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Cassie

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

Requirements

  • Java SE 6
  • Scala 2.8
  • Cassandra 0.8

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" % "0.6.0"

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 seed hosts will be queried for a full list of nodes in the cluster.

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

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".) Alternative host discovery methods, like ServerSets, are TODO.

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 implicit Codec parameters for the given type.

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

strings.insert("newstring", Column("colname", "colvalue"))

The insert method looks for implicit parameters of type Codec[String] to convert the key, name and value to byte arrays. In this case, the codecs package already provides Utf8Codec as an implicit parameter, so the conversion is seamless. Cassie handles String and Array[Byte] instances out of the box, and also provides some useful non-standard types:

  • AsciiString: character sequence encoded with US-ASCII
  • FixedInt: 32-bit integer stored as a 4-byte sequence
  • FixedLong: 64-bit integer stored as an 8-byte sequence
  • VarInt: 32-bit integer stored using Avro's variable-length integer encoding
  • VarLong: 64-bit integer stored using Avro's variable-length integer encoding

These types also have implicit conversions defined, so if you have an instance of ColumnFamily[String, String, VarLong] you can use regular Longs.

Accessing Column Families

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

val people  = keyspace.columnFamily[String, String, String]("People", MicrosecondEpochClock)
val numbers = keyspace.columnFamily[String, String, VarInt]("People", MicrosecondEpochClock,
                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.

TODO: write or link to docs on Futures

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.

You can also get a set of columns:

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

This returns a Future[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]]]] which maps row keys to column names to columns.

Iterating Through Rows

Cassie provides functionality for iterating through the rows of a column family. This works with both the random partitioner and the order-preserving partitioner.

It does this by requesting a certain number of rows, starting with the first possible row ("") and ending with the last row possible row (""). The last key of the returned rows is then used as the start key for the next request, until either no rows are returned or the last row is returned twice.

(The performance hit in this is that the last row of one request will be the first row of the next.)

You can iterate over every column of every row:

for ((key, col) <- people.rowIteratee(100) {
  println(" Found column %s in row %s", col, key)
}

(This gets 100 rows at a time.)

Or just one column from every row:

for ((key, col) <- people.columnIteratee(100, "name") {
  println(" Found column %s in row %s", col, key)
}

Or a set of columns from every row:

for ((key, col) <- people.columnsIteratee(100, Set("name", "motto")) {
  println(" Found column %s in row %s", col, key)
}

The 'ColumnIteratee' object returned by these methods implements Iterable for use in loops like those shown, but it also allows for async iteration. An Iteratee contains a batch of values, and has a hasNext() method indicating whether more batches are available. If more batches are available, continue() will request the next batch and return a Future[Iteratee].

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))

Or even insert column names and values of a different type than those of the ColumnFamily:

people.insert("biscuitfoof", Column[AsciiString, AsciiString]("name", "Biscuit"))
people.insert("biscuitfoof", Column[AsciiString, AsciiString]("motto", "Mlalm."))

Batch operations are also possible:

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

(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 one of Cassie's strictly-increasing Clock implementations -- NanosecondEpochClock is recommended). 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.

LexicalUUIDs can be used as column names, in which case they're stored as 16-byte values and are sortable by LexicalUUIDType, or as keys, in which case they're stored as traditional, hex-encoded strings. Cassie provides implicit conversions between LexicalUUID and String:

val uuid = LexicalUUID(people.clock)

people.insert(uuid, Column("one", "two")) // converted to hex automatically

people.insert("key", Column(uuid, "what")) // converted to a byte array

TODO counter column families

Things What Ain't Done Yet

  • Anything relating to super columns
  • Range queries
  • Authentication
  • Counting
  • Meta data (e.g., describe_*)

Why? I don't need it yet.

Thanks

Many thanks to:

  • Cliff Moon
  • James Golick
  • Robert J. Macomber

License

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

Published under The MIT License, see LICENSE

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