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

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TasmoMaterialized Views of Event Streams using HBase

Presenters:Pete MaternJonathan Colt

2 © Jive confidential

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

eventsHbase 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

TasmoHbase

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

eventsTasmo

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

readindexviews

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 ExistenceRelationships

Writeevents

Relationships Views

Traverse Join / Select

writes scans

concurrencyconsistency

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 addso Snowflake -1 is used for removeso 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.

16 © Jive confidential

Questions and Answers

Open source

https://github.com/jivesoftware/tasmo

Please Help!

[email protected]@jivesoftware.com