piccolbo

#on cluster
thrift /spark/sbin/start-thriftserver.sh --master yarn-client
#ssh tunnel, direct 10000 to unused 8157
ssh -i ~/caserta-1.pem -N -L 8157:ec2-54-221-27-21.compute-1.amazonaws.com:10000 [email protected]
#see this for JDBC config on client http://blogs.aws.amazon.com/bigdata/post/TxT7CJ0E7CRX88/Using-Amazon-EMR-with-SQL-Workbench-and-other-BI-Tools

piccolbo

• merge any development branch you need to merge
• git checkout master
• run test

make install-dev
make test

• when test pass git push
• Update HISTORY.rst
• Check readthedocs to make sure docs are OK

piccolbo

Putting wings on the Elephant

[operating-hadoop]

HBase is used widely at Facebook and one of the biggest usecase is Facebook Messages. With a billion users there are a lot of reliability and performance challenges on both HBase and HDFS. HDFS was originally designed for a batch processing system like MapReduce/Hive. A realtime usecase like Facebook Messages where the p99 latency can`t be more than a couple hundreds of milliseconds poses a lot of challenges for HDFS. In this talk we will share the work the HDFS team at Facebook has done to support a realtime usecase like Facebook Messages : (1) Using system calls to tune performance; (2) Inline checksums to reduce iops by 40%; (3) Reducing the p99 for read and write latencies by about 10x; (4) Tools used to determine root cause of outliers. We will discuss the details of each technique, the challenges we faced, lessons learned and results showing the impact of each improvement.

speaker: Pritam Damania

Real-Time Market Basket Analysis for Retail with