If you can't read please download the document
Upload
zznate
View
12.856
Download
1
Embed Size (px)
Citation preview
PowerPoint Presentation
Apache Cassandra
An Introduction for Java Developers
Nate [email protected]@zznate
What is Apache Cassandra?
CAP Theorem
ConsistencyAvailability Partition Tolerance
Though shalt have but 2
- Conjecture made by Eric Brewer in 2000- Published as formal proof in 2002- See: http://en.wikipedia.org/wiki/CAP_theorem for more
Apache Cassandra Concepts
- Explicit choice of partition tolerance and availability. Consistency is tunable.- No read before write- Merge on read- Idempotent- Schema Optional- All nodes share the same roll- Still performs well with larger-than-memory data sets
Generally complements another system(s)
(Not intended to be one-size-fits-all)
*** You should always use the right tool for the right job anyway
How does this differ from an RDBMS?
How does this differ from an RDBMS?
Substantially.
vs. RDBMS - No Joins
Unless: - you do them on the client - you do them via Map/Reduce
vs. RDBMS - Schema Optional
(Though you can add meta information for validation and type checking)
*** Supports secondary indexes too: WHERE state = 'TX'
vs. RDBMS - Prematerialized and Transaction-less
- No ACID transactions - Limited support for ad-hoc queries
vs. RDBMS - Prematerialized and Transaction-less
- No ACID transactions - Limited support for ad-hoc queries
*** You are going to give up both of these anyway when you shard an RDBMS ***
vs. RDBMS - Facilitates Consolidation
It can be your caching layer * Off-heap cache (provided you install JNA)
It can be your analytics infrastructure * true map/reduce * pig driver * hive driver coming soon
vs. RDBMS - Shared-Nothing Architecture
Every node plays the same role: no masters, no slaves, no special nodes
*** No single point of failure
vs. RDBMS - Real Linear Scalability
Want 2x performance? Add 2x nodes.
*** 'No downtime' included!
vs. RDBMS - Performance
Reads on par with writes
Clustering
Clustering
Single node cluster (easy development setup)- one node owns the whole hash range
Clustering
Two node cluster- Key range divided between nodes
Clustering
Consistent Hashing: md5(zznate) = C
Clustering
Consistent Hashing FTW:- Ring ownership continuously gossiped between nodes- Any node can act as a coordinator to service client requests for any key * requests forwarded to the appropriate nodes by coordinator transparently to the client
Clustering
Client Read: get(zznate)md5 = C
Clustering Scale Out
Clustering Scale Out
Clustering Scale Out
Clustering - Multi-DC
Clustering - Reliability
Clustering - Reliability
Clustering - Reliability
Clustering - Reliability
Clustering - Multi-Datacenter
Clustering Multi-DC Reliability
Storage (Briefly)
Storage (Briefly)
Understanding the on-disk format is extremely helpful in designing your data model correctly
Storage - SSTable
- SSTables are immutable (Merge on read)- Newest timestamp wins
Storage Compaction
Merge SSTables keeping count down making Merge on Read more efficientDiscards Tombstones (more on this later!)
Data Model
Data Model
"...sparse, persistent, distributed, multi-dimensional sorted map."
(The Bigtable paper)
Data Model
Keyspace- Collection of Column Families
- Controls replication
Column Family
- Similar to a table
- Columns ordered by name
Data Model Column Family
Static Column Family- Model my object data
Dynamic Column Family
- Pre-calculated query results
Nothing stopping you from mixing them!
Data Model Static CF
zznate
driftx
thobbs
jbellis
password: *
password: *
password: *
name: Nate
name: Brandon
name: Tyler
password: *
name: Jonathan
site: datastax.com
Users
Data Model Prematerialized Query
Following
zznate
driftx
thobbs
jbellis
driftx:
thobbs:
driftx:
thobbs:
mdennis:
zznate
zznate:
pcmanus
xedin:
Data Model Prematerialized Query
Additional examples:Timeline of tweets by a userTimeline of tweets by all of the people a user is followingList of comments sorted by scoreList of friends grouped by state
API Operations
Five general categories
RetrievingWriting/Updating/Removing (all the same op!)Increment counters
Meta InformationSchema ManipulationCQL Execution
Using a Client
Hector Client:http://hector-client.org- Most popular Java client - In use at very large installations- A number of tools and utilities built on top- Very active community- MIT Licensed
*** like any open source project fully dependent on another open source project it has it's worts
Sample Project for Experimenting
https://github.com/zznate/cassandra-tutorialhttps://github.com/zznate/hector-examplesBuilt using Hector Really basic designed to be beginner level w/ very few moving partsModify/abuse/alter as needed
*** Descriptions of what is going on and how to run each example are in the Javadoc comments.
ColumnFamilyTemplate
Familiar, type-safe approach- based on template-method design pattern- generic: ColumnFamilyTemplate (K is the key type, N the column name type)
ColumnFamilyTemplate template = new ThriftColumnFamilyTemplate(keyspaceName, columnFamilyName, StringSerializer.get(), StringSerializer.get());
*** (no generics for clarity)
ColumnFamilyTemplate
new ThriftColumnFamilyTemplate(keyspaceName, columnFamilyName, StringSerializer.get(), StringSerializer.get());
Key Format
Column Name Format- Cassandra calls this a comparator- Remember: defines column order in on-disk format
ColumnFamilyTemplate
ColumnFamilyResult res = cft.queryColumns("zznate");
String value = res.getString("email");
Date startDate = res.getDate(startDate);
Key Format
Column Name Format
ColumnFamilyTemplate
ColumnFamilyResult wrapper = template.queryColumns("zznate", "patricioe", "thobbs");
String nateEmail = wrapper.getString("email");
wrapper.next();
String patoEmail = wrapper.getString("email");
wrapper.next(); String tylerEmail = wrapper.getString("email");
Querying multiple rows and iterating over results
ColumnFamilyTemplate
ColumnFamilyUpdater updater = template.createUpdater("zznate");
updater.setString("companyName","DataStax");updater.addKey("sergek");updater.setString("companyName","PrestoSports");
template.update(updater);
Inserting data with ColumnFamilyUpdater
ColumnFamilyTemplate
template.deleteColumn("zznate", "notNeededStuff");template.deleteColumn("zznate", "somethingElse");template.deleteColumn("patricioe", "aDifferentColumnName");...template.deleteRow(someuser);
template.executeBatch();
Deleting Data with ColumnFamilyTemplate
Deletion
Deletion
Again: Every mutation is an insert!
- Merge on read
- Sstables are immutable
- Highest timestamp wins
Deletion As Seen by CLI
[default@Tutorial] list StateCity;Using default limit of 100
-------------------
RowKey: CA Burlingame
=> (column=650, value=33372e3537783132322e3334, timestamp=1310340410528000)
-------------------
RowKey: TX Austin
=> (column=202, value=33302e3237783039372e3734, timestamp=1310143852392000)
=> (column=203, value=33302e3237783039372e3734, timestamp=1310143852444000)
=> (column=204, value=33302e3332783039372e3733, timestamp=1310143852448000)
=> (column=205, value=33302e3332783039372e3733, timestamp=1310143852453000)
=> (column=206, value=33302e3332783039372e3733, timestamp=1310143852457000)
Deletion As Seen by CLI
[default@Tutorial] list StateCity;Using default limit of 100
-------------------
RowKey: CA Burlingame
-------------------
RowKey: TX Austin
=> (column=202, value=33302e3237783039372e3734, timestamp=1310143852392000)
=> (column=203, value=33302e3237783039372e3734, timestamp=1310143852444000)
=> (column=204, value=33302e3332783039372e3733, timestamp=1310143852448000)
=> (column=205, value=33302e3332783039372e3733, timestamp=1310143852453000)
=> (column=206, value=33302e3332783039372e3733, timestamp=1310143852457000)
Deletion FYI
mutator.addDeletion("202230", "Npanxx", city, stringSerializer);
Does not exist? You just inserted a tombstone!
Sending a deletion for a non-existing row:
[default@Tutorial] list Npanxx; Using default limit of 100
. . .
-------------------
RowKey: 202230
-------------------
. . .
Integrating with existing patterns
Integrating with existing patterns
Yes.
Integrating with existing patterns
Integrating with existing patterns
Hector Object Mapper:
https://github.com/rantav/hector/wiki/Hector-Object-Mapper-%28HOM%29
Hector JPA:
https://github.com/riptano/hector-jpa
Integrating with existing patterns
CQL: JDBC Driver and Pool in 1.0!
JdbcTemplate FTW!
Development Resources
Hector Documentation
http://hector-client.orgCassandra Maven Plugin
http://mojo.codehaus.org/cassandra-maven-plugin/CCM localhost
cassandra cluster
https://github.com/pcmanus/ccmOpsCenter
http://www.datastax.com/products/opscenter
Cassandra AMIs
https://github.com/riptano/CassandraClusterAMI
Putting it Together
Take control of consistency
If you do need a high degree of consistency, use thresholds to trigger different behavior
- Bank account:
on values over $10,000, wait to here from all replicas
- Distributed Shopping Cart:
Show a confirmation page to verify order resolution
*** What is your appetite for risk?
Uniquely identify operations in the application
Facilitates idempotent behavior and out-of-order execution
Denormalization
The point of normalization is to avoid update anomalies
***But In an append-only system, we don't do updates
Summary
- Take advantage of strengths
- Look for idempotence and asynchronicity in your business processes
- If it's not in the API, you are probably doing it wrong
- Seek death is still possible if you model incorrectly
Questions
Nate [email protected]@zznate
Additional Resources
DataStax Documentation: http://www.datastax.com/docs/0.8/index
Apache Cassandra project wiki: http://wiki.apache.org/cassandra/
The Dynamo Paper
http://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf
P. Helland. Building on Quicksand
http://arxiv.org/pdf/0909.1788
P. Helland. Life Beyond Distributed Transactions
http://www.ics.uci.edu/~cs223/papers/cidr07p15.pdf
S. Anand. Netflix's Transition to High-Availability Storage Systems
http://media.amazonwebservices.com/Netflix_Transition_to_a_Key_v3.pdf
The Megastore Paper
http://research.google.com/pubs/archive/36971.pdf