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The following commit(s) were added to refs/heads/master by this push:
     new 02c9e04  MASC blog post (#221)
02c9e04 is described below

commit 02c9e047f135e89e650478cd6f6d059ce97c5f7e
Author: Scott Graham <5720537+gramha...@users.noreply.github.com>
AuthorDate: Wed Feb 26 17:24:41 2020 -0500

    MASC blog post (#221)
    
    
    Co-authored-by: Markus Cozowicz <marc...@microsoft.com>
---
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diff --git a/_posts/blog/2020-02-26-accumulo-spark-connector.md 
b/_posts/blog/2020-02-26-accumulo-spark-connector.md
new file mode 100644
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--- /dev/null
+++ b/_posts/blog/2020-02-26-accumulo-spark-connector.md
@@ -0,0 +1,192 @@
+---
+title: Microsoft MASC, an Apache Spark connector for Apache Accumulo
+author: Markus Cozowicz, Scott Graham
+---
+
+# Overview
+[MASC](https://github.com/microsoft/masc) provides an Apache Spark native 
connector for Apache Accumulo to integrate the rich Spark machine learning 
eco-system with the scalable and secure data storage capabilities of Accumulo. 
+
+## Major Features
+- Simplified Spark DataFrame read/write to Accumulo using DataSource v2 API
+- Speedup of 2-5x over existing approaches for pulling key-value data into 
DataFrame format
+- Scala and Python support without overhead for moving between languages
+- Process streaming data from Accumulo without loading it all into Spark memory
+- Push down filtering with a flexible expression language 
([JUEL](http://juel.sourceforge.net/)): user can define logical operators and 
comparisons to reduce the amount of data returned from Accumulo 
+- Column pruning based on selected fields transparently reduces the amount of 
data returned from Accumulo
+- Server side inference: ML model inference can run on the Accumulo nodes 
using MLeap to increase the scalability of AI solutions as well as keeping data 
in Accumulo
+
+## Use-cases
+MASC is advantageous in many use-cases, below we list a few.
+
+**Scenario 1**: A data analyst needs to execute model inference on large 
amount of data in Accumulo.<br>
+**Benefit**: Instead of transferring all the data to a large Spark cluster to 
score using a Spark model, the connector exports and runs the model on the 
Accumulo cluster. This reduces the need for a large Spark cluster as well as 
the amount of data transferred between systems, and can improve inference 
speeds (>2x speedups observed).
+
+**Scenario 2**: A data scientist needs to train a Spark model on a large 
amount of data in Accumulo.<br>
+**Benefit**: Instead of pulling all the data into a large Spark cluster and 
restructuring the format to use Spark ML Lib tools, the connector streams data 
into Spark as a DataFrame reducing time to train and Spark cluster size / 
memory requirements.
+
+**Scenario 3**: A data analyst needs to perform ad hoc analysis on large 
amounts of data stored in Accumulo.<br>
+**Benefit**: Instead of pulling all the data into a large Spark cluster, the 
connector prunes rows and columns using pushdown filtering with a flexible 
expression language.
+
+# Architecture
+The Accumulo-Spark connector is composed of two components:
+
+- Accumulo server-side iterator performs
+  - column pruning
+  - row-based filtering
+  - [MLeap](https://github.com/combust/mleap) ML model inference and
+  - row assembly using [Apache AVRO](https://avro.apache.org/)
+- Spark DataSource V2 
+  - determines the number of Spark tasks based on available Accumulo table 
splits
+  - translates Spark filter conditions into a 
[JUEL](http://juel.sourceforge.net/) expression
+  - configures the Accumulo iterator
+  - deserializes the AVRO payload
+
+![Architecture](/images/blog/202002_masc/architecture.svg "MASC Architecture 
Diagram")
+
+# Usage
+More detailed documentation on installation and use is available in the 
+[Connector 
documentation](https://github.com/microsoft/masc/blob/master/connector/README.md)
+
+## Dependencies
+- Java 8
+- Spark 2.4.3+
+- Accumulo 2.0.0+
+
+JARs available on Maven Central Repository:
+- [![Maven 
Central](https://img.shields.io/maven-central/v/com.microsoft.masc/microsoft-accumulo-spark-datasource.svg?label=Maven%20Central)
 Spark 
DataSource](https://search.maven.org/search?q=g:%22com.microsoft.masc%22%20AND%20a:%22microsoft-accumulo-spark-datasource%22)
+
+- [![Maven 
Central](https://img.shields.io/maven-central/v/com.microsoft.masc/microsoft-accumulo-spark-iterator.svg?label=Maven%20Central)
 Accumulo 
Iterator](https://search.maven.org/search?q=g:%22com.microsoft.masc%22%20AND%20a:%22microsoft-accumulo-spark-iterator%22)
 - Backend for Spark DataSource
+
+## Example use
+```python
+from configparser import ConfigParser
+from pyspark.sql import types as T
+
+def get_properties(properties_file):
+    """Read Accumulo client properties file"""
+    config = ConfigParser()
+    with open(properties_file) as stream:
+        config.read_string("[top]\n" + stream.read())
+    return dict(config['top'])
+
+properties = 
get_properties('/opt/muchos/install/accumulo-2.0.0/conf/accumulo-client.properties')
+properties['table'] = 'demo_table' # Define Accumulo table where data will be 
written
+properties['rowkey'] = 'id'        # Identify column to use as the key for 
Accumulo rows
+
+# define the schema
+schema = T.StructType([
+  T.StructField("sentiment", T.IntegerType(), True),
+  T.StructField("date", T.StringType(), True),
+  T.StructField("query_string", T.StringType(), True),
+  T.StructField("user", T.StringType(), True),
+  T.StructField("text", T.StringType(), True)
+])
+
+# Read from Accumulo
+df = (spark
+      .read
+      .format("com.microsoft.accumulo")
+      .options(**options)  # define Accumulo properties
+      .schema(schema))     # define schema for data retrieval
+
+# Write to Accumulo
+properties['table'] = 'output_table'
+
+(df
+ .write
+ .format("com.microsoft.accumulo")
+ .options(**options)
+ .save())
+```
+
+See the [demo 
notebook](https://github.com/microsoft/masc/blob/master/connector/examples/AccumuloSparkConnector.ipynb)
 for more examples.
+
+# Computational Performance of AI Scenario
+## Setup
+The benchmark setup used a 1,000-node Accumulo 2.0.0 Cluster (16,000 cores) 
running and a 256-node Spark 2.4.3 cluster (4,096 cores). All nodes used [Azure 
D16s_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-general)
 (16 cores) virtual machines. 
[Fluo-muchos](https://github.com/apache/fluo-muchos) was used to handle 
Accumulo and Spark cluster deployments and configuration. 
+
+In all experiments we use the same base dataset which is a collection of 
Twitter user tweets with labeled sentiment value. This dataset is known as the 
Sentiment140 dataset ([Go, Bhayani, & Huang, 
2009](http://www-nlp.stanford.edu/courses/cs224n/2009/fp/3.pdf)). The training 
data consist of 1.6M samples of tweets, where each tweet has columns indicating 
the sentiment label, user, timestamp, query term, and text. The text is limited 
to 140 characters and the overall uncompressed size of t [...]
+
+| sentiment | id | date | query_string | user | text |
+| --- | --- | --- | --- | --- | --- |
+|0|1467810369|Mon Apr 06 22:19:...|    NO_QUERY|_TheSpecialOne_|@switchfoot 
http:...|
+|0|1467810672|Mon Apr 06 22:19:...|    NO_QUERY|  scotthamilton|is upset that 
he ...|
+|0|1467810917|Mon Apr 06 22:19:...|    NO_QUERY|       mattycus|@Kenichan I 
dived...|
+
+To evaluate different table sizes and the impact of splitting the following 
procedure was used to generate the Accumulo tables:
+
+- Prefix id with split keys (e.g. 0000, 0001, ..., 1024)
+- Create Accumulo table and configure splits
+- Upload prefixed data to Accumulo using Spark and the MASC writer 
+- Duplicate data using custom Accumulo server-side iterator
+- Validate data partitioning
+
+A common machine learning scenario was evaluated using a sentiment model 
trained using [SparkML](https://spark.apache.org/docs/latest/ml-guide.html). 
+To train the classification model, we generated feature vectors from the text 
of tweets (text column). We used a feature engineering pipeline (a.k.a. 
featurizer) that breaks the text into tokens, splitting on whitespaces and 
discarding any capitalization and non-alphabetical characters. The pipeline 
consisted of 
+
+- Regex Tokenizer
+- Hashing Transformer
+- Logistic Regression
+
+See the [benchmark notebook 
(Scala)](https://github.com/microsoft/masc/blob/master/connector/examples/AccumuloSparkConnectorBenchmark.ipynb)
 for more details.
+
+## Results
+The first set of experiments evaluated data transfer efficiency and ML model 
inference performance. The chart below shows
+
+- Accumulo table split size (1GB, 8GB, 32GB, 64GB) 
+- Total table size (1TB, 10TB, 100TB, 1PB)
+- Operations
+  - Count: plain count of the data
+  - Inference: Accumulo server-side inference using MLeap
+  - Transfer: Filtering results for 30% data transfer
+- Time is reported in minutes
+
+Remarks
+- Time is log-scale
+- Inference was run with and without data transfer to isolate server-side 
performance.
+- The smaller each Accumulo table split is, the more splits we have and thus 
higher parallelization.
+
+![Runtime](/images/blog/202002_masc/runtime.png "Runtime 
Performance"){:.blog-img-center}
+
+The second set of experiments highlights the computational performance 
improvement of using the server-side inference approach compared to running 
inference on the Spark cluster.
+
+![Mleap](/images/blog/202002_masc/sparkml_vs_mleap_accumulo.png "Spark ML vs 
MLeap Performance"){:.blog-img-center}
+
+# Learnings
+- Accumulo MLeap Server-side inference vs Spark ML results in a 2x improvement
+- Multi-threading in Spark jobs can be used to fully utilize Accumulo servers
+  - Useful if less Spark cluster has less cores than Accumulo
+  - e.g. 8 threads * 2,048 Spark executor = 16,384 Accumulo threads
+- Unbalanced Accumulo table splits can introduce performance bottlenecks
+
+# Useful links
+- [Complete Jupyter demo notebook 
(PySpark)](https://github.com/microsoft/masc/blob/master/connector/examples/AccumuloSparkConnector.ipynb)
 for usage of the Accumulo-Spark connector
+- [Complete Jupyter benchmark notebook 
(Scala)](https://github.com/microsoft/masc/blob/master/connector/examples/AccumuloSparkConnectorBenchmark.ipynb)
 for usage of the Accumulo-Spark connector
+- GitHub Repository [Microsoft's contributions for Spark with Apache 
Accumulo](https://github.com/microsoft/masc)
+- [MLeap](https://github.com/combust/mleap) - Scala/Java stand-alone model 
inference for SparkML-based models
+- [SparkML](https://spark.apache.org/docs/latest/ml-guide.html) - Spark 
machine learning library
+- MASC Maven artifacts
+  - [Accumulo Iterator - Backend for Spark 
DataSource](https://mvnrepository.com/artifact/com.microsoft.masc/microsoft-accumulo-spark-iterator)
+  - [Spark 
DataSource](https://mvnrepository.com/artifact/com.microsoft.masc/microsoft-accumulo-spark-datasource)
+
+# License
+This work is publicly available under the Apache License 2.0 on GitHub under 
[Microsoft's contributions for Apache Spark with Apache 
Accumulo](https://github.com/microsoft/masc). 
+
+# Contributions 
+Feedback, questions, and contributions are welcome!
+
+Thanks to contributions from members on the Azure Global Customer Engineering 
and Azure Government teams.
+
+- [Anupam Sharma](https://github.com/AnupamMicrosoft)
+- [Arvind Shyamsundar](https://github.com/arvindshmicrosoft)
+- [Billie Rinaldi](https://github.com/billierinaldi)
+- [Chenhui Hu](https://github.com/chenhuims)
+- [Jun-Ki Min](https://github.com/loomlike)
+- [Marc Parisi](https://github.com/phrocker)
+- [Markus Cozowicz](https://github.com/eisber)
+- Pavandeep Kalra
+- [Robert Alexander](https://github.com/roalexan)
+- [Scott Graham](https://github.com/gramhagen)
+- [Tao Wu](https://github.com/wutaomsft)
+
+Special thanks to [Anca Sarb](https://github.com/ancasarb) for promptly 
assisting with [MLeap performance 
issues](https://github.com/combust/mleap/issues/633).
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