Atzmon Hen-Tov & Lior Schachter, Pontis Businesses everywhere are increasingly challenged by their dependencies on legacy platforms. The dramatic increase in data volume, speed, and types of data is quickly outstripping the capabilities of these legacy systems. By transitioning from a legacy RDBMS to a Hadoop-based platform, Pontis was able to process and analyze billions of mobile subscriber events every day. In this talk, we’ll provide a quick overview of our legacy system, as well as our process for migrating to our target architecture. We’ll continue with a review our Hadoop platform selection process, which involved a thorough RFP and a detailed analysis of the top Hadoop platform vendors. This session will focus on how we gradually transitioned to our big data platform over the course of several product versions, resulting in higher scalability and a lower TCO in each version. We’ll outline the benefits of the target architecture, and detail how we successfully integrated Hadoop into our organization. Our session will conclude with a look at technical solutions for dealing with big data deficiencies.

1. Animated version

7. Operat
or
System
s
OCS
IN
CDR
PCC
CRM
Data Flow
IntegrationLayer
RT Complex
Event
Processing
Decisioning Engine
iCLM UI
Marketing
Operations
Business
Discovery,
Monitoring
& Reporting
Visual
Rules
Subscriber Data Store
HBa
se
MarketingCSR Monitoring
Big Data
Analytics
Hadoop M/R Event,
aggregation and profile
Data
Hive DWH
Subscriber Profile
Decisioning Engine
Chann
els

11. We conducted an RFP for selecting the most Telco-Grade platform.
The RFP focused on non-functional capabilities such as sustainable
performance, high-availability and manageability.

14. The approach
 Each step should increase scalability and reduce TCO.
 Runtime (OLTP) processing:
 We replace the underline plumbing's-minimal changes to business logic.
 All changes can be turned on/off by GUI configurations:
 Modular hybrid architecture.
 Ability to work in dual mode - Good for QA…But also for production (legacy)…
 Analytics processing:
 Calculate the Profile in M/R (Java).
 Scalable.
 We have the best Java developers.
 Wrap it with a DSL (Domain-Specific-Languages)
 That’s how we work for years – (ModelTalk paper)
 Non-Java-programmers can do the Job.

16. Legacy Architecture

17. Phase 1

18. Phase 1 – File queues in NFS
Resulting context
 Pure plumbing change – no changes to business logic code.
 Offloading oracle: *2 Performance boost.
 No BigData technology.
 Windows NFS client performance is a bottleneck.
Phase # Customers # Events
Legacy 10M 120M
Phase 1 10M 200M

19. Phase 2

20. Phase 2 – Introducing MapR Hadoop
Cluster
Resulting Context
 MapR FS + NFS :
 Horizontally scalable
 Cheap compared to high-end NFS solutions.
 Fast and High-Available (using VIPs)
 Avoiding another hop to HDFS (Flume, Kafka).
 Many small files are stored in HDFS (100s of millions) – no need to merge files
Phase # Customers # Events
Legacy 10M 120M
Phase 1 10M 200M
Phase 2 unlimited 200M

21. Phase 2 – Introducing MapR Hadoop
Cluster
Resulting Context
 Avro files:
 Complex Object Graph
 Troubleshooting with PIG
 Out-of-the-box upgrade (e.g. adding field)
 Map/Reduce is incremental – Avro record capture the subscriber state
 Map/Reduce efficiency - avoiding huge joins
 Subscriber Profile calculation:
 Performance : 2-3 hours.
 Linear scalability: No limitation on number of subscribers/raw data (buy more nodes)
 Fast run over history data allows for early launch
 Sqoop - very fast insertions to MS-SQL (10s of millions of records in minutes).
 Data-Analysts started working over Hive environment.
 No HA for OOZIE yet…
 Hue is premature
 MS-SQL and ODBC over Hive is slow and limited

22. Phase 3

23. Phase 3 –Introducing MapR M7 Table
 Extensive YCSB load tests to find best table structure and read/update
granularity. Main conclusions:
 M7 knows how to handle very big heap – 90GB.
 Update granularity : small updates (using columns) = fast reads
(*)While in other KV store need to update the entire BLOB
 CSR tables migrated from Oracle to M7 Table:
 10s of billions of records
 Need sub-second random access per subscriber
 99.9% Writes – by Runtime machines (almost each event processing operation
produces update)
 0.1% Reads – by Customer’s CSR representative.
 Rows – per subscriber key, 10’s of millions
 2 CFs – TTL 365 days. 1 version.
 Qualifier:
 key:[date_class_event_id], value: record
 Up to thousands per Row

24. Phase 3 –Introducing MapR M7 Table
Resulting Context
 Choosing the right features – no too demanding performance wise.
 Easy to create and manage tables– still there’s some tweaking.
 No cross-table ACID - need to develop a solution for keeping consistency across
M7 Table/Oracle/Files-system.
 Hard for QA - compared to RDBMS. No easy way to query. Need to develop tools.
Phase # Customers # Events
Legacy 10M 120M
Phase 1 10M 200M
Phase 2 unlimited 200M
Phase 3 unlimited 300M

25. Phase 4

26. Phase 4 – Migrating OLTP features to
M7 tables
 Subscriber State table migrated from Oracle to M7 Table:
 25% Writes– by Runtime machines updating the state
 100% Reads – by Runtime.
 Rows – per subscriber key, 10’s of millions
 1 CFs – TTL -1. 1 version.
 YCSB to validate the solution  Sizing model
 Qualifier:
 key:state_name, value: state value.
 Dozens per Row.
 But….Only 10% are being updated per event
 Subscriber Profile Table migrated from MS-SQL to M7 Table.
 Bulk insert once a day
 Outbound Queue Table migrated from MS-SQL to M7 Table.

27. Phase 4 – Migrating OLTP features to
M7 tables
Resulting Context
 No longer dependent on Oracle for OLTP.
 Real-time processing can handle billions of events per day.
 Sizing is linear and easy to calculate:
 Number of subscribers * state size * 80% should reside in cache.
 HW spec: 128GB RAM, 12 SAS drives.
 Consistency management is very complicated.
Phase # Customers # Events
Legacy 10M 120M
Phase 1 10M 200M
Phase 2 unlimited 200M
Phase 3 unlimited 300M
Phase 4 unlimited unlimited

28. Phase 5

29. Phase 5 – Decommission legacy RDBMS
Resulting Context
 MySQL is not a new technology in our stack (part of MapR
distribution).
 Removing Oracle/MS-SQL from our architecture has
significant impact on system cost, deployment, monitoring
etc.

30. Atzmon Hen-Tov Lior Schachter