Slides from my N

1. High Replication
Datastore
Ikai Lan
plus.ikailan.com
NYC GTUG
July 27, 2011
Wednesday, July 27, 2011

2. About the speaker
• Ikai Lan
• Developer Relations at Google based out
of San Francisco, CA
• Twitter: @ikai
• Google+: plus.ikailan.com
Wednesday, July 27, 2011

3. Agenda
• What is App Engine?
• What is High Replication datastore?
• Underneath the hood
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4. What is App Engine?
Wednesday, July 27, 2011

5. Software
Platform
Infrastructure
Source: Gartner AADI Summit Dec 2009
Wednesday, July 27, 2011

6. Software
Platform
Infrastructure
Source: Gartner AADI Summit Dec 2009
Wednesday, July 27, 2011

7. Software
Platform
Infrastructure
Source: Gartner AADI Summit Dec 2009
Wednesday, July 27, 2011

8. Software
Platform
Infrastructure
Source: Gartner AADI Summit Dec 2009
Wednesday, July 27, 2011

9. SDK & “The Cloud”
Hardware
Networking
Operating system
Application runtime
Java, Python, Go
Static file serving
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10. Scales dynamically
App
Server
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11. Scales dynamically
App
Server
App
Server
App
Server
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12. Customer: WebFilings
Disruptive multi-tenant App Engine application adopted by
Fortune 500 companies.
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13. Customer: The Royal Wedding
Peaked at 32,000 requests per second with no disruption!
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14. >100K Developers
>200K Apps
>1.5B daily pageviews
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15. App Engine
Datastore
Schemaless, non-relational
datastore built on top of
Google’s Bigtable technology
Enables rapid development
and scalability
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16. High Replication
• strongly consistent
• multi datacenter
• High reliability
• consistent
performance
• no data loss
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17. How do I use HR?
• Create a new application! Just remember
the rules
• Fetch by key and ancestor queries exhibit
strongly consistent behavior
• Queries without an ancestor exhibit
eventually consistent behavior
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18. Strong vs. Eventual
• Strong consistency means immediately after
the datastore tells us the data has been
committed, a subsequent read will return
the data written
• Eventual consistency means that some time
after the datastore tells us data has been
committed, a read will return written data -
immediate read may or may not
Wednesday, July 27, 2011

19. This is strongly
consistent
DatastoreService datastore = DatastoreServiceFactory
.getDatastoreService();
Entity item = new Entity("Item");
item.setProperty("data", 123);
Key key = datastore.put(item);
// This exhibits strong consistency.
// It should return the item we just saved.
Entity result = datastore.get(key);
Wednesday, July 27, 2011

20. This is strongly
consistent
// Save the entity root
Entity root = new Entity("Root");
Key rootKey = datastore.put(root);
// Save the child
Entity childItem = new Entity("Item", rootKey);
childItem.setProperty("data", 123);
datastore.put(childItem);
Query strongConsistencyQuery = new Query("Item");
strongConsistencyQuery.setAncestor(rootKey);
strongConsistencyQuery.addFilter("data", FilterOperator.EQUAL, 123);
FetchOptions opts = FetchOptions.Builder.withDefaults();
// This query exhibits strong consistency.
// It will return the item we just saved.
List<Entity> results = datastore.prepare(strongConsistencyQuery)
.asList(opts);
Wednesday, July 27, 2011

21. This is eventually
consistent
Entity item = new Entity("Item");
item.setProperty("data", 123);
datastore.put(item);
// Not an ancestor query
Query eventuallyConsistentQuery = new Query("Item");
eventuallyConsistentQuery.addFilter("data", FilterOperator.EQUAL, 123);
FetchOptions opts = FetchOptions.Builder.withDefaults();
// This query exhibits eventual consistency.
// It will likely return an empty list.
List<Entity> results = datastore.prepare(eventuallyConsistentQuery)
.asList(opts);
Wednesday, July 27, 2011

22. Why?
• Reads are transactional
• On a read, we try to determine if we have
the latest version of some data
• If not, we catch up the data on the node to
the latest version
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23. To understand this ...
• We need some understanding of Paxos ...
• ... which necessitates some understanding
of transactions
• ... which necessitates some understanding
of entity groups
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24. Entity Groups
Entity
User
group root
Blog Blog
Entry Entry Entry
Comment
Comment Comment
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25. Entity groups
// Save the entity root
Entity root = new Entity("Root");
Key rootKey = datastore.put(root);
// Save the child
Entity childItem = new Entity("Item", rootKey);
childItem.setProperty("data", 123);
datastore.put(childItem);
Query strongConsistencyQuery = new Query("Item");
strongConsistencyQuery.setAncestor(rootKey);
strongConsistencyQuery.addFilter("data", FilterOperator.EQUAL, 123);
FetchOptions opts = FetchOptions.Builder.withDefaults();
// This query exhibits strong consistency.
// It will return the item we just saved.
List<Entity> results = datastore.prepare(strongConsistencyQuery)
.asList(opts);
Wednesday, July 27, 2011

26. Entity groups
// Save the entity root
Entity root = new Entity("Root");
Key rootKey = datastore.put(root);
// Save the child
Entity childItem = new Entity("Item", rootKey);
childItem.setProperty("data", 123);
datastore.put(childItem);
Query strongConsistencyQuery = new Query("Item");
strongConsistencyQuery.setAncestor(rootKey);
strongConsistencyQuery.addFilter("data", FilterOperator.EQUAL, 123);
FetchOptions opts = FetchOptions.Builder.withDefaults();
// This query exhibits strong consistency.
// It will return the item we just saved.
List<Entity> results = datastore.prepare(strongConsistencyQuery)
.asList(opts);
Wednesday, July 27, 2011

27. Optimistic locking
Client A reads Client B
data. It's reads data.
current It's current
version is 11 version is 11
Modify data. Modify data.
Increment version Increment version
to 12 Datastore to 12
Client B tries
Client ! tries to to save data.
save data. Success!
Datastore
version is
higher or equal
than my
version - FAIL
Wednesday, July 27, 2011

28. Transactional reads
// Save the entity root
Entity root = new Entity("Root");
Key rootKey = datastore.put(root);
// Save the child
Entity childItem = new Entity("Item", rootKey);
childItem.setProperty("data", 123);
datastore.put(childItem);
Query strongConsistencyQuery = new Query("Item");
strongConsistencyQuery.setAncestor(rootKey);
strongConsistencyQuery.addFilter("data", FilterOperator.EQUAL, 123);
FetchOptions opts = FetchOptions.Builder.withDefaults();
// This query exhibits strong consistency.
// It will return the item we just saved.
List<Entity> results = datastore.prepare(strongConsistencyQuery)
.asList(opts);
Wednesday, July 27, 2011

29. Transactional reads
Still being committed
Blog Entry
Version 11
Comment Comment
Parent: Entry Parent: Entry
Version 11 Version 12
Client B
Client A reads
Datastore transactionally
data
writing data
Version 12 has not finished committing -
Datastore returns version 11
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30. Paxos simplified
Give me the
newest data Node A Node B
Datastore
Client
Is my data
up to date?
Node C Node D
1. If the data is up to date, return it
2. if the data is NOT up to date, "catch up" the data
by applying the jobs in the journal and return the latest
data
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31. More reading
• My example was grossly oversimplified
• More details can be found here:
http://www.cidrdb.org/cidr2011/Papers/
CIDR11_Paper32.pdf
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32. Contradictory advice
• Entity groups must be as big as possible to
cover as much related data as you can
• Entity groups must be small enough such
that your write rate per entity group never
goes above one write/second
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33. Summary
• Remember the rules of strong consistency
and eventual consistency
• Group your data into entity groups when
possible and use ancestor queries
Wednesday, July 27, 2011

34. Questions?
• Twitter: @ikai
• Google+: plus.ikailan.com
Wednesday, July 27, 2011