5 +--
content/roadmap.html | 91 +++++++++++++++++++++++++++++++++--------------
2 files changed, 68 insertions(+), 28 deletions(-)
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http://git-wip-us.apache.org/repos/asf/apex-site/blob/90c85388/content/docs.html
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diff --git a/content/docs.html b/content/docs.html
index e405e95..15a4af7 100644
--- a/content/docs.html
+++ b/content/docs.html
@@ -99,8 +99,9 @@ dimensional computations on a simulated sales data stream.</li>
<li><a href="https://www.youtube.com/watch?v=98EW5NGM3u0">Next Gen Decision Making in < 2ms</a> A video discussing CapitalOne's experience with Apache Apex and evaluation of competing technologies along with the <a href="http://www.slideshare.net/ApacheApex/capital-ones-next-generation-decision-in-less-than-2-ms">slides</a>. </li>
<li><a href="https://www.youtube.com/watch?v=EdBiOnQn3Gw">Apache Nifi Integration with Apex</a> video and <a href="http://www.slideshare.net/ApacheApex/integrating-ni-fiandapex-by-bryan-bende">slide deck</a>.</li>
<li><a href="https://www.brighttalk.com/webcast/13685/190407">Introducing Apache Apex</a> A webinar that begins with the historical context for the rise of Hadoop and Big Data, discusses why the promise of Hadoop remains largely unfulfilled and why moving beyond Map-Reduce model is essential and why operability is critically important. It continues with a discussion of the programming model, the various components of a running application on a YARN cluster and the large library of operators and connectors available with Apache Apex for reading data from and writing data to external systems. Concludes with a brief description of the visualization dashboards.</li>
-<li><a href="http://www.slideshare.net/PramodImmaneni/meetup-59089806">Stream Processing with Apache Apex</a> A broad overview slide deck covering topics such as windowing, static and dynamic partitioning, unification, fault tolerance, locality, monitoring, etc.</li>
-<li><a href="https://www.brighttalk.com/webcast/13685/194115">Fault Tolerance and Processing Semantics</a> A webinar and associated <a href="http://www.slideshare.net/ApacheApexOrganizer/webinar-fault-toleranceandprocessingsemantics">slides</a> covering core Apache Apex features including checkpointing and fault tolerance with fast, incremental recovery via a buffer server which uses a publish-subscribe model for inter-operator data transport. A variety of failure scenarios and processing guarantees are discussed.</li>
+<li><a href="https://www.youtube.com/watch?v=1DVMSRTNdIQ">Stream Processing with Apache Apex</a> <a href="http://www.slideshare.net/ApacheApex/hadoop-summit-sj-2016-next-gen-big-data-analytics-with-apache-apex">(slides)</a> A broad overview slide deck covering topics such as windowing, static and dynamic partitioning, unification, fault tolerance, locality, monitoring, etc.</li>
+<li><a href="https://www.youtube.com/watch?v=FCMY6Ii89Nw">Fault Tolerance and Processing Semantics</a> <a href="http://www.slideshare.net/ApacheApexOrganizer/webinar-fault-toleranceandprocessingsemantics">(slides)</a> A webinar covering core Apache Apex features including checkpointing and fault tolerance with fast, incremental recovery via a buffer server which uses a publish-subscribe model for inter-operator data transport. A variety of failure scenarios and processing guarantees are discussed.</li>
+<li><a href="https://www.youtube.com/watch?v=kJWMajIjGG0">Smart Partitioning with Apache Apex</a> <a href="http://www.slideshare.net/ApacheApex/smart-partitioning-with-apache-apex-webinar">(slides)</a> Webinar covering partitioning, including unique Apex features such as elasticity with dynamic resource allocation, parallel partitions for speculative execution and processing SLA etc.</li>
<li><a href="http://www.slideshare.net/DevendraVyavahare/windowing-in-apex">Windows in Apache Apex</a> Discusses the various flavors of windows available in Apache Apex and how to configure and
use them via callbacks. Contrasts windows with micro-batches.</li>
<li><a href="http://www.slideshare.net/DevendraVyavahare/batch-processing-vs-real-time-data-processing-streaming">Real Time Stream Processing Versus Batch</a> Slide deck compares and contrasts the needs, use cases and challenges of stream processing with those of batch processing.</li>
http://git-wip-us.apache.org/repos/asf/apex-site/blob/90c85388/content/roadmap.html
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diff --git a/content/roadmap.html b/content/roadmap.html
index 8371f65..5bee01a 100644
--- a/content/roadmap.html
+++ b/content/roadmap.html
@@ -275,6 +275,36 @@ http://mesos.apache.org/documentation/latest/fetcher/
</td>
</tr>
+ <tr>
+ <td>
+ <a target="_blank" href="https://issues.apache.org/jira/browse/APEXCORE-498">APEXCORE-498</a>
+ </td>
+ <td title="Named Checkpoints
+
+1. Ability to tag/name the checkpoints
+2. On demand - checkpoint the DAG
+3. Start the app from the named checkpoints
+
+All checkpoints that happened before the committed window is deleted but the named checkpoints won't be deleted.">
+ Named Checkpoints - Checkpoint the DAG with a name/tag and start the app from that point
+ </td>
+ <td>
+
+
+ </td>
+ </tr>
+ <tr>
+ <td>
+ <a target="_blank" href="https://issues.apache.org/jira/browse/APEXCORE-536">APEXCORE-536</a>
+ </td>
+ <td title="Currently Apex depends on Hadoop 2.2 and runs on all later 2.x version. Hadoop 2.2 is quite old, most Apex users have more recent Hadoop installs. Latest distro releases are based on 2.6 and 2.7. There are several important features that were added in Hadoop since 2.2 that Apex should be able to leverage.">
+ Upgrade Hadoop dependency
+ </td>
+ <td>
+
+
+ </td>
+ </tr>
</tbody>
</table>
@@ -370,40 +400,49 @@ This jira item can contain tasks for providing similar support in Apex">
</tr>
<tr>
<td>
- <a target="_blank" href="https://issues.apache.org/jira/browse/APEXMALHAR-2026">APEXMALHAR-2026</a>
+ <a target="_blank" href="https://issues.apache.org/jira/browse/APEXMALHAR-2089">APEXMALHAR-2089</a>
</td>
- <td title="Add libraryies for spooling datastructures to a key value store. There are several customer use cases which require spooled data structures.
-
-1 - Some operators like AbstractFileInputOperator have ever growing state. This is an issue because eventually the state of the operator will grow larger than the memory allocated to the operator, which will cause the operator to perpetually fail. However if the operator's datastructures are spooled then the operator will never run out of memory.
-
-2 - Some users have requested for the ability to maintain a map as well as a list of keys over which to iterate. Most key value stores don't provide this functionality. However, with spooled datastructures this functionality can be provided by maintaining a spooled map and an iterable set of keys.
-
-3 - Some users have requested building graph databases within APEX. This would require implementing a spooled graph data structure.
-
-4 - Another use case for spooled data structures is database operators. Database operators need to write data to a data base, but sometimes the database is down. In this case most of the database operators repeatedly fail until the database comes back up. In order to avoid constant failures the database operator need to writes data to a queue when the data base is down, then when the database is up the operator need to take data from the queue and write it to the database. In the case of a database failure this queue will grow larger than the total amount of memory available to the operator, so the queue should be spooled in order to prevent the operator from failing.
-
-5 - Any operator which needs to maintain a large data structure in memory currently needs to have that data serialized and written out to HDFS with every checkpoint. This is costly when the data structure is large. If the data structure is spooled, then only the changes to the data structure are written out to HDFS instead of the entire data structure.
-
-6 - Also building an Apex Native database for aggregations requires indices. These indices need to take the form of spooled data structures.
+ <td title="Apex should provide a runner for Beam. This ticket is a proxy for BEAM-261 as the implementation should probably live in the Beam repository.
+">
+ Apache Beam support
+ </td>
+ <td>
+
+
+ </td>
+ </tr>
+ <tr>
+ <td>
+ <a target="_blank" href="https://issues.apache.org/jira/browse/APEXMALHAR-2130">APEXMALHAR-2130</a>
+ </td>
+ <td title="This feature is used for supporting windowing.
-7 - In the future any operator which needs to maintain a data structure larger than the memory available to it will need to spool the data structure.">
- Spill-able Datastructures
+The storage needs to have the following features:
+1. Spillable key value storage (integrate with APEXMALHAR-2026)
+2. Upon checkpoint, it saves a snapshot for the entire data set with the checkpointing window id. This should be done incrementally (ManagedState) to avoid wasting space with unchanged data
+3. When recovering, it takes the recovery window id and restores to that snapshot
+4. When a window is committed, all windows with a lower ID should be purged from the store.
+5. It should implement the WindowedStorage and WindowedKeyedStorage interfaces, and because of 2 and 3, we may want to add methods to the WindowedStorage interface so that the implementation of WindowedOperator can notify the storage of checkpointing, recovering and committing of a window.
+">
+ Scalable windowed storage
</td>
<td>
- <a target="_blank" href="https://issues.apache.org/jira/browse/APEXMALHAR/fixforversion/12335815">3.5.0</a>
+ <a target="_blank" href="https://issues.apache.org/jira/browse/APEXMALHAR/fixforversion/12338174">3.6.0</a>
</td>
</tr>
<tr>
<td>
- <a target="_blank" href="https://issues.apache.org/jira/browse/APEXMALHAR-2089">APEXMALHAR-2089</a>
+ <a target="_blank" href="https://issues.apache.org/jira/browse/APEXMALHAR-2260">APEXMALHAR-2260</a>
</td>
- <td title="Apex should provide a runner for Beam. This ticket is a proxy for BEAM-261 as the implementation should probably live in the Beam repository.
+ <td title="Support execution of Python code in an operator.
+
+https://lists.apache.org/thread.html/9837b1dee8f909ed400c6030ce5c6a94a12f43183718019dd0bfd228@%3Cdev.apex.apache.org%3E
">
- Apache Beam support
+ Python execution for operator logic
</td>
<td>
@@ -412,17 +451,17 @@ This jira item can contain tasks for providing similar support in Apex">
</tr>
<tr>
<td>
- <a target="_blank" href="https://issues.apache.org/jira/browse/APEXMALHAR-2142">APEXMALHAR-2142</a>
+ <a target="_blank" href="https://issues.apache.org/jira/browse/APEXMALHAR-2261">APEXMALHAR-2261</a>
</td>
- <td title="">
- High-level API window support
+ <td title="A high level API similar to the Apex Java stream API that lets users specify an application in Python.
+
+https://lists.apache.org/thread.html/9837b1dee8f909ed400c6030ce5c6a94a12f43183718019dd0bfd228@%3Cdev.apex.apache.org%3E
+">
+ Python binding for high level API
</td>
<td>
- <a target="_blank" href="https://issues.apache.org/jira/browse/APEXMALHAR/fixforversion/12335815">3.5.0</a>
-
-
</td>
</tr>
</tbody>