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A Scala client for Cassandra

README.md

Cassie

Cassie is a small, lightweight Cassandra client built on Finagle with with all that provides plus column name/value encoding and decoding.

It is heavily used in production at Twitter so such be considered stable, yet it is incomplete in that it doesn't support the full feature set of Cassandra and will continue to evolve.

Requirements

  • Java SE 6
  • Scala 2.8
  • Cassandra 0.8 or later
  • sbt 0.7

Note that Cassie is usable from Java. Its not super easy, but we're working to make it easier.

Let's Get This Party Started

In your simple-build-tool project file, add Cassie as a dependency:

val twttr = "Twitter's Repository" at "http://maven.twttr.com/"
val cassie = "com.twitter" % "cassie" % "0.19.0"

Finagle

Before going further, you should probably learn about Finagle and its paradigm for asynchronous computing– https://github.com/twitter/finagle.

Connecting To Your Cassandra Cluster

First create a cluster object, passing in a list of seed hosts. By default, when creating a connection to a Keyspace, the given hosts will be queried for a full list of nodes in the cluster. If you don't want to report stats use NullStatsReceiver.

val cluster = new Cluster("host1,host2", OstrichStatsReceiver)

Then create a Keyspace instance which will use Finagle to maintain per-node connection pools and do retries:

val keyspace = cluster.keyspace("MyCassieApp").connect()
// see KeyspaceBuilder for more options here. Try the defaults first.

(If you have some nodes with dramatically different latency—e.g., in another data center–or if you have a huge cluster, you can disable keyspace mapping via "mapHostsEvery(0.minutes)" in which case clients will connect directly to the seed hosts passed to "new Cluster".)

A Quick Note On Timestamps

Cassandra uses client-generated timestamps to determine the order in which writes and deletes should be processed. Cassie previously came with several different clock implementations. Now all Cassie users use the MicrosecondEpochClock and timestamps should be mostly hidden from users.

A Longer Note, This Time On Column Names And Values

Cassandra stores the name and value of a column as an array of bytes. To convert these bytes to and from useful Scala types, Cassie uses Codec parameters for the given type.

For example, take adding a column to a column family of UTF-8 strings:

val strings = keyspace.columnFamily[Utf8Codec, Utf8Codec, Utf8Codec]
strings.insert("newstring", Column("colname", "colvalue"))

The insert method here requires a String and Column[String, String] because the type parameters of the columnFamily call were all Codec[String]. The conversion between Strings and ByteArrays will be seamless. Cassie has codecs for a number of data types already:

  • Utf8Codec: character sequence encoded with UTF-8
  • IntCodec: 32-bit integer stored as a 4-byte sequence
  • LongCodec: 64-bit integer stored as an 8-byte sequence
  • LexicalUUIDCodec a UUID stored as a 16-byte sequence
  • ThriftCodec a Thrift struct stored as variable-length sequence of bytes

Accessing Column Families

Once you've got a Keyspace instance, you can load your column families:

val people  = keyspace.columnFamily[Utf8Codec, Utf8Codec, Utf8Codec]("People")
val numbers = keyspace.columnFamily[Utf8Codec, Utf8Codec, IntCodec]("People",
                defaultReadConsistency = ReadConsistency.One,
                defaultWriteConsistency = WriteConsistency.Any)

By default, ColumnFamily instances have a default ReadConsistency and WriteConsistency of Quorum, meaning reads and writes will only be considered successful if a quorum of the replicas for that key respond successfully. You can change this default or simply pass a different consistency level to specific read and write operations.

Reading Data From Cassandra

Now that you've got your ColumnFamily, you can read some data from Cassandra:

people.getColumn("codahale", "name")

getColumn returns an Future[Option[Column[Name, Value]]] where Name and Value are the type parameters of the ColumnFamily. If the row or column doesn't exist, None is returned. Explaining Futures is out of scope for this README, go the Finagle docs to learn more. But in essence you can do this:

people.getColumn("codahale", "name") map { _ match { case col: Some(Column[String, String]) => # we have data case None => # there was no column } } handle { case e => { # there was an exception, do something about it } }

This whole block returns a Future which will be satisfied when the thrift rpc is done and the callbacks have run.

Anyway, continuing– you can also get a set of columns:

people.getColumns("codahale", Set("name", "motto"))

This returns a Future[java.util.Map[Name, Column[Name, Value]]], where each column is mapped by its name.

If you want to get all columns of a row, that's cool too:

people.getRow("codahale")

Cassie also supports multiget for columns and sets of columns:

people.multigetColumn(Set("codahale", "darlingnikles"), "name")
people.multigetColumns(Set("codahale", "darlingnikles"), Set("name", "motto"))

multigetColumn returns a Future[Map[Key, Map[Name, Column[Name, Value]]]] whichmaps row keys to column names to columns.

Asynchronous Iteration Through Rows and Columns

NOTE: This is new/experimental and likely to change in the future.

Cassie provides functionality for iterating through the rows of a column family and columns in a row. This works with both the random partitioner and the order-preserving partitioner, though iterating through rows in the random partitioner had undefined order.

You can iterate over every column of every row:

val finished = cf.rowsIteratee(100).foreach { case(key, columns) => println(key) //this function is executed async for each row println(cols) } finished() //this is a Future[Unit]. wait on it to know when the iteration is done

This gets 100 rows at a time and calls the above partial function on each one.

Writing Data To Cassandra

Inserting columns is pretty easy:

people.insert("codahale", Column("name", "Coda"))
people.insert("codahale", Column("motto", "Moar lean."))

You can insert a value with a specific timestamp:

people.insert("darlingnikles", Column("name", "Niki").timestamp(200L))
people.insert("darlingnikles", Column("motto", "Told ya.").timestamp(201L))

Batch operations are also possible:

people.batch() { cf =>
  cf.insert("puddle", Column("name", "Puddle"))
  cf.insert("puddle", Column("motto", "Food!"))
}.execute()

(See BatchMutationBuilder for a better idea of which operations are available.)

Deleting Data From Cassandra

First, it's important to understand exactly how deletes work in a distributed system like Cassandra.

Once you've read that, then feel free to remove a column:

people.removeColumn("puddle", "name")

Or a set of columns:

people.removeColumns("puddle", Set("name", "motto"))

Or even a row:

people.removeRow("puddle")

Generating Unique IDs

If you're going to be storing data in Cassandra and don't have a naturally unique piece of data to use as a key, you've probably looked into UUIDs. The only problem with UUIDs is that they're mental, requiring access to MAC addresses or Gregorian calendars or POSIX ids. In general, people want UUIDs which are:

  • Unique across a large set of workers without requiring coordination.
  • Partially ordered by time.

Cassie's LexicalUUIDs meet these criteria. They're 128 bits long. The most significant 64 bits are a timestamp value (from Cassie's strictly-increasing Clock implementation). The least significant 64 bits are a worker ID, with the default value being a hash of the machine's hostname.

When sorted using Cassandra's LexicalUUIDType, LexicalUUIDs will be partially ordered by time -- that is, UUIDs generated in order on a single process will be totally ordered by time; UUIDs generated simultaneously (i.e., within the same clock tick, given clock skew) will not have a deterministic order; UUIDs generated in order between single processes (i.e., in different clock ticks, given clock skew) will be totally ordered by time.

See Lamport. Time, clocks, and the ordering of events in a distributed system. Communications of the ACM (1978) vol. 21 (7) pp. 565 and Mattern. Virtual time and global states of distributed systems. Parallel and Distributed Algorithms (1989) pp. 215–226 for a more thorough discussion.

Things What Ain't Done Yet

  • Authentication
  • Meta data (e.g., describe_*)

Thanks

Many thanks to (pre twitter fork):

  • Cliff Moon
  • James Golick
  • Robert J. Macomber

License

Copyright (c) 2010 Coda Hale Copyright (c) 2011-2012 Twitter, Inc.

Published under The Apache 2.0 License, see LICENSE.

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