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How Do I Cassandra?

The document provides an overview of Cassandra and how to use it. It discusses that Cassandra is a distributed database that scales out across commodity servers and remains available even during failures. It also covers that Cassandra uses a column-oriented data model and partitions data by row key across nodes, with configurable replication for high availability. The document recommends Cassandra for workloads where availability is critical and provides examples of how companies like Reddit and UrbanAirship use it.

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HOW DO I CASSANDRA? Rick Branson, DataStax Memphis Python Users Group November 21st, 2011
WHO? • This is my first day at DataStax, halp! • Coroutine for 18 months • American Roamer for 3 years • FedEx for 4 years
OCCUPY SQL Why is it that 99% of the applications go into 1% of database softwares?
HOW DO I NOSQL? 1. Anvil transitions. Deal with it. 2. Find data store that doesn’t use SQL 3. ANYTHING 4. Cram all the things into it 5. Triumphantly blog this success 6. Complain a month later when it bursts into flames
SEEMS LEGIT ... it really is legit. There are real data sets that don’t make sense in the relational model, nor modern ACID databases.
THE GREAT HAMMER ... but, the typical developer only has a relational database; everything looks like a nail.
SNAKE OIL Typically, developers pick data structures suited to each use case. The purveyors of relational would have you believe everything fits into one mold.
FIT WHAT INTO WHERE? Trees, Semi-Structured Data, Web Content, Multi-Dimensional Cubes, Graphs
HAMMERS GONNA HAMMER It does work – so does COBOL. Just like a motion picture film, the speed of modern computers creates an illusion.
MAKE A WHAT OUTTA WHO? ASSUMPTIONS MADE ON YOUR BEHALF • Data must be synchronized everywhere, instantaneously • The structure of data doesn’t change over time • Hardware never fails and networks are perfect • Humans always do the right thing • No upgrading or patches are ever needed • Planned outages are good for the soul
THE MOVEMENT “No one tool is appropriate for solving every problem.” ~ Cliff McKinney Gettysburg Address, cir. 1863
SRS BZNS 10gen, Basho, DataStax, Cloudant, Cloudera, HortonWorks, VMware, Oracle Guess the number of craps Russ here gives about databases?
MOVING ON OH: “The last DBA in the room went dataway”
AN DEFINITION Cassandra – affectionately “C*” – is an open source, distributed store for structured data that scales-out on cheap, commodity hardware and stays up even when things get really bad.
FUH-GET ABOUT IT Ruins availability • Strict Consistency • Transactions Deadlocks & Not scalable • Ad-hoc Queries • Indexes aka “Dumbass” Queries Scumbag index covers every column; ignored by planner
THE REWARDS • Simplicity of Operations • Transparency • Very High Availability • Painless Scale-Out • Solid, Predictable Performance on Commodity and Cloud Servers
Cassandra is about trading some developer time for operational pain – or 3am phone calls as some of us know it.
DEM GOOD GENES Put simply: the scalable, efficient storage model from Google’s BigTable atop the incredibly fault resistant distributed design of Amazon’s Dynamo resisting, not just tolerating!
VS OTHER NoSQL • Truly Distributed Design • No Special Servers, No SPOF • 2D Data Model • Rocks on Spinning Disks • Single-Server Durability
HORRIBLE IDEAS THINGS NOT TO USE CASSANDRA FOR • Prototyping • Temporal Data • Distributed Locks • Message Queues • ... anywhere availability doesn’t matter
UNBOUNDED HUMANS ... OR DATA THAT THRIVES ON CASSANDRA • Social Voluntary, human-generated data: text messages, comments, check- ins, real-time collaboration • Activity Involuntary or indirectly human-generated data: news feeds, timelines, clickstreams, system + application logs • Images Smaller files < ~10MB fit well into columns • ... anywhere availability is paramount, (not necessarily big data or scale either)
TO START • easy_install pycassa • Pretty much ignore any info pre-0.7 • Use DataStax Community Edition • Unfortunately, the “official” wiki sucks, so use DataStax docs & tutorials • IRC, Mailing List, StackOverflow, Quora all monitored regularly and kick ass
WHY CASSANDRA + PYTHON? • pycassa is one of the best client • Healthy Cassandra+Python Community • Many examples of real, solid, production Python applications built on- top of Cassandra
PYTHON + CASSANDRA NOTABLE IMPLEMENTATIONS • Reddit Caches generated comment trees in Cassandra • UrbanAirship Delivers 20M push notifications per day; bursts to 100k/sec+ • GameFly behind their social network The database • Digg Pretty much the whole site
DATA MODEL “I gotcha column family ratttt heeeeyyyaahhh!”
AN COLUMN FAMILY jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
AN COLUMN FAMILY • Don’t think in relational database terms • Columns are { name, value } tuples • It’s dictionaries all the way down... { “key”: { “name”: “value” } } Column Column Family Row
Row Key jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
Row Key jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F Rows are randomly ordered
Columns jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
Column Names jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age: 12 gender: M suzy age: 10 gender: F
Column Names jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age: 12 gender: M suzy age: 10 gender: F Column Name dictates order
Column Values jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age: 12 gender: M suzy age: 10 gender: F
PARTITIONING Cassandra divides the data set into ranges and distributes rows among multiple servers to achieve scale-out.
Row Key determines node placement jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
Row Key MD5 Hash jim 5e02739678... MD5 hash carol a9a0198010... operation yields a 128-bit number johnny f4eb27cea7... for keys of any size. suzy 78b421309e...
Conceptually, hashed keys are placed along a ring representing the keyspace. Keyspace
The ring is divided up into ranges, one for each node in the cluster. Node A Node B Node D Node C
In a 4-node cluster, each node holds roughly a quarter of the rows. End (Token) Start Node A 0x00000000000000000000000000000000 0xc0000000000000000000000000000001 Node B 0x40000000000000000000000000000000 0x00000000000000000000000000000001 Node C 0x80000000000000000000000000000000 0x40000000000000000000000000000001 Node D 0xc0000000000000000000000000000000 0x80000000000000000000000000000001
Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e...
Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e...
Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e...
Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e...
Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e...
REPLICATION Cassandra creates multiple copies of rows so that if any one node is behaving badly, all of the data stays available and fully operational.
REPLICATION • Column Family has “Replication Factor” • RF = total number of copies of each row • Don’t be fail; NEVER use 1 • Use 3+ in the “cloud” • Use 2+ on redundant hardware • Write operations on any replica
Replicas are chosen by jumping to the next node(s) on the ring Node A Node B Node D Node C carol a9a0198010...
Replication Factor = 2 Node A Node B Node D Node C carol a9a0198010...
Replication Factor = 3 Node A Node B Node D Node C carol a9a0198010...
DEEPER: TIMESTAMPS • I lied earlier • Columns are actually tuples of: { name, value, timestamp } • Distributed conflict resolution • Highest timestamp value wins! • Provided by client for every write
jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
jim age: 36 car: camaro gender: M 2011/01/01 12:35 2011/01/01 12:35 2011/01/01 12:35 carol age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 johnny age:12 gender: M 2011/05/15 07:35 2011/05/15 07:35 suzy age:10 gender: F 2011/09/17 19:13 2011/09/17 19:13
Per-Column Timestamp jim age: 36 car: camaro gender: M 2011/01/01 12:35 2011/01/01 12:35 2011/01/01 12:35 carol age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 johnny age:12 gender: M 2011/05/15 07:35 2011/05/15 07:35 suzy age:10 gender: F 2011/09/17 19:13 2011/09/17 19:13
jim age: 36 car: camaro gender: M 2011/01/01 12:35 2011/01/01 12:35 2011/01/01 12:35 carol age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 johnny age:12 gender: M 2011/05/15 07:35 2011/05/15 07:35 suzy age:10 gender: F 2011/09/17 19:13 2011/09/17 19:13
carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39
insert(“carol”, { “car”: “daewoo”, 2011/11/15 15:00 }) carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39
insert(“carol”, { “car”: “daewoo”, 2011/11/15 15:00 }) carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39
insert(“carol”, { “car”: “daewoo”, 2011/11/15 15:00 }) carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 2011/11/15 15:00 > 2011/01/01 12:38
insert(“carol”, { “car”: “daewoo”, 2011/11/15 15:00 }) carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39
insert(“carol”, { “car”: “daewoo”, 2011/11/15 15:00 }) carol¹ age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 Cassandra replicates data automagically
DEEPER: CONSISTENCY • I lied again left something out • All ops also have a consistency level • Controls number of replicas involved in an operation (read or write) • Eventual Consistency > Full Consistency • Use lowest you can get away with • Use quorum in place of “all” consistency
WRITE CONSISTENCY LEVEL: ONE carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 Writes to any replica and acknowledges to client, other nodes eventually get the update
WRITE CONSISTENCY LEVEL: QUORUM carol¹ age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 Writes to majority replicas, other nodes eventually get the update
READ CONSISTENCY LEVEL: ONE carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 Reads from first replica to respond to query, so could be inconsistent
READ CONSISTENCY LEVEL: QUORUM Timestamp resolves conflict, “daewoo” wins carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 Reads from majority replicas, so if paired with quorum writes, guarantees consistency
QUICKIE LIST-AS-COLUMNS TIMELINE COLUMN FAMILY time-uuid: time-uuid: time-uuid: jim “hello!” “echo!” “anyone!” time-uuid: time-uuid: carol “first!” “second!” Time-sortable globally unique IDs orders columns by their insertion time
THINK IN QUERIES • Break your normalization habit • Roughly ~one CF per query • Denormalize! • Use in-column entity caching
QUESTIONS YET?
PYCASSA CREATE A CONNECTION >>> pool = pycassa.ConnectionPool(“Keyspace”, server_list=[“192.168.1.1”]) Moar servers here will distribute this client’s load over multiple nodes in the cluster.
PYCASSA OPEN A COLUMN FAMILY >>> cf = pycassa.ColumnFamily(pool, “People”) Column Family
PYCASSA GET A ROW Row Key >>> cf.get(“carol”) { “age”: “37”, “car”: “subaru”, “gender”: “F” } All columns returned as a dictionary
PYCASSA GET SPECIFIC COLUMNS Row Key Column Names >>> cf.get(“carol”, columns=[“age”, “gender”]) { “age”: “37”, “gender”: “F” }
PYCASSA GET SLICE OF COLUMNS Row Key Column Name Range >>> cf.get(“carol”, column_start=”a”, column_end=”d”) { “age”: “37”, “car”: “subaru” }
PYCASSA “UPSERT” A COLUMN Column Name >>> cf.insert(“carol”, { “car”: “mercedes” }) Row Key Column Value
PYCASSA DELETE A ROW >>> cf.remove(“carol”) Row Key
PYCASSA DELETE A COLUMN Column Names >>> cf.remove(“carol”, columns=[“car”]) Row Key
PYCASSA CUSTOM TIMESTAMPS • Default is GMT timestamp • Overridden for inserts & removes • Per-CF generator function can be specified. Insert beats delete! >>> cf.insert(“carol”, { “car”: “subaru” }, timestamp=10000001) >>> cf.remove(“carol”, columns=[“car”], timestamp=10000000)
PYCASSA CONSISTENCY LEVEL • Can be overridden on all operations • Global default read & write is ONE • Per-CF override for default >>> cf.insert(“carol”, { “car”: “subaru” }, write_consistency_level=ConsistencyLevel.QUORUM) >>> cf.get(“carol”, read_consistency_level=ConsistencyLevel.QUORUM)
PYCASSA UUIDv4 >>> import uuid >>> cf.insert(uuid.uuid4().bytes_le, { “foo”: “bar” }) >>> cf.insert(“joe”, { uuid.uuid4(): “bar” })
PYCASSA TimeUUID (aka UUIDv1) >>> import uuid >>> timeline.insert(“joe”, { uuid.uuid1(): “hello!” })
DIDN’T COVER • CQL • Batch Operations • Schema Definition • SuperColumns (don’t use them) • Key Range Queries (don’t use them) • Secondary Indexes • ... and the list goes on
SHAMELESS • DataStax is the leading provider of Apache Cassandra training, support, and consulting services • DataStax Enterprise is Hadoop on top of Cassandra; really nice for operations • Seriously smart peeps, no lie • www.datastax.com
QUESTIONS?
THANKS http://www.datastax.com/docs/1.0/ http://www.datastax.com/dev/tutorials/ http://www.datastax.com/products/community/ http://pycassa.github.com/ http://apache.cassandra.org/ Twitter: @rbranson

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How Do I Cassandra?

  • 1.
    HOW DO I CASSANDRA? RickBranson, DataStax Memphis Python Users Group November 21st, 2011
  • 2.
    WHO? • This is my first day at DataStax, halp! • Coroutine for 18 months • American Roamer for 3 years • FedEx for 4 years
  • 3.
    OCCUPY SQL Why isit that 99% of the applications go into 1% of database softwares?
  • 4.
    HOW DO INOSQL? 1. Anvil transitions. Deal with it. 2. Find data store that doesn’t use SQL 3. ANYTHING 4. Cram all the things into it 5. Triumphantly blog this success 6. Complain a month later when it bursts into flames
  • 5.
    SEEMS LEGIT ... itreally is legit. There are real data sets that don’t make sense in the relational model, nor modern ACID databases.
  • 6.
    THE GREAT HAMMER ...but, the typical developer only has a relational database; everything looks like a nail.
  • 7.
    SNAKE OIL Typically, developerspick data structures suited to each use case. The purveyors of relational would have you believe everything fits into one mold.
  • 8.
    FIT WHAT INTOWHERE? Trees, Semi-Structured Data, Web Content, Multi-Dimensional Cubes, Graphs
  • 9.
    HAMMERS GONNA HAMMER It does work – so does COBOL. Just like a motion picture film, the speed of modern computers creates an illusion.
  • 10.
    MAKE A WHATOUTTA WHO? ASSUMPTIONS MADE ON YOUR BEHALF • Data must be synchronized everywhere, instantaneously • The structure of data doesn’t change over time • Hardware never fails and networks are perfect • Humans always do the right thing • No upgrading or patches are ever needed • Planned outages are good for the soul
  • 11.
    THE MOVEMENT “No onetool is appropriate for solving every problem.” ~ Cliff McKinney Gettysburg Address, cir. 1863
  • 12.
    SRS BZNS 10gen, Basho,DataStax, Cloudant, Cloudera, HortonWorks, VMware, Oracle Guess the number of craps Russ here gives about databases?
  • 13.
    MOVING ON OH: “Thelast DBA in the room went dataway”
  • 14.
    AN DEFINITION Cassandra – affectionately “C*” – is an open source, distributed store for structured data that scales-out on cheap, commodity hardware and stays up even when things get really bad.
  • 15.
    FUH-GET ABOUT IT Ruins availability • Strict Consistency • Transactions Deadlocks & Not scalable • Ad-hoc Queries • Indexes aka “Dumbass” Queries Scumbag index covers every column; ignored by planner
  • 16.
    THE REWARDS • Simplicity of Operations • Transparency • Very High Availability • Painless Scale-Out • Solid, Predictable Performance on Commodity and Cloud Servers
  • 17.
    Cassandra is abouttrading some developer time for operational pain – or 3am phone calls as some of us know it.
  • 18.
    DEM GOOD GENES Put simply: the scalable, efficient storage model from Google’s BigTable atop the incredibly fault resistant distributed design of Amazon’s Dynamo resisting, not just tolerating!
  • 19.
    VS OTHER NoSQL • Truly Distributed Design • No Special Servers, No SPOF • 2D Data Model • Rocks on Spinning Disks • Single-Server Durability
  • 20.
    HORRIBLE IDEAS THINGS NOT TO USE CASSANDRA FOR • Prototyping • Temporal Data • Distributed Locks • Message Queues • ... anywhere availability doesn’t matter
  • 21.
    UNBOUNDED HUMANS ...OR DATA THAT THRIVES ON CASSANDRA • Social Voluntary, human-generated data: text messages, comments, check- ins, real-time collaboration • Activity Involuntary or indirectly human-generated data: news feeds, timelines, clickstreams, system + application logs • Images Smaller files < ~10MB fit well into columns • ... anywhere availability is paramount, (not necessarily big data or scale either)
  • 22.
    TO START • easy_installpycassa • Pretty much ignore any info pre-0.7 • Use DataStax Community Edition • Unfortunately, the “official” wiki sucks, so use DataStax docs & tutorials • IRC, Mailing List, StackOverflow, Quora all monitored regularly and kick ass
  • 23.
    WHY CASSANDRA + PYTHON? • pycassa is one of the best client • Healthy Cassandra+Python Community • Many examples of real, solid, production Python applications built on- top of Cassandra
  • 24.
    PYTHON + CASSANDRA NOTABLEIMPLEMENTATIONS • Reddit Caches generated comment trees in Cassandra • UrbanAirship Delivers 20M push notifications per day; bursts to 100k/sec+ • GameFly behind their social network The database • Digg Pretty much the whole site
  • 25.
    DATA MODEL “I gotchacolumn family ratttt heeeeyyyaahhh!”
  • 26.
    AN COLUMN FAMILY jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
  • 27.
    AN COLUMN FAMILY • Don’t think in relational database terms • Columns are { name, value } tuples • It’s dictionaries all the way down... { “key”: { “name”: “value” } } Column Column Family Row
  • 28.
    Row Key jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
  • 29.
    Row Key jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F Rows are randomly ordered
  • 30.
    Columns jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
  • 31.
    Column Names jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age: 12 gender: M suzy age: 10 gender: F
  • 32.
    Column Names jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age: 12 gender: M suzy age: 10 gender: F Column Name dictates order
  • 33.
    Column Values jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age: 12 gender: M suzy age: 10 gender: F
  • 34.
    PARTITIONING Cassandra divides thedata set into ranges and distributes rows among multiple servers to achieve scale-out.
  • 35.
    Row Key determinesnode placement jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
  • 36.
    Row Key MD5 Hash jim 5e02739678... MD5 hash carol a9a0198010... operation yields a 128-bit number johnny f4eb27cea7... for keys of any size. suzy 78b421309e...
  • 37.
    Conceptually, hashed keysare placed along a ring representing the keyspace. Keyspace
  • 38.
    The ring isdivided up into ranges, one for each node in the cluster. Node A Node B Node D Node C
  • 39.
    In a 4-nodecluster, each node holds roughly a quarter of the rows. End (Token) Start Node A 0x00000000000000000000000000000000 0xc0000000000000000000000000000001 Node B 0x40000000000000000000000000000000 0x00000000000000000000000000000001 Node C 0x80000000000000000000000000000000 0x40000000000000000000000000000001 Node D 0xc0000000000000000000000000000000 0x80000000000000000000000000000001
  • 40.
    Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e...
  • 41.
    Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e...
  • 42.
    Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e...
  • 43.
    Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e...
  • 44.
    Start End A 0xc000000000..1 0x0000000000..0 B 0x0000000000..1 0x4000000000..0 C 0x4000000000..1 0x8000000000..0 D 0x8000000000..1 0xc000000000..0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e...
  • 45.
    REPLICATION Cassandra creates multiplecopies of rows so that if any one node is behaving badly, all of the data stays available and fully operational.
  • 46.
    REPLICATION • Column Family has “Replication Factor” • RF = total number of copies of each row • Don’t be fail; NEVER use 1 • Use 3+ in the “cloud” • Use 2+ on redundant hardware • Write operations on any replica
  • 47.
    Replicas are chosenby jumping to the next node(s) on the ring Node A Node B Node D Node C carol a9a0198010...
  • 48.
    Replication Factor =2 Node A Node B Node D Node C carol a9a0198010...
  • 49.
    Replication Factor =3 Node A Node B Node D Node C carol a9a0198010...
  • 50.
    DEEPER: TIMESTAMPS • Ilied earlier • Columns are actually tuples of: { name, value, timestamp } • Distributed conflict resolution • Highest timestamp value wins! • Provided by client for every write
  • 51.
    jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
  • 52.
    jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F
  • 53.
    jim age: 36 car: camaro gender: M 2011/01/01 12:35 2011/01/01 12:35 2011/01/01 12:35 carol age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 johnny age:12 gender: M 2011/05/15 07:35 2011/05/15 07:35 suzy age:10 gender: F 2011/09/17 19:13 2011/09/17 19:13
  • 54.
    Per-Column Timestamp jim age: 36 car: camaro gender: M 2011/01/01 12:35 2011/01/01 12:35 2011/01/01 12:35 carol age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 johnny age:12 gender: M 2011/05/15 07:35 2011/05/15 07:35 suzy age:10 gender: F 2011/09/17 19:13 2011/09/17 19:13
  • 55.
    jim age: 36 car: camaro gender: M 2011/01/01 12:35 2011/01/01 12:35 2011/01/01 12:35 carol age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 johnny age:12 gender: M 2011/05/15 07:35 2011/05/15 07:35 suzy age:10 gender: F 2011/09/17 19:13 2011/09/17 19:13
  • 56.
    carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39
  • 57.
    insert(“carol”, { “car”: “daewoo”, 2011/11/15 15:00 }) carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39
  • 58.
    insert(“carol”, { “car”: “daewoo”, 2011/11/15 15:00 }) carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39
  • 59.
    insert(“carol”, { “car”: “daewoo”, 2011/11/15 15:00 }) carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 2011/11/15 15:00 > 2011/01/01 12:38
  • 60.
    insert(“carol”, { “car”: “daewoo”, 2011/11/15 15:00 }) carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39
  • 61.
    insert(“carol”, { “car”: “daewoo”, 2011/11/15 15:00 }) carol¹ age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 Cassandra replicates data automagically
  • 62.
    DEEPER: CONSISTENCY • Ilied again left something out • All ops also have a consistency level • Controls number of replicas involved in an operation (read or write) • Eventual Consistency > Full Consistency • Use lowest you can get away with • Use quorum in place of “all” consistency
  • 63.
    WRITE CONSISTENCY LEVEL: ONE carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 Writes to any replica and acknowledges to client, other nodes eventually get the update
  • 64.
    WRITE CONSISTENCY LEVEL: QUORUM carol¹ age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 Writes to majority replicas, other nodes eventually get the update
  • 65.
    READ CONSISTENCY LEVEL: ONE carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 Reads from first replica to respond to query, so could be inconsistent
  • 66.
    READ CONSISTENCY LEVEL: QUORUM Timestamp resolves conflict, “daewoo” wins carol¹ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 carol² age: 37 car: daewoo gender: F 2011/01/01 12:38 2011/11/15 15:00 2011/01/01 12:39 carol³ age: 37 car: subaru gender: F 2011/01/01 12:38 2011/01/01 12:38 2011/01/01 12:39 Reads from majority replicas, so if paired with quorum writes, guarantees consistency
  • 67.
    QUICKIE LIST-AS-COLUMNS TIMELINE COLUMNFAMILY time-uuid: time-uuid: time-uuid: jim “hello!” “echo!” “anyone!” time-uuid: time-uuid: carol “first!” “second!” Time-sortable globally unique IDs orders columns by their insertion time
  • 68.
    THINK IN QUERIES • Break your normalization habit • Roughly ~one CF per query • Denormalize! • Use in-column entity caching
  • 69.
  • 70.
    PYCASSA CREATE A CONNECTION >>> pool = pycassa.ConnectionPool(“Keyspace”, server_list=[“192.168.1.1”]) Moar servers here will distribute this client’s load over multiple nodes in the cluster.
  • 71.
    PYCASSA OPEN A COLUMN FAMILY >>> cf = pycassa.ColumnFamily(pool, “People”) Column Family
  • 72.
    PYCASSA GET A ROW Row Key >>> cf.get(“carol”) { “age”: “37”, “car”: “subaru”, “gender”: “F” } All columns returned as a dictionary
  • 73.
    PYCASSA GET SPECIFIC COLUMNS Row Key Column Names >>> cf.get(“carol”, columns=[“age”, “gender”]) { “age”: “37”, “gender”: “F” }
  • 74.
    PYCASSA GET SLICE OF COLUMNS Row Key Column Name Range >>> cf.get(“carol”, column_start=”a”, column_end=”d”) { “age”: “37”, “car”: “subaru” }
  • 75.
    PYCASSA “UPSERT” A COLUMN Column Name >>> cf.insert(“carol”, { “car”: “mercedes” }) Row Key Column Value
  • 76.
    PYCASSA DELETE A ROW >>> cf.remove(“carol”) Row Key
  • 77.
    PYCASSA DELETE A COLUMN Column Names >>> cf.remove(“carol”, columns=[“car”]) Row Key
  • 78.
    PYCASSA CUSTOM TIMESTAMPS • Default is GMT timestamp • Overridden for inserts & removes • Per-CF generator function can be specified. Insert beats delete! >>> cf.insert(“carol”, { “car”: “subaru” }, timestamp=10000001) >>> cf.remove(“carol”, columns=[“car”], timestamp=10000000)
  • 79.
    PYCASSA CONSISTENCY LEVEL • Can be overridden on all operations • Global default read & write is ONE • Per-CF override for default >>> cf.insert(“carol”, { “car”: “subaru” }, write_consistency_level=ConsistencyLevel.QUORUM) >>> cf.get(“carol”, read_consistency_level=ConsistencyLevel.QUORUM)
  • 80.
    PYCASSA UUIDv4 >>> import uuid >>> cf.insert(uuid.uuid4().bytes_le, { “foo”: “bar” }) >>> cf.insert(“joe”, { uuid.uuid4(): “bar” })
  • 81.
    PYCASSA TimeUUID (aka UUIDv1) >>> import uuid >>> timeline.insert(“joe”, { uuid.uuid1(): “hello!” })
  • 82.
    DIDN’T COVER • CQL • Batch Operations • Schema Definition • SuperColumns (don’t use them) • Key Range Queries (don’t use them) • Secondary Indexes • ... and the list goes on
  • 83.
    SHAMELESS • DataStax isthe leading provider of Apache Cassandra training, support, and consulting services • DataStax Enterprise is Hadoop on top of Cassandra; really nice for operations • Seriously smart peeps, no lie • www.datastax.com
  • 84.
  • 85.