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Big Data Open Source Software
and Projects
ABDS in Summary XIX: Layer 14B
Data Science Curriculum
March 1 2015
Geoffrey Fox
[email protected]
http://www.infomall.org
School of Informatics and Computing
Digital Science Center
Indiana University Bloomington
Functionality of 21 HPC-ABDS Layers
1) Message Protocols:
2) Distributed Coordination:
3) Security & Privacy:
4) Monitoring:
5) IaaS Management from HPC to hypervisors:
6) DevOps:
Here are 21 functionalities.
7) Interoperability:
(including 11, 14, 15 subparts)
8) File systems:
9) Cluster Resource Management:
4 Cross cutting at top
10) Data Transport:
17 in order of layered diagram
11) A) File management
starting at bottom
B) NoSQL
C) SQL
12) In-memory databases&caches / Object-relational mapping / Extraction Tools
13) Inter process communication Collectives, point-to-point, publish-subscribe, MPI:
14) A) Basic Programming model and runtime, SPMD, MapReduce:
B) Streaming:
15) A) High level Programming:
B) Application Hosting Frameworks
16) Application and Analytics:
17) Workflow-Orchestration:
Apache Storm
• https://storm.incubator.apache.org/
• Apache Storm is a distributed real time computation framework for
processing streaming data.
• Storm is being used to do real time analytics, online machine
learning, distributed RPC etc.
• Provides scalable, fault tolerant and guaranteed message
processing.
• Trident is a high level API on top of Storm which provides functions
like stream joins, groupings, filters etc. Also Trident has exactly-once
processing guarantees.
• The project was originally developed at Twitter for processing
Tweets from users and was donated to ASF in 2013.
• Storm has being used in very large deployments in Fortune 500
companies like Twitter and Yahoo.
Apache Samza (LinkedIn)
• http://samza.incubator.apache.org/
• Similar to Apache Storm, Apache Samza is a distributed real
time computation framework for processing streaming data.
• Apache Samza is built on top of Apache Kafka and Apache
Yarn. Samza uses Kafka as its messaging layer and Yarn for
managing the cluster of nodes with Samza processes.
• Samza is scalable, fault tolerant and provides guaranteed
message processing.
• Samza was originally developed at LinkedIn and was donated
to ASF in 2013
Apache S4
• http://incubator.apache.org/s4/
• Apache S4 is a distributed real time computation framework
for processing unbounded streams of data.
• Unlike Storm and Samza S4 provides a key value based system
for processing data
• The system is scalable, fault tolerant and provides guaranteed
message processing.
• S4 was originally developed at Yahoo and was donated to ASF
in 2011
• S4 isn’t popular as Apache Storm
Granules
• http://granules.cs.colostate.edu/
• This builds on NaradaBrokering (Layer 13)
and started at Indiana University
(now at Colorado State) led by Shrideep Pallickara
• Supports long running, stateful iterative computations
with science enhancements to MapReduce with data
streaming in.
• Runs on HPC or Clouds or distributed resources with C,
C++,C#, Java, R, and Python
Databus (LinkedIn)
• Closed source Databus http://data.linkedin.com/projects/databus
• Databus provides a timeline-consistent stream of change capture events for a
database. It enables applications to watch a database, view and process updates in
near real-time.
• Databus provides a complete after-image of every new/changed record as well as
deletes, while maintaining timeline consistency and transactional boundaries.
• The application integration is decoupled from the source database, and each
application integration is isolated, which allows for parallel development and rapid
innovation.
• Databus has a few key parts:
– a database connector to watch changes and maintain a clock or sequence value
– an in-memory relay that keeps recent changes for efficient retrieval
– a bootstrap service/database that enables long lookback queries (including from the
beginning of time)
– a client that provides a simple API to get changes since a point in time
• To use databus, the consuming application simply maintains a high watermark, and
periodically requests all changes since that point in time using the Databus client.
Each consuming application maintains its own high watermark, which provides
isolation from one another
Google MillWheel
• http://research.google.com/pubs/pub41378.html
• MillWheel is a distributed real time computation framework by
Google.
• Provides scalable, fault tolerant and exactly once message
processing guarantees.
• The key data abstraction of the MillWheel is Key-Value pairs and
data is processed in a directed acyclic graph where nodes are the
computation nodes.
• The project is not open source and is planned to be available to
general public through Google Cloud platform as a SaaS.
• Similar functionality to Apache Storm
• Part of Google Cloud Dataflow
http://googlecloudplatform.blogspot.com/2014/06/sneak-peekgoogle-cloud-dataflow-a-cloud-native-data-processing-service.html
that also has Google Pub-Sub and FlumeJava
• See Amazon Kinesis http://aws.amazon.com/kinesis/ which
combines Pub-Sub and Apache Storm capabilities
Facebook Puma/Ptail/Scribe/ODS
• Facebook Insights tool gives content developers an interactive
web portal that presents business analytics related to their
Social plug-ins with only 30 seconds of latency, handling 20
billion events a day (2012) and uses 4 subsystems below
• Scribe: aggregating streaming log data.
– https://github.com/facebookarchive/scribe/wiki
• ODS: Real-time monitoring system built on Hbase and stores data produced by
Scribe
– http://cdn.oreillystatic.com/en/assets/1/event/85/Facebook%E2%80%99s%20L
arge%20Scale%20Monitoring%20System%20Built%20on%20HBase%20Present
ation.pdf
– http://cloud.pubs.dbs.uni-leipzig.de/sites/cloud.pubs.dbs.unileipzig.de/files/RealtimeHadoopSigmod2011.pdf
– Time series data for real-time monitoring and trends, Collects metrics from
each server, Aggregates in useful ways, Detects and alerts on anomalies
• Puma: a real time stream processing system batches data in memory
– http://www.slideshare.net/cloudera/building-realtime-big-data-services-atfacebook-with-hadoop-and-hbase-jonathan-gray-facebook
– http://www.cs.duke.edu/~kmoses/cps516/puma.html
• Data is read from the log files using Ptail, which is an internal tool built to
aggregate data from multiple Scribe stores. It tails (fetches last data out) the log
files and pulls data out.
•
•
Azure Stream Analytics I
Microsoft Azure has several streaming solutions:
Stream Analytics is is a SQL language based implementation for querying streaming data.
The inputs to Stream Analytics comes from Event Hubs, which are a service bus/message
broker type offering where users can send their events to and subscribe to events.
– Service Bus is used as an publish-subscribe broker in event hub.
– Stream Analytics can subscribe to event hub to receive the event streams.
– Microsoft have extended SQL language to support stream querying.
– http://azure.microsoft.com/en-us/documentation/articles/stream-analytics-get-started/
– http://azure.microsoft.com/en-us/documentation/articles/stream-analytics-real-time-eventprocessing-reference-architecture/
– http://azure.microsoft.com/en-us/documentation/services/service-bus/
•
Azure is also offering Storm as a dedicated service.
– http://azure.microsoft.com/en-us/documentation/articles/hdinsight-storm-overview/
•
•
•
Language support - Storm offers a more diversified set of languages whereas Azure Stream
Analytics supports only a SQL language very similar to that provided with SQL Server.
Deployment model – Storm runs on dedicated HDInsight clusters, whereas Azure Stream
Analytics has built-in multi-tenancy support.
Data Interface – Azure Stream Analytics has first party click & configure support for Event
Hub, Azure Blob Storage, and Azure SQL Database. Storm has ingestion from Azure Event
Hub, Azure Service Bus, and Apache Kafka amongst others, as well as data egress to Apache
Cassandra, HDFS and SQL Azure Database.
Azure Stream Analytics II
• http://download.microsoft.com/download/6/2/3/623924DEB083-4561-9624-C1AB62B5F82B/real-time-event-processingwith-microsoft-azure-stream-analytics.pdf