<commit_before>DataTorrent RTS Release Notes
========================================================================================================================

Version: 3.5.0
Release date: Sep 26, 2016
------------------------------------------------------------------------------------------------------------------------

### Summary
DataTorrent RTS continues to deliver features that sets it apart in bringing operability in running an enterprise grade big data-in-motion platform. This particular release brought new features such as

* Allowing users to analyze "stuck operator" by obtaining stats from the running JVM (i.e. GC stats and thread dump)
* Ability to show/hide critical path in both logical and physical DAG

### Apache Apex Malhar
The other important part of going to production is a library of operators that is more than just functional. They need to be fault tolerant, partitionable, support idempotency, and dynanically scalable. The recent release of Apache Apex Malhar 3.5.0 provides new and updated operators and APIs to bring those enterprise features. 

* Windowed Operator that supports the windowing semantics outlined by Apache Beam and Google Cloud DataFlow, including the concepts of event time windows, session windows, watermarks, allowed lateness, and triggering.
* High level Java stream API now uses the aforementioned Windowed Operator to support stateful transformation with Apache Beam style windowing semantics.
* Introduction of Spillable Data Structures that make use of Managed State.
* Deduper Operator to process  whether a given record is a duplicate or not<commit_msg>Update release_notes.md (#87)<commit_after>DataTorrent RTS Release Notes
========================================================================================================================

Version: 3.5.0
Release date: Sep 26, 2016
------------------------------------------------------------------------------------------------------------------------

### Summary
DataTorrent RTS continues to deliver features that sets it apart in bringing operability in running an enterprise grade big data-in-motion platform. This particular release brought new features such as

* Allowing users to analyze "stuck operator" by obtaining stats from the running JVM (i.e. GC stats and thread dump)
* Ability to show/hide critical path in both logical and physical DAG

### Apache Apex Malhar
The other important part of going to production is a library of operators that is more than just functional. They need to be fault tolerant, partitionable, support idempotency, and dynamically scalable. The recent release of Apache Apex Malhar 3.5.0 provides new and updated operators and APIs to bring those enterprise features. 

* Windowed Operator that supports the windowing semantics outlined by Apache Beam and Google Cloud DataFlow, including the concepts of event time windows, session windows, watermarks, allowed lateness, and triggering.
* High level Java stream API now uses the aforementioned Windowed Operator to support stateful transformation with Apache Beam style windowing semantics.
* Introduction of Spillable Data Structures that make use of Managed State.
* Deduper Operator to process  whether a given record is a duplicate or not
{'fraction_non_alphanumeric': 0.17647058823529413, 'fraction_numerical': 0.007965686274509803, 'mean_word_length': 3.6246458923512748, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7209415', 'n_tokens_mistral': 702, 'n_tokens_neox': 637, 'n_words': 399}