This document evaluates AppScale, an open-source platform that allows Google App Engine applications to run on distributed data stores like HBase, Cassandra, and MongoDB. It describes AppScale's features for porting applications between data stores and compares the performance of different data stores under light, medium, and heavy loads. While AppScale provides access to many data stores, each varies in how fully it implements the App Engine APIs. The document calls for expanding AppScale's services to new domains and integrating additional databases.

1. An Evaluation of Distributed
Datastores Using
The AppScale Cloud
1
Platform
Presented By- Himanshu Ranjan Vaishnav
TE-42065 (Comp-I)
SEMINAR GUIDE - Prof. Mrs S. S. Sonawani
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2. 2 What is AppScale?
 AppScale is an open-source implementation of the Google App Engine
cloud platform.
 AppScale is an extension of the non-scalable software development kit
that Google makes available for testing and debugging applications.
 App-Scale currently supports HBase, Hypertable, Cassandra, Voldemort,
MongoDB, MemcacheDB, Scalaris, and MySQL Cluster datastores.
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3. 3 What AppScale Does?
 AppScale is a robust, open source implementation of the Google App
Engine APIs that executes over private virtualized cluster resources and
cloud infrastructures including Amazon Web Services and Eucalyptus.
 Users can execute their existing Google App Engine applications over
AppScale without modification.
 AppScale automates deployment and simplifies configuration of
datastores that implement the API and facilitates their comparison and
evaluation on end-to-end performance using real programs (Google App
Engine applications).
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4. 4 AppScale Features
• More Choices of data Stores • MapReduce
• App Engine Portability
• Neptune Language • Fault Tolerance
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And More

5. 5 Google App Engine
 A software development platform
 Platform-as-a-service (PaaS)
 GAE Datastore
 Big Table
 A master/slave relationship
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6. 6 Continue….
 GAE Datastore API provides the following primitives:
For eg.
• Put (k, v): Add key k and value v to table; creating a table if needed
• Get (k): Return value associated with key k
• Delete (k): Remove key k and its value
• Query (q): Perform query q using the Google Query Language (GQL) on a
single table, returning a list of values
• Count (t): For a given query, returns the size of the list of values returned
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7. 7 Google App Engine APIs
 Blobstore API  Users API
 Channel API  URL Fetch API
 Datastore API  XMPP API
 Images API  MapReduce Streaming API
 Memcache API  EC2 API
 Namespace API
 Task Queue API
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8. 8 AppScale deployment
 AS – App Server
 ALB – App Load Balancer
 DBS – Data Base Slave Peer
 DBM – Data Base Master Peer 04/01/13

9. 9 Multi-tiered approach within AppScale
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10. 10 Database Services
 Protocol Buffer Server (PBServer)
 User/App Server (UAServer)
 Blobstore service
 Monitoring Services
 Neptune
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11. 11 APPSCALE DISTRIBUTED DATABASE
SUPPORT
 Cassandra
 HBase
 Hypertable
 MemcacheDB
 MongoDB
 Voldemort
 MySQL
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12. 12 1. Cassandra
 Facebook engineers designed, implemented, and released
 A hybrid approach
 Consistent
 Written in the Java and exposes its API through the Thrift software
framework
 Supports range queries
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13. 13 2. HBase
 Developed and released by PowerSet
 An official Hadoop subproject
 Employs a master-slave distributed architecture
 Provides flexible column support
 Written primarily in Java, with a small portion of the code base in C
 HBase is deployed over the Hadoop Distributed File System (HDFS)
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14. 14 3. Hypertable
 Hypertable was developed by Zvents
 Provide an open source version of Google’s BigTable
 Written in C++
 RangeServer
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15. 15 4. MemcacheDB
 Developed by Open source developer Steve Chu
 Employs a master-slave approach
 Runs with a single master node and multiple replica nodes
 Written in C and uses Berkeley DB
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16. 16 5. MongoDB
 Developed and released by 10gen
 Provide both the speed and scalability
 Written in C++
 Queries are performed using hashtable
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17. 17 6. Voldemort
 Developed by and currently in use internally at LinkedIn
 Eventual consistency
 More Developer friendly
 Written in Java and exposes its API via Thrift
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18. 18 7. MySQL
 A well-known relational database
 Employ MySQL Cluster
 Provides concurrent access to the system
 Written in C and C++
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19. 19 EVALUATION
 Load tables in all databases with 1000 items
 Test specifics:
– On Each database put, get, delete, no-op performed
– Considered- light load: one thread, medium load: three concurrent thread,
heavy thread: nine concurrent thread
– Repeat each experiment 5 times
 Executes this application in an AppScale cloud
 Each node executes with 2 virtual processors, 10GB of disk(max), 4GB of
memory
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20. 20 Experimental Results
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21. 21 Limitations
 Persistence  Lake of retrieving the entire table
to run a query
 Blobstore Max File Size
 Not released the source code of
 Datastore
the Java App Engine server
 Task Queue
 Mail
 Follow a ”deploy on all nodes”
 Limited distribution supported
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22. 22 Future Work
 Expand out of the web services domain
– Investigating opportunities in streaming
– Integrated MapReduce support for highperformance computing (HPC)
– Co-locate AppEngines and use shared memory
 Additional databases:
– MongoDB, Scalaris, CouchDB
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23. 23 Continue…
 Extending AppScale with new services for
- large-scale data analytics
- data
- computation intensive tasks
 Cloud-agnostic
 Integration of mobile device
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24. 24 CONCLUSION
 Presents an open source implementation of the Google App Engine (GAE)
Datastore API with in a cloud platform called AppScale
 The implementation unifies access to wide range of open source
distributed database technologies and automates their configuration and
deployment. However, each database differs in the degree to which it
implements the APIs.
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25. 25
DEMO
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26. 26
Thank You
Any Questions ??
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