Speakers: Pete Matern and Jonathan Colt (Jive Software) Tasmo is a system that enables application development on top of event streams and HBase. Its functionality is similar to a materialized view in a relational database, where data is maintained at write time in the forms it is needed at read time for display and indexing. Tasmo is designed for significantly read-heavy applications that display the same underlying data in multiple forms, where repeatedly performing the required selects and joins at read time can be prohibitively expensive. In this talk, we'll explore the features and roadmap for Tasmo.

1. Tasmo
Materialized Views of Event Streams using HBase
Presenters:
Pete Matern
Jonathan Colt

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What’s the problem
• Joining to death at read time
• With our operational constraints of a single point of failure (single db
instance)
• Can only scale up - not out
• Read load far exceeds write load
• Read every field of an object every time any field changed to support
indexing
• Read every field of an object to update one

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What we needed
• Joins performed at write time (materialized views)
• Horizontally scalable
• No single point of failure
• Incremental updates
• Notification of changes
• Idempotency
• Tolerance of duplicate and out of order input
• Front end developers work against their object model rather than HBase
specific constructs.

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What we built: Tasmo
Stateless HA service which
• Maintains materialized views of data
• Consumes our model (declaration of input and output types)
• Notifies consumers when views change
• Replaces all our relational db usage

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How we consume and render our model
• Every reader of our model defines views for Tasmo to maintain
• Views contain joined/filtered data specific to point of use
• Readers of these views render output or further process the data
events
Hbase ReadersTasmo
read viewsread / write
View
definition ViewsViewsViews

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How we declare our input and output (Model)
Type: Content
● Subject: String
● Body: String
● Container: Reference
● Author: Reference
Event Declarations
Type: User
● Username: String
● First Name: String
● Last Name: String
● Creation Date: Long
Type: Content
● Subject
● Container (Type: Folder)
○ Name
○ ModDate
● Author (Type: User)
○ Username
○ CreationDate
View Declaration

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Event > Model > View > Web Page
body = “When can
we try it?”
Model
Container
Content
Author
Comment
Tasmo
Hbase
View
Comment Event

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Web Page backed by View Instance

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How we notify consumers
• Consumers register for notifications on a type of view
• Applying an event to the model in Tasmo results in the set of affected view
instances.
• We push the modified view instances to registered consumers
Search
events
Tasmo
notify
Binary storage
Activity Analysis

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How we maintain search indices
• Define views of data which correspond to the index schemas
• Indexing engine registers for notifications of these view types
• Tasmo fires notifications for affected view instances per event
• Indexing engine reads the modified views, which represent complete and
up to date documents for indexing.
Search
events
Hbase
Tasmo
notify
read
index
views
read / write

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10,000 feet how it works
Consumes events, consults configuration describing joins and selects, applies
all relevant changes in event to update data views
Values
Existence
Relationships
Write
events
Relationships Views
Traverse Join / Select
writes
scans
concurrency
consistency
retry ( multiversion concurrency)
updates /
removes
Tasmo

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Taking over time
• Snowflake id for every event - makes them unique and time orderable
• Event time is based on when the system receives an event
• Event time is used as HBase cell timestamp - logically stale writes no op
• Event time has the room to disambiguate add vs remove:
o Snowflake ids are even numbers.
o Snowflake is used directly for adds
o Snowflake -1 is used for removes
o For a given event - adds trump removes

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Concurrency Issues
• Problem: As different events add/remove relationships in parallel, we can
fail to add/remove elements of views.
• Solution: Per relationship high water marks maintained in an HBase table.
We test the per relationship times we saw during a path traversal against
the high water mark. If we detect we are stale, we retry the operation.

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Why HBase?
• Timestamp control
• Row level atomicity of changes
• Performance and proven scalability

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Roadmap
• Production later this year. Currently heavily used by
developers at Jive.
• Looking at what work could be moved into
coprocessors.
• Considering double writes into two HBase clusters for
higher availability if MTTR is too high in our
environment.

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Questions and Answers
Open source
https://github.com/jivesoftware/tasmo
Please Help!
jonathan.colt@jivesoftware.com
pete@jivesoftware.com