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Reactive type-safe Scala driver for Cassandra/Datastax Enteprise
Scala
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phantom-connectors/src Release/1.8.0: Releasing the new QueryBuilder and changeset for 1.8.0.
phantom-dsl/src PHANTOM-116: Fixing support for Thrift columns and blob columns.
phantom-example/src Release/1.8.0: Releasing the new QueryBuilder and changeset for 1.8.0.
phantom-scalatra-test/src Release/1.8.0: Releasing the new QueryBuilder and changeset for 1.8.0.
phantom-spark/src/main/scala/com/websudos/phantom/spark Releasing the new QueryBuilder.
phantom-testkit/src/main Release/1.8.0: Releasing the new QueryBuilder and changeset for 1.8.0.
phantom-thrift/src PHANTOM-116: Fixing support for Thrift columns and blob columns.
phantom-udt/src Release/1.8.0: Releasing the new QueryBuilder and changeset for 1.8.0.
phantom-zookeeper/src Release/1.8.0: Releasing the new QueryBuilder and changeset for 1.8.0.
project Bumping version
.gitignore Release/1.5.0: Official Scala 2.11 support in phantom.
.travis.yml Trying to fix resolution issues of Twitter artefacts.
CONTRIBUTING.md Release/1.5.0: Official Scala 2.11 support in phantom.
LICENSE.txt Releasing the new QueryBuilder.
README.md Removing usage of long order keys.
changelog.md Release/1.5.0: Official Scala 2.11 support in phantom.
scalastyle-config.xml Releasing the new QueryBuilder.

README.md

phantom Build Status Coverage Status Maven Central

Reactive type-safe Scala DSL for Cassandra

To stay up-to-date with our latest releases and news, follow us on Twitter: @websudos.

If you use phantom, please consider adding your company to our list of adopters. Phantom is and will always be completely free and open source, but the more adopters our projects have, the more people from our company will actively work to make them better.

phantom

Using phantom

Scala 2.10 and 2.11 releases

We publish phantom in 2 formats, stable releases and bleeding edge.

  • The stable release is always available on Maven Central and will have a version with a patch number "0". E.g "x.x.0".

  • Intermediary releases are available through our managed Maven repository,"Websudos releases" at "http://maven.websudos.co.uk/ext-release-local".

Latest versions

  • Latest stable version: 1.8.0 (Maven Central)
  • Bleeding edge: 1.8.0 (Websudos Maven Repo)

You will also be needing the default resolvers for Maven Central and the typesafe releases. Phantom will never rely on any snapshots or be published as a snapshot version, the bleeding edge is always subject to internal scrutiny before any releases into the wild.

The Apache Cassandra version used for auto-embedding Cassandra during tests is: val cassandraVersion = "2.1.0-rc5". You will require JDK 7 to use Cassandra, otherwise you will get an error when phantom tries to start the embedded database. The recommended JDK is the Oracle variant.

Version highlights and upcoming features

Breaking API changes in Phantom 1.8.0 and beyond.

The 1.8.0 release constitutes a major re-working of a wide number of internal phantom primitives, including but not limited to a brand new Scala flavoured QueryBuilder with full support for all CQL 3 features and even some of the more "esoteric" options available in CQL. We went above and beyond to try and offer a tool that's comprehensive and doesn't miss out on any feature of the protocol, no matter how small.

If you are wondering what happened to 1.7.0, it was never publicly released as testing the new querybuilder entailed serious internal efforts and for such a drastic change we wanted to do as much as possible to eliminate books. Surely there will be some still found, but hopefully very few and with your help they will be very short lived.

Ditching the Java Driver was not a question of code quality in the driver, but rather an opportunity to exploit the more advanced Scala type system features to introduce behaviour such as preventing duplicate limits on queries using phantom types, to prevent even more invalid queries from compiling, and to switch to a fully immutable QueryBuilder that's more in tone with idiomatic Scala, as opposed to the Java-esque mutable alternative already existing the java driver.

A new import structure

import com.websudos.phantom.Implicits._ has now been renamed to import com.websudos.phantom.dsl._. The old import is still there but deprecated.

A natural question you may ask is why we resorted to seemingly unimportant changes, but the goal here was to enforce the new implicit mechanism and use a uniform importing experience across all modules. So you can have the series of import com.websudos.phantom.dsl._, import com.websudos.phantom.thrift._, import com.websudos.phantom.testkit._ and so on, all identical, all using Scala package object definitions as intended.

Propagating parse errors

Until now, our implementation of Cassandra primitives has been based on the Datastax Java Driver and on an Option based DSL. This made it heard to deal with parse errors at runtime, specifically those situations when the DSL was unable to parse the required type from the Cassandra result or in a simple case where `null was returned for a non-optional column.

The core of the Column[Table, Record, ValueType].apply(value: ValueType] method which was used to parse rows in a type safe manner was written like this:

import com.datastax.driver.core.Row

def apply(row: Row):  = optional(row).getOrElse(throw new Exception("Couldn't parse things")

This approach left the original exception which caused the parser to parse a null and subsequently a None was ignored.

With the new type-safe primitive interface that no longer relies on the Datastax Java driver we were also able to move the Option based parsing mechanism to a Try mechanism which will now log all parse errors un-altered, in the exact same way the are thrown at compile time, using the logger for the given table.

Internally, we are now using something like this:

   def optional(r: Row): Try[T]

   def apply(r: Row): T = optional(r) match {
     case Success(value) => value
     case Failure(ex) => {
       table.logger.error(ex.getMessage)
       throw ex
     }
   }

The exception is now logged and propagated with no interference. We intercept it to provide consistent logging in the same table logger where you would naturally monitor for logs.

Improving query performance

Play enumerators and Twitter ResultSpools have been removed from the default one, get, fetch and collect methods. You will have to explicitly call fetchEnumerator and fetchSpool if you want result throttling through async lazy iterators. This will offer everyone a signifact performance improvement over query performance. Async iterators needed a lot of expensive "magic" to work properly, but you don't always need to fold over 100k records. That behaviour was implemented both as means of showing off as well as doing all in one loads like the Spark - Cassandra connector performs. E.g dumping C* data into HDFS or whatever backup system. A big 60 - 70% gain should be expected.

Phantom connectors now require an implicit com.websudos.phantom.connectors.KeySpace to be defined. Instead of using a plain string, you just have to use KeySpace.apply or simply: trait MyConnector extends Connector { implicit val keySpace = KeySpace("your_def") }. This change allows us to replace the existing connector model and vastly improve the number of concurrent cluster connections required to perform operations on various keyspaces. Insteaed of the 1 per keyspace model, we can now successfully re-use the same session without evening needing to switch as phantom will use the full CQL reference syntax, e.g SELECT FROM keyspace.table instead of SELECY FROM table.

A entirely new set of options have been enabled in the type safe DSLs. You can now alter tables, specify advanced compressor behaviour and so forth, all from within phantom and with the guarantee of auto-completion and type safety.

Support for ALTER queries.

This was never possible before in phantom, and now from 1.7.0 onwards we feature full support for using ALTER queries.

Table of contents

resolvers ++= Seq(
  "Typesafe repository snapshots" at "http://repo.typesafe.com/typesafe/snapshots/",
  "Typesafe repository releases" at "http://repo.typesafe.com/typesafe/releases/",
  "Sonatype repo"                    at "https://oss.sonatype.org/content/groups/scala-tools/",
  "Sonatype releases"                at "https://oss.sonatype.org/content/repositories/releases",
  "Sonatype snapshots"               at "https://oss.sonatype.org/content/repositories/snapshots",
  "Sonatype staging"                 at "http://oss.sonatype.org/content/repositories/staging",
  "Java.net Maven2 Repository"       at "http://download.java.net/maven/2/",
  "Twitter Repository"               at "http://maven.twttr.com",
  "Websudos releases"                at "http://maven.websudos.co.uk/ext-release-local"
)

For most things, all you need is phantom-dsl and phantom-testkit. Read through for information on other modules.

libraryDependencies ++= Seq(
  "com.websudos"  %% "phantom-dsl"                   % phantomVersion,
  "com.websudos"  %% "phantom-testkit"               % phantomVersion
)

The full list of available modules is:

libraryDependencies ++= Seq(
  "com.websudos"  %% "phantom-dsl"                   % phantomVersion,
  "com.websudos"  %% "phantom-example"               % phantomVersion,
  "com.websudos"  %% "phantom-scalatra"              % phantomVersion,
  "com.websudos"  %% "phantom-spark"                 % phantomVersion,
  "com.websudos"  %% "phantom-thrift"                % phantomVersion,
  "com.websudos"  %% "phantom-testkit"               % phantomVersion,
  "com.websudos"  %% "phantom-udt"                   % phantomVersion,
  "com.websudos"  %% "phantom-zookeeper"             % phantomVersion
)

If you include phantom-zookeeper, make sure to add the following resolvers:

resolvers += "twitter-repo" at "http://maven.twttr.com"

resolvers += "websudos-repo" at "http://maven.websudos.co.uk/ext-release-local"

Primitive columns

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This is the list of available columns and how they map to C* data types. This also includes the newly introduced static columns in C* 2.0.6.

The type of a static column can be any of the allowed primitive Cassandra types. phantom won't let you mixin a non-primitive via implicit magic.

phantom columns Java/Scala type Cassandra type
BlobColumn java.nio.ByteBuffer blog
BigDecimalColumn scala.math.BigDecimal decimal
BigIntColumn scala.math.BigInt varint
BooleanColumn scala.Boolean boolean
DateColumn java.util.Date timestamp
DateTimeColumn org.joda.time.DateTime timestamp
DoubleColumn scala.Double double
EnumColumn scala.Enumeration text
FloatColumn scala.Float float
IntColumn scala.Int int
InetAddressColumn java.net.InetAddress inet
LongColumn scala.Long long
StringColumn java.lang.String text
UUIDColumn java.util.UUID uuid
TimeUUIDColumn java.util.UUID timeuuid
CounterColumn scala.Long counter
StaticColumn<type> <type> type static

Optional primitive columns

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Optional columns allow you to set a column to a null or a None. Use them when you really want something to be optional. The outcome is that instead of a T you get an Option[T] and you can match, fold, flatMap, map on a None.

The Optional part is handled at a DSL level, it's not translated to Cassandra in any way.

phantom columns Java/Scala type Cassandra columns
OptionalBlobColumn Option[java.nio.ByteBuffer] blog
OptionalBigDecimalColumn Option[scala.math.BigDecimal] decimal
OptionalBigIntColumn Option[scala.math.BigInt] varint
OptionalBooleanColumn Option[scala.Boolean] boolean
OptionalDateColumn Option[java.util.Date] timestamp
OptionalDateTimeColumn Option[org.joda.time.DateTime] timestamp
OptionalDoubleColumn Option[scala.Double] double
OptionalEnumColumn Option[scala.Enumeration] text
OptionalFloatColumn Option[scala.Float] float
OptionalIntColumn Option[scala.Int] int
OptionalInetAddressColumn Option[java.net.InetAddress] inet
OptionalLongColumn Option[Long] long
OptionalStringColumn Option[java.lang.String] text
OptionalUUIDColumn Option[java.util.UUID] uuid
OptionalTimeUUID Option[java.util.UUID] timeuuid

Collection columns

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Cassandra collections do not allow custom data types. Storing JSON as a string is possible, but it's still a text column as far as Cassandra is concerned. The type in the below example is always a default C* type.

JSON columns require you to define a toJson and fromJson method, telling phantom how to go from a String to the type you need. It makes no assumptions as to what library you are using, although we have tested with lift-json and play-json.

Examples on how to use JSON columns can be found in JsonColumnTest.scala

phantom columns Cassandra columns
ListColumn.<type> list<type>
SetColumn.<type> set<type>
MapColumn.<type, type> map<type, type>
JsonColumn.<type> text
JsonListColumn.<type> list<text>
JsonSetColumn.<type> set<type>

Indexing columns

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phantom uses a specific set of traits to enforce more advanced Cassandra limitations and schema rules at compile time. Instead of waiting for Cassandra to tell you you've done bad things, phantom won't let you compile them, saving you a lot of time.

The error messages you get when your model is off with respect to Cassandra rules is not particularly helpful and we are working on a better builder to allow for better error messages. Until then, if you see things like:

import com.websudos.phantom.dsl._

case class Student(id: UUID, name: String)

class Students extends CassandraTable[Students, Student] {
  object id extends UUIDColumn(this) with PartitionKey[UUID]
  object name extends StringColumn(this)

  def fromRow(row: Row): Student = {
    Student(id(row), name(row))
  }
}

object Students extends Students with Connector {

  /**
   * The below code will result in a compilation error phantom produces by design.
   * This behaviour is not only correct with respect to CQL but also intended by the implementation.
   *
   * The reason why it won't compile is because the "name" column is not an index in the "Students" table, which means using "name" in a "where" clause is
   * invalid CQL. Phantom prevents you from running most invalid queries by simply giving you a compile time error instead.
   */
  def getByName(name: String): Future[Option[Student]] = {
    select.where(_.name eqs name).one()
  }
}

The compilation error message for the above looks something like this:

 value eqs is not a member of object x$9.name

Might seem overly mysterious to start with, but the logic is simple. There is no implicit conversion in scope to convert your non-indexed column to a QueryColumn. If you don't have an index, you can't query.

  Students.update.where(_.id eqs someId).onlyIf(_.name is "test")

PartitionKey

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This is the default partitioning key of the table, telling Cassandra how to divide data into partitions and store them accordingly. You must define at least one partition key for a table. Phantom will gently remind you of this with a fatal error.

If you use a single partition key, the PartitionKey will always be the first PrimaryKey in the schema.

It looks like this in CQL: PRIMARY_KEY(your_partition_key, primary_key_1, primary_key_2).

Using more than one PartitionKey[T] in your schema definition will output a Composite Key in Cassandra. PRIMARY_KEY((your_partition_key_1, your_partition_key2), primary_key_1, primary_key_2).

PrimaryKey

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As it's name says, using this will mark a column as PrimaryKey. Using multiple values will result in a Compound Value. The first PrimaryKey is used to partition data. phantom will force you to always define a PartitionKey so you don't forget about how your data is partitioned. We also use this DSL restriction because we hope to do more clever things with it in the future.

A compound key in C* looks like this: PRIMARY_KEY(primary_key, primary_key_1, primary_key_2).

Before you add too many of these, remember they all have to go into a where clause. You can only query with a full primary key, even if it's compound. phantom can't yet give you a compile time error for this, but Cassandra will give you a runtime one.

SecondaryIndex

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This is a SecondaryIndex in Cassandra. It can help you enable querying really fast, but it's not exactly high performance. It's generally best to avoid it, we implemented it to show off what good guys we are.

When you mix in Index[T] on a column, phantom will let you use it in a where clause. However, don't forget to allowFiltering for such queries, otherwise C* will give you an error.

ClusteringOrder

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This can be used with either java.util.Date or org.joda.time.DateTime. It tells Cassandra to store records in a certain order based on this field.

An example might be: object timestamp extends DateTimeColumn(this) with ClusteringOrder[DateTime] with Ascending To fully define a clustering column, you MUST also mixin either Ascending or Descending to indicate the sorting order.

Thrift Columns

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These columns are especially useful if you are building Thrift services. They are deeply integrated with Twitter Scrooge and relevant to the Twitter ecosystem(Finagle, Zipkin, Storm etc) They are available via the phantom-thrift module and you need to import the Thrift package to get all necessary types into scope.

 import com.websudos.phantom.thrift._

In the below scenario, the Cassandra type is always text and the type you need to pass to the column is a Thrift struct, specifically com.twitter.scrooge .ThriftStruct. phantom will use a CompactThriftSerializer, store the record as a binary string and then reparse it on fetch.

Thrift serialization and de-serialization is extremely fast, so you don't need to worry about speed or performance overhead. You generally use these to store collections(small number of items), not big things.

phantom columns Cassandra columns
ThriftColumn.<type> text
ThriftListColumn.<type> list<text>
ThriftSetColumn.<type> set<text>
ThriftMapColumn.<type, type> map<text, text>

Data modeling with phantom

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import java.util.Date
import com.websudos.phantom.sample.ExampleModel
import com.websudos.phantom.dsl._

case class ExampleModel (
  id: Int,
  name: String,
  props: Map[String, String],
  timestamp: Int,
  test: Option[Int]
)

sealed class ExampleRecord extends CassandraTable[ExampleRecord, ExampleModel] {

  object id extends UUIDColumn(this) with PartitionKey[UUID]
  object timestamp extends DateTimeColumn(this) with ClusteringOrder[DateTime] with Ascending
  object name extends StringColumn(this)
  object props extends MapColumn[ExampleRecord, ExampleModel, String, String](this)
  object test extends OptionalIntColumn(this)

  def fromRow(row: Row): ExampleModel = {
    ExampleModel(id(row), name(row), props(row), timestamp(row), test(row));
  }
}

Querying with Phantom

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The query syntax is inspired by the Foursquare Rogue library and aims to replicate CQL 3 as much as possible.

Phantom works with both Scala Futures and Twitter Futures as first class citizens.

Common query methods

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The full list can be found in CQLQuery.scala.

Method name Description
tracing_= The Cassandra utility method. Enables or disables tracing.
queryString Get the output CQL 3 query of a phantom query.
consistencyLevel Retrieves the consistency level in use.
consistencyLevel_= Sets the consistency level to use.
retryPolicy Retrieves the RetryPolicy in use.
retryPolicy_= Sets the RetryPolicy to use.
serialConsistencyLevel Retrieves the serial consistency level in use.
serialConsistencyLevel_= Sets the serial consistency level to use.
forceNoValues_= Sets the serial consistency level to use.
routingKey Retrieves the Routing Key as a ByteBuffer.

Select queries

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Method name Description
where The WHERE clause in CQL
and Chains several clauses, creating a WHERE ... AND query
orderBy Adds an ORDER_BY column_name to the query
allowFiltering Allows Cassandra to filter records in memory. This is an expensive operation.
limit Sets the exact number of records to retrieve.

Select queries are very straightforward and enforce most limitations at compile time.

where and and clause operators

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Operator name Description
eqs The "equals" operator. Will match if the objects are equal
in The "in" operator. Will match if the object is found the list of arguments
gt The "greater than" operator. Will match a the record is greater than the argument and exists
gte The "greater than or equals" operator. Will match a the record is greater than the argument and exists
lt The "lower than" operator. Will match a the record that is less than the argument and exists
lte The "lower than or equals" operator. Will match a the record that is less than the argument and exists

Partial selects

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All partial select queries will return Tuples and are therefore limited to 22 fields. We haven't yet bothered to add more than 10 fields in the select, but you can always do a Pull Request. The file you are looking for is here. The 22 field limitation will change in Scala 2.11 and phantom will be updated once cross version compilation is enabled.

  def getNameById(id: UUID): Future[Option[String]] = {
    ExampleRecord.select(_.name).where(_.id eqs someId).one()
  }

  def getNameAndPropsById(id: UUID): Future[Option(String, Map[String, String])] {
    ExampleRecord.select(_.name, _.props).where(_.id eqs someId).one()
  }

"Insert" queries

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Method name Description
value A type safe Insert query builder. Throws an error for null values.
valueOrNull This will accept a null without throwing an error.
ttl Sets the "Time-To-Live" for the record.

"Update" queries

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Method name Description
where The WHERE clause in CQL
and Chains several clauses, creating a WHERE ... AND query
modify The actual update query builder
onlyIf Addition update condition. Used on non-primary columns

Example:

ExampleRecord.update
  .where(_.id eqs myUuid)
  .modify(_.name setTo "Barack Obama")
  .and(_.props put ("title" -> "POTUS"))
  .future()

"Delete" queries

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Method name Description
where The WHERE clause in CQL
and Chains several clauses, creating a WHERE ... AND query

Delete queries are very simple ways to either delete a row or alternatively set a column to null. For instance:

BasicTable.update.where(_.id eqs someId).modify(_.someSet setTo Set.empty[String])

// is actually equivalent to

BasicTable.delete(_.someSet).where(_.id eqs someId)

Query API

Phantom offers a dual query API based on Scala concurrency primitives, which makes it trivial to use phantom in most known frameworks, such as Play!, Spray, Akka, Scruffy, Lift, and many others. Integration is trivial and easily achievable, all you have to do is to use the Scala API methods and you get out of the box integration.

Phantom also offers another API based on Twitter proprietary concurrency primitives. This is due to the fact that internally we rely very heavily on the Twitter eco-system. It's why phantom also offers Finagle-Thrift support out of the box and integrates with Twitter Scrooge. It fits in perfectly with applications powered by Finagle RPC, Zipkin, Thrift, Ostrich, Aurora, Mesos, and the rest of the Twitter lot.

Method name Description Scala result type
future Executes a command and returns a ResultSet. This is useful when you don't need to return a value. scala.concurrent.Future[ResultSet]
execute Executes a command and returns a ResultSet. This is useful when you don't need to return a value. com.twitter.util.Future[ResultSet]
one Executes a command and returns an Option[T]. Use this when you are selecting and you only need one value. Adds LIMIT 1 to the CQL query. scala.concurrent.Future[Option[Record]]
get Executes a command and returns an Option[T]. Use this when you are selecting and you only need one value. AddsLIMIT 1 to the CQL query. com.twitter.util.Future[Option[Record]]
fetch Returns a sequence of matches. Use when you expect more than 1 match. scala.concurrent.Future[Seq[Record]]
collect Returns a sequence of matches. Use when you expect more than 1 match. com.twitter.util.Future[Seq[Record]
fetchSpool This is useful when you need the underlying ResultSpool. com.twitter.concurrent.Spool[T]]
fetchEnumerator This is useful when you need the underlying Play based enumerator. play.api.libs.iteratee.Enumerator[T]

Scala Futures

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Phantom offers a dual asynchronous Future API for the completion of tasks, scala.concurrent.Future and com.twitter.util.Future. However, the concurrency primitives are all based on Google Guava executors and listening decorators. The future API is just for the convenience of users. The Scala Future methods are:

ExampleRecord.select.one() // When you only want to select one record
ExampleRecord.update.where(_.name eqs name).modify(_.name setTo "someOtherName").future() // When you don't care about the return type.
ExampleRecord.select.fetchEnumerator // when you need an Enumerator
ExampleRecord.select.fetch // When you want to fetch a Seq[Record]

Examples with Scala Futures

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import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.Future

object ExampleRecord extends ExampleRecord {
  override val tableName = "examplerecord"

  // now define a session, a normal Datastax cluster connection
  implicit val session = SomeCassandraClient.session;

  def getRecordsByName(name: String): Future[Seq[ExampleModel]] = {
    ExampleRecord.select.where(_.name eqs name).fetch
  }

  def getOneRecordByName(name: String, someId: UUID): Future[Option[ExampleModel]] = {
    ExampleRecord.select.where(_.name eqs name).and(_.id eqs someId).one()
  }
}

Twitter Futures

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Phantom doesn't depend on Finagle for this, we are simply using "com.twitter" %% "util-core" % Version" to return a com.twitter.util.Future. However, the concurrency primitives are all based on Google Guava executors and listening decorators. The future API is just for the convenience of users.

ExampleRecord.select.get() // When you only want to select one record
ExampleRecord.update.where(_.name eqs name).modify(_.name setTo "someOtherName").execute() // When you don't care about the return type.
ExampleRecord.select.enumerate // when you need an Enumerator
ExampleRecord.select.collect // When you want to fetch a Seq[Record]

More examples with Twitter Futures

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import com.twitter.util.Future

object ExampleRecord extends ExampleRecord {
  override val tableName = "examplerecord"

  // now define a session, a normal Datastax cluster connection
  implicit val session = SomeCassandraClient.session;

  def getRecordsByName(name: String): Future[Seq[ExampleModel]] = {
    ExampleRecord.select.where(_.name eqs name).collect
  }

  def getOneRecordByName(name: String, someId: UUID): Future[Option[ExampleModel]] = {
    ExampleRecord.select.where(_.name eqs name).and(_.id eqs someId).get()
  }
}

Collections and operators

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Based on the above list of columns, phantom supports CQL 3 modify operations for CQL 3 collections: list, set, map. All operators will be available in an update query, specifically:

ExampleRecord.update.where(_.id eqs someId).modify(_.someList $OPERATOR $args).future().

List operators

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Examples in ListOperatorsTest.scala.

Name Description
prepend Adds an item to the head of the list
prependAll Adds multiple items to the head of the list
append Adds an item to the tail of the list
appendAll Adds multiple items to the tail of the list
discard Removes the given item from the list.
discardAll Removes all given items from the list.
setIdIx Updates a specific index in the list

Set operators

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Sets have a better performance than lists, as the Cassandra documentation suggests. Examples in SetOperationsTest.scala.

Name Description
add Adds an item to the tail of the set
addAll Adds multiple items to the tail of the set
remove Removes the given item from the set.
removeAll Removes all given items from the set.

Map operators

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Both the key and value types of a Map must be Cassandra primitives. Examples in MapOperationsTest.scala:

Name Description
put Adds an (key -> value) pair to the map
putAll Adds multiple (key -> value) pairs to the map

Automated schema generation

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Replication strategies and more advanced features are not yet available in phantom, but CQL 3 Table schemas are automatically generated from the Scala code. To create a schema in Cassandra from a table definition:

import scala.concurrent.Await
import scala.concurrent.duration._

Await.result(ExampleRecord.create().future(), 5000 millis)

Of course, you don't have to block unless you want to.

Partition tokens

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import scala.concurrent.Await
import scala.concurrent.duration._
import com.websudos.phantom.dsl._

sealed class ExampleRecord2 extends CassandraTable[ExampleRecord2, ExampleModel] {

  object id extends UUIDColumn(this) with PartitionKey[UUID]
  object order_id extends LongColumn(this) with ClusteringOrder[Long] with Descending
  object timestamp extends DateTimeColumn(this)
  object name extends StringColumn(this)
  object props extends MapColumn[ExampleRecord2, ExampleRecord, String, String](this)
  object test extends OptionalIntColumn(this)

  override def fromRow(row: Row): ExampleModel = {
    ExampleModel(id(row), name(row), props(row), timestamp(row), test(row));
  }
}


val orderedResult = Await.result(Articles.select.where(_.id gtToken one.get.id ).fetch, 5000 millis)

PartitionToken operators

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Operator name Description
eqsToken The "equals" operator. Will match if the objects are equal
gtToken The "greater than" operator. Will match a the record is greater than the argument
gteToken The "greater than or equals" operator. Will match a the record is greater than the argument
ltToken The "lower than" operator. Will match a the record that is less than the argument and exists
lteToken The "lower than or equals" operator. Will match a the record that is less than the argument

For more details on how to use Cassandra partition tokens, see SkipRecordsByToken.scala

Cassandra Time Series

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phantom supports Cassandra Time Series. To use them, simply mixin com.websudos.phantom.keys.ClusteringOrder and either Ascending or Descending.

Restrictions are enforced at compile time.

import com.websudos.phantom.dsl._

sealed class ExampleRecord3 extends CassandraTable[ExampleRecord3, ExampleModel] with LongOrderKey[ExampleRecod3, ExampleRecord] {

  object id extends UUIDColumn(this) with PartitionKey[UUID]
  object timestamp extends DateTimeColumn(this) with ClusteringOrder[DateTime] with Ascending
  object name extends StringColumn(this)
  object props extends MapColumn[ExampleRecord2, ExampleRecord, String, String](this)
  object test extends OptionalIntColumn(this)

  override def fromRow(row: Row): ExampleModel = {
    ExampleModel(id(row), name(row), props(row), timestamp(row), test(row));
  }
}

Automatic schema generation can do all the setup for you.

Compound keys

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Phantom also supports using Compound keys out of the box. The schema can once again by auto-generated.

A table can have only one PartitionKey but several PrimaryKey definitions. Phantom will use these keys to build a compound value. Example scenario, with the compound key: (id, timestamp, name)

import com.websudos.phantom.dsl._

sealed class ExampleRecord3 extends CassandraTable[ExampleRecord3, ExampleModel] {

  object id extends UUIDColumn(this) with PartitionKey[UUID]
  object order_id extends LongColumn(this) with ClusteringOrder[Long] with Descending
  object timestamp extends DateTimeColumn(this) with PrimaryKey[DateTime]
  object name extends StringColumn(this) with PrimaryKey[String]
  object props extends MapColumn[ExampleRecord2, ExampleRecord, String, String](this)
  object test extends OptionalIntColumn(this)

  override def fromRow(row: Row): ExampleModel = {
    ExampleModel(id(row), name(row), props(row), timestamp(row), test(row));
  }
}

CQL 3 Secondary Keys

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When you want to use a column in a where clause, you need an index on it. Cassandra data modeling is out of the scope of this writing, but phantom offers com.websudos.phantom.keys.Index to enable querying.

The CQL 3 schema for secondary indexes can also be auto-generated with ExampleRecord4.create().

SELECT is the only query you can perform with an Index column. This is a Cassandra limitation. The relevant tests are found here.

import com.websudos.phantom.dsl._

sealed class ExampleRecord4 extends CassandraTable[ExampleRecord4, ExampleModel] {

  object id extends UUIDColumn(this) with PartitionKey[UUID]
  object order_id extends LongColumn(this) with ClusteringOrder[Long] with Descending
  object timestamp extends DateTimeColumn(this) with Index[DateTime]
  object name extends StringColumn(this) with Index[String]
  object props extends MapColumn[ExampleRecord2, ExampleRecord, String, String](this)
  object test extends OptionalIntColumn(this)

  override def fromRow(row: Row): ExampleModel = {
    ExampleModel(id(row), name(row), props(row), timestamp(row), test(row));
  }
}

Asynchronous iterators for large record sets

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Phantom comes packed with CQL rows asynchronous lazy iterators to help you deal with billions of records. phantom iterators are based on Play iterators with very lightweight integration.

The functionality is identical with respect to asynchronous, lazy behaviour and available methods. For more on this, see this Play tutorial

Usage is trivial. If you want to use slice, take or drop with iterators, the partitioner needs to be ordered.

import scala.concurrent.Await
import scala.concurrent.duration._
import com.websudos.phantom.dsl._


sealed class ExampleRecord3 extends CassandraTable[ExampleRecord3, ExampleModel] {

  object id extends UUIDColumn(this) with PartitionKey[UUID]
  object order_id extends LongColumn(this) with ClusteringOrder[Long] with Descending
  object timestamp extends DateTimeColumn(this) with PrimaryKey[DateTime]
  object name extends StringColumn(this) with PrimaryKey[String]
  object props extends MapColumn[ExampleRecord2, ExampleRecord, String, String](this)
  object test extends OptionalIntColumn(this)

  override def fromRow(row: Row): ExampleModel = {
    ExampleModel(id(row), name(row), props(row), timestamp(row), test(row));
  }
}

object ExampleRecord3 extends ExampleRecord3 {
  def getRecords(start: Int, limit: Int): Future[Set[ExampleModel]] = {
    select.fetchEnumerator.slice(start, limit).collect
  }
}

Batch statements

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phantom also brrings in support for batch statements. To use them, see IterateeBigTest.scala

We have tested with 10,000 statements per batch, and 1000 batches processed simultaneously. Before you run the test, beware that it takes ~40 minutes.

Batches use lazy iterators and daisy chain them to offer thread safe behaviour. They are not memory intensive and you can expect consistent processing speed even with 1 000 000 statements per batch.

Batches are immutable and adding a new record will result in a new Batch, just like most things Scala, so be careful to chain the calls.

phantom also supports COUNTER batch updates and UNLOGGED batch updates.

LOGGED batch statements

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import com.websudos.phantom.dsl._

Batch.logged
    .add(ExampleRecord.update.where(_.id eqs someId).modify(_.name setTo "blabla"))
    .add(ExampleRecord.update.where(_.id eqs someOtherId).modify(_.name setTo "blabla2"))
    .future()

COUNTER batch statements

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import com.websudos.phantom.dsl._

Batch.counter
    .add(ExampleRecord.update.where(_.id eqs someId).modify(_.someCounter increment 500L))
    .add(ExampleRecord.update.where(_.id eqs someOtherId).modify(_.someCounter decrement 300L))
    .future()

UNLOGGED batch statements

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import com.websudos.phantom.dsl._

Batch.unlogged
    .add(ExampleRecord.update.where(_.id eqs someId).modify(_.name setTo "blabla"))
    .add(ExampleRecord.update.where(_.id eqs someOtherId).modify(_.name setTo "blabla2"))
    .future()

Thrift integration

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We use Apache Thrift extensively for our backend services. phantom is very easy to integrate with Thrift models and uses Twitter Scrooge to compile them. Thrift integration is optional and available via "com.websudos" %% "phantom-thrift" % phantomVersion.

namespace java com.websudos.phantom.sample.ExampleModel

stuct ExampleModel {
  1: required i32 id,
  2: required string name,
  3: required Map&lt;string, string&gt; props,
  4: required i32 timestamp
  5: optional i32 test
}

Apache ZooKeeper Integration

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If you have never heard of Apache ZooKeeper before, a much better place to start is here. Phantom offers a complete set of features for ZooKeeper integration using the finagle-zookeeper project.

ZooKeeper Connectors

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Using a set of conventions phantom can automate the entire process of using ZooKeeper in a distributed environment. Phantom will deal with a large series of concerns for you, specifically:

  • Creating a ZooKeeper client and initialising it in due time.
  • Fetching and parsing a sequence of Cassandra ports from ZooKeeper.
  • Creating a Cluster configuration based on the sequence of Cassandra ports available in ZooKeeper.
  • Creating an implicit session for queries to execute.

The entire process described above is entirely automated with a series of sensible defaults available. More details on default implementations are available below. Bottom line, if you want to go custom, you may override at will, if you just want to get something working as fast as possible, then phantom-zookeeper can do everything for you.

The simple Cassandra Connector

This implementation is a very simple way to connect to a running Cassandra node. This is not using ZooKeeper and it's not really indented for multi-node testing or connections, but sometimes you just want to get things working immediately.

The implementation details are available here, but without further ado, this connector will attempt to connector to a local Cassandra, either embedded or not.

Inside Websudos, our port convention is 9042 for local Cassandra and 9142 for embedded. This is reflected in our cassandra.yaml configuration files. Overidding this is quite simple, although you will need to create your own pair of manager and connector.

The DefaultZooKeeperConnector and DefaultZooKeeperManager

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The default implementation expects Cassandra IPs to be listed in a Sequence of host:port combinations, with : as a separator literal. It also expects the default path in ZooKeeper for Cassandra ports to be /cassandra and the sequence of ports should look like this:

host1:port1, host2:port2, host3:port3, host4:port4

Phantom will fetch the data found on the /cassandra path on the ZooKeeper master and attempt to parse all host:port pairs to a Seq[InetSocketAddress] and build a com.datastax.driver.core.Cluster using the sequence of addresses.

Using that Cluster phantom will spawn an implicit session: com.datastax.driver.core.Session. This session is the execution context of all queries inside a table definition. The DefaultZooKeeperManager, found here, will do all the plumbing work for you. More details on the internals are available here.

phantom-testkit

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Naturally, no job is considered truly done with the full power testing automation provided out-of-the box. This is exactly what we tried to achieve with the testing utilities, giving you a very simple, easily extensible, yet highly sensible defaults. We wanted something that works for most things most of the time with 0 integration work on your behalf, yet allowing you to go crazy and custom as you please if the scenario warrants it.

With that design philosophy in mind, we've created two kinds of tests, 1 running with a SimpleCassandraConnector, with the implementation found here, where the testing utilities will auto-spawn an Embedded Cassandra database with the right version and the right settings, run all the tests and cleanup after tests are done.

The other, more complex implementation, targets users who want to use phantom/Cassandra in a distributed environment. This is an easy way to automate multi-DC or multi-cluster tests via service discovery with Apache ZooKeeper. More details are available right above. The BaseTest implementation, which uses a DefaultZooKeeperConnector, is found here, and it follows the pattern described above.

There are 4 core implementations available:

Name Description ZooKeeper support Auto-embedding support
CassandraFlatSpec Simple FlatSpec trait mixin, based on org.scalatest.FlatSpec No Yes
CassandraFeatureSpec Simple FeatureSpec trait mixin, based on org.scalatest.FeatureSpec No Yes
BaseTest ZooKeeper powered FlatSpec trait mixin, based on org.scalatest.FlatSpec Yes Yes
FeatureBestTest ZooKeeper powered FeatureSpec trait mixin, based on org.scalatest.FeatureSpec Yes Yes

Using the built in testing utilities is very simple. In most cases, you use one of the first two base implementations, either CassandraFlatSpec or CassandraFeatureSpec, based on what kind of tests you like writing(flat or feature).

To get started with phantom tests, the usual steps are as follows:

  • Create a global method to initialise all your tables using phantom's auto-generation capability.
  • Create a global method to cleanup and truncate your tables after tests finish executing.
  • Create a root specification file that you plan to use for all your tests.
import scala.concurrent.{ Await, Future }
import scala.concurrent.duration._
import com.websudos.phantom.dsl._


object DatabaseService {
  def init(): Future[List[ResultSet]] = {
    val create = Future.sequence(List(
      Table1.create.future(),
      Table2.create.future()
    ))

    Await.ready(create, 5.seconds)
  }

   def cleanup(): Future[List[ResultSet]] = {
    val truncate = Future.sequence(List(
      Table1.truncate.future(),
      Table2.truncate.future()
    ))
    Await.ready(truncate, 5.seconds)
  }
}

import com.websudos.phantom.testkit._

trait CustomSpec extends CassandraFlatSpec {

   override def beforeAll(): Unit = {
     super.beforeAll()
     DatabaseService.init()
   }

   override def afterAll(): Unit = {
     super.afterAll()
     DatabaseService.cleanup()
   }
}

Running your database tests with phantom is now trivial. A great idea is to use asynchronous testing patterns and future sequencers to get the best possible performance even out of your tests. Now all your other test suites that need a running database would look like this:

import com.websudos.phantom.dsl._
import com.websudos.util.testing._

class UserDatabaseServiceTest extends CustomSpec {
  it should "register a user from a model" in {
    val user = //.. create a user

    // A for-yield will get de-sugared to a flatMap chain, but in effect you get a sequence that says:
    // First write, then fetch by id. The beauty of it is the first future will only complete when the user has been written
    // So you have an async sequence guarantee that the "getById" will be done only after the user is actually available.
    val chain = for {
      store <- UserDatabaseService.register(user)
      get <- UserDatabaseService.getById(user.id)
    } yield get

    // The "successful" method comes from com.websudos.util.testing._ in our util project.
    chain.successful {
      result => {

        // result is now Option[User]

        result.isDefined shouldEqual true
        result.get shouldEqual user
      }
    }
  }
}

If you are using ZooKeeper and you want to run tests through a full ZooKeeper powered cycle, where Cassandra settings are retrieved from a ZooKeeper that can either be running locally or auto-spawned if none is found, pick one of the last two base suites.

Auto-embedded Cassandra

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Phantom spares you of the trouble to spawn your own Cassandra server during tests. The implementation of this is based on the cassandra-unit project. Phantom will automatically pick the right version of Cassandra, however do be careful. We often tend to use the latest version as we do our best to keep up with the latest features.

You may use a brand new phantom feature, see the tests passing with flying colours locally and then get bad errors in production. The version of Cassandra covered by the latest phantom release and used for embedding is written at the very top of this readme.

Running the tests locally

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phantom uses the phantom-testkit module to run tests without a local Cassandra server running. There are no pre-requisites for running the tests. Phantom will automatically load an Embedded Cassandra with the right version, run all the tests and do the cleanup afterwards. Read more on the testing utilities to see how you can achieve the same thing in your own database tests.

If a local Cassandra installation is found running on localhost:9042, phantom will attempt to use that instead. Some of the version based logic is found directly inside phantom, although advanced compatibility and protocol version detection has been a task we left to our dear partners at Datastax as we've felt re-implementing that concern in Scala would bring no significant value add.

Phantom uses multiple SBT configurations to distinguish between two kinds of tests, normal and performance tests. Performance tests are not run during Travis CI runs and we usually run them manually when serious changes are made to the underlying Twitter Spool and Play Iterator based iterators, events that are very rare indeed.

  • Use sbt test to run the normal test suite which should finish pretty quickly, within 2 minutes.
  • Use sbt perf:test if you have a lot of time on your hands and you are debugging performance issues with the framework. This will take 40 -50 minutes.

Contributors

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Phantom was developed at websudos as an in-house project. All Cassandra integration at Websudos goes through phantom, and nowadays it's safe to say most Scala/Cassandra users in the world rely on phantom.

Copyright

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Special thanks to Viktor Taranenko from WhiskLabs, who gave us the original idea.

Copyright 2013 - 2015 websudos.

Contributing to phantom

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Contributions are most welcome! Use GitHub for issues and pull requests and we will happily help out in any way we can!

Using GitFlow

To contribute, simply submit a "Pull request" via GitHub.

We use GitFlow as a branching model and SemVer for versioning.

  • When you submit a "Pull request" we require all changes to be squashed.
  • We never merge more than one commit at a time. All the n commits on your feature branch must be squashed.
  • We won't look at the pull request until Travis CI says the tests pass, make sure tests go well.

Scala Style Guidelines

In spirit, we follow the Twitter Scala Style Guidelines. We will reject your pull request if it doesn't meet code standards, but we'll happily give you a hand to get it right.

Some of the things that will make us seriously frown:

  • Blocking when you don't have to. It just makes our eyes hurt when we see useless blocking.
  • Testing should be thread safe and fully async, use ParallelTestExecution if you want to show off.
  • Writing tests should use the pre-existing tools, they bring in EmbeddedCassandra, Zookeeper and other niceties, allowing us to run multi-datacenter tests.
  • Use the common patterns you already see here, we've done a lot of work to make it easy.
  • Don't randomly import stuff. We are very big on alphabetized clean imports.
  • Tests must pass on both the Oracle and OpenJDK JVM implementations. The only sensitive bit is the Scala reflection mechanism used to detect columns.

YourKit Java Profiler

yourkit

We are very grateful to have the open source license support of YourKit, the most advanced Java profiler.

YourKit is the very core of our performance bottleneck testing, and without it phantom would still be a painfully slow tool.

YourKit Java profiler

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