phantom
============== 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.
The latest major release is: val phantomVersion = 1.2.2.
Phantom is published to Maven Central and it's actively and avidly developed.
Intermediary releases are available through our managed Maven repository,"Websudos releases" at "http://maven.websudos.co.uk/ext-release-local".
The latest development version is val phantomVersion = 1.2.7. This version is likely only available on our Maven repository as an intermediary release.
The latest Scala 2.11 release is val phantomVersion = 1.2.7. At this point in time, the only Scala 2.11 compatible modules are:
phantom-dsl.
The Apache Cassandra version used for auto-embedding Cassandra during tests is: val cassandraVersion = "2.1.0-rc5".
- Issues and questions
- Adopters
- Roadmap
- Commercial support
- Using phantom in your project
-
phantom columns
- Data modeling with phantom
-
Basic query examples
- Automated schema generation
-
Cassandra indexing
- Asynchronous iterators
-
Batch statements
- Thrift integration
- Thrift integration
- Copyright
We love Cassandra to bits and use it in every bit our stack. phantom makes it super trivial for Scala users to embrace Cassandra.
Cassandra is highly scalable and it's by far the most powerful database technology available, open source or otherwise.
Phantom is built on top of the Datastax Java Driver, which does most of the heavy lifting.
If you're completely new to Cassandra, a much better place to start is the Datastax Introduction to Cassandra
We are very happy to help implement missing features in phantom, answer questions about phantom, and occasionally help you out with Cassandra questions, although do note we're a bit short staffed!
You can get in touch via the newzly-phantom Google Group or via the below listed emails.
We are also extremely grateful if you add your company to our list of adopters, as it makes it easy for us to further increase adoption, contributions and make phantom better and better.
This is a list of companies that have embraced phantom as part of their technology stack and using it in production environments.
While dates are not fixed, we will use this list to tell you about our plans for the future. If you have great ideas about what could benefit all phantom adopters, please get in touch. We are very happy and eager to listen.
- User defined types
We are working closely around the latest features in the Datastax Java driver and Apache Cassandra 2.1 to offer a fully type safe DSL for user defined types. This feature is well in progress and you can expect to see it live roughly at the same time as the release of the Datastax 2.1 driver, planned for July 2014.
Some of the cool features include automatic schema generation, fully type safe referencing of fields and inner members of UDTs and fully type safe querying.
- Zookeeper support(available as of 1.1.0).
Since Cassandra cannot be loadbalanced effectively and Zookeeper is to date the de-facto standard for distributed synchronisation and service discovery,
we figured a pre-build integration based on finagle-zookeeper would be useful.
We've even taken it one step further, writing some pretty cool tools for testing automations. With a simple trait you can run asynchronous tests against an embedded Cassandra instance and an embedded Zookeeper instance. This process is completely transparent and you don't really need to do anything. No config or starting tools is necessary, everything will start and stop automatically, configure itself automatically and run tests in parallel using async assertions, all automatic and with our tools.
We are also testing it in production in a massive enterprise to make sure it's reliable with a few dozen nodes in a cluster, not just the local embedded flavour.
- A new QueryBuilder
At present times, phantom is relying on the underlying Datastax Java Driver to serialise queries to CQL and while the querybuilder implementation is excellent for Java standards, it doesn't add any Scala features, since it was a Java only product. With a Scala based builder we plan to add more advanced behaviour, such as immutable builders and phantom types.
- Spark integration
Thanks to the recent partnership between Databricks and Datastax, Spark is getting a Cassandra facelift with a Datastax backed integration. We won't be slow to follow up with a type safe Scala variant of that integration, so you can enjoy the benefits of high power computation with Cassandra as a backup storage through the simple high power DSL we've gotten you used to.
You can expect to see the spark integration live in a new phantom-spark module in the 1.3.0 or 1.4.0 version, planned sometime in September 2014.
We, the people behind phantom run a software development house specialised in Scala, NoSQL and distributed systems. If you are after enterprise grade training or support for using phantom, Websudos is here to help!
We offer a comprehensive range of services, including but not limited to:
- Remote contractors for hire
- Training
- In house software development
- Team building
- Architecture planning
- Startup product development
We are big fans of open source and we will open source every project we can! To read more about our OSS efforts, click here.
For most things, all you need is phantom-dsl. Read through for information on other modules.
libraryDependencies ++= Seq(
"com.websudos" %% "phantom-dsl" % phantomVersion
)The full list of available modules is:
libraryDependencies ++= Seq(
"com.websudos" %% "phantom-dsl" % phantomVersion,
"com.websudos" %% "phantom-example" % phantomVersion,
"com.websudos" %% "phantom-spark" % phantomVersion,
"com.websudos" %% "phantom-thrift" % phantomVersion,
"com.websudos" %% "phantom-test" % phantomVersion,
"com.websudos" %% "phantom-testing" % phantomVersion,
"com.websudos" %% "phantom-udt" % phantomVersion,
"com.websudos" %% "phantom-zookeeper" % phantomVersion
)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 |
| 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 |
| CounterColumn | scala.Long | counter |
| StaticColumn<type> | <type> | type static |
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 |
| 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 |
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> |
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.
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).
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.
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.
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.
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 com.websudos.phantom.thrift.Implicits._ to get them.
In the below scenario, the C* 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> |
import java.util.{ Date, UUID }
import com.datastax.driver.core.Row
import com.websudos.phantom.sample.ExampleModel
import com.websudos.phantom.Implicits._
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 with Ascending
object name extends StringColumn(this)
object props extends MapColumn[ExampleRecord, ExampleModel, 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));
}
}
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.
| 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. |
useConsistencyLevel |
Sets the consistency level to use. |
setFetchSize |
Sets the maximum number of records to retrieve. Default is 10000 |
limit |
Sets the exact number of records to retrieve. |
Select queries are very straightforward and enforce most limitations at compile time.
| 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 |
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()
}| 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. |
useConsistencyLevel |
Sets the consistency level to use. |
ttl |
Sets the "Time-To-Live" for the record. |
| Method name | Description |
|---|---|
where |
The WHERE clause in CQL |
and |
Chains several clauses, creating a WHERE ... AND query |
modify |
The actual update query builder |
useConsistencyLevel |
Sets the consistency level to use. |
onflyIf |
Addition update condition. Used on non-primary columns |
| Method name | Description |
|---|---|
where |
The WHERE clause in CQL |
useConsistencyLevel |
Sets the consistency level to use. |
The full list can be found in [CQLQuery.scala](https://github.com/websudos/phantom/blob/develop/phantom-dsl/src/main/scala/com/websudos/phantom/query/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. |
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]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()
}
}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]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()
}
}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().
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 |
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. |
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 |
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.
import scala.concurrent.Await
import scala.concurrent.duration._
import com.websudos.phantom.Implicits._
sealed class ExampleRecord2 extends CassandraTable[ExampleRecord2, ExampleModel] with LongOrderKey[ExampleRecod2, ExampleRecord] {
object id extends UUIDColumn(this) with PartitionKey[UUID]
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)
| 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
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.Implicits._
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 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.
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 org.joda.time.DateTime
import com.websudos.phantom.Implicits._
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 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));
}
}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 com.websudos.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 java.util.UUID
import org.joda.time.DateTime
import com.websudos.phantom.Implicits._
sealed class ExampleRecord4 extends CassandraTable[ExampleRecord4, ExampleModel] with LongOrderKey[ExampleRecod4, ExampleRecord] {
object id extends UUIDColumn(this) with PartitionKey[UUID]
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));
}
}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 org.joda.time.DateTime
import com.websudos.phantom.Implicits._
sealed class ExampleRecord3 extends CassandraTable[ExampleRecord3, ExampleModel] with LongOrderKey[ExampleRecord3, ExampleRecord] {
object id extends UUIDColumn(this) with PartitionKey[UUID]
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
}
}
phantom also brrings in support for batch statements. To use them, see [IterateeBigTest.scala]( https://github .com/websudos/phantom/blob/develop/phantom-test/src/test/scala/com/websudos/phantom/iteratee/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.
import com.websudos.phantom.Implicits._
BatchStatement()
.add(ExampleRecord.update.where(_.id eqs someId).modify(_.name setTo "blabla"))
.add(ExampleRecord.update.where(_.id eqs someOtherId).modify(_.name setTo "blabla2"))
.future()
import com.websudos.phantom.Implicits._
CounterBatchStatement()
.add(ExampleRecord.update.where(_.id eqs someId).modify(_.someCounter increment 500L))
.add(ExampleRecord.update.where(_.id eqs someOtherId).modify(_.someCounter decrement 300L))
.future()import com.websudos.phantom.Implicits._
UnloggedBatchStatement()
.add(ExampleRecord.update.where(_.id eqs someId).modify(_.name setTo "blabla"))
.add(ExampleRecord.update.where(_.id eqs someOtherId).modify(_.name setTo "blabla2"))
.future()
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<string, string> props,
4: required i32 timestamp
5: optional i32 test
}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.
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 initilising 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.
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](https://github .com/websudos/phantom/blob/develop/phantom-zookeeper/src/main/scala/com/websudos/phantom/zookeeper/SimpleCassandraConnector.scala), 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 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.
For testing automation purposes, phantom-zookeeper contains a simple implementation of a ZooKeeper node. The implementation is available [here]
(https://github.com/websudos/phantom/blob/develop/phantom-zookeeper/src/main/scala/com/websudos/phantom/zookeeper/ZookeeperInstance.scala),
and it's used mainly for testing purposes. If you are using ZooKeeper in a production environment and you are using the phantom-zookeeper module to
automate your Cassandra connections, the phantom testing utilities will automatically spawn a ZooKeeperInstance if no local ZooKeeper server is found
running on the default localhost:2181 address.
The ZooKeeper Instance will pick a free port by itself, spawn a ZooKeeper instance, create the /cassandra path,
add localhost:9142 to it, and propagate the host:port combination through an environment variable. The testing utilities will then read an
environment variable, spawn ZooKeeper Client based on finagle-zookeeper, spawn an EmbeddedCassandra server if none is found running, fetch the settings from ZooKeeper and create
all the plumbing you need to run the tests. You get all that for free by mixing in a single trait,
just like we do here.
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).
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.
Phantom spares you of the trouble to spawn your own Cassandra server during tests. The implementation of this is based on the [cassandra-unit] (https://github.com/jsevellec/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.
phantom uses the phantom-testing 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.
Phantom was developed at websudos as an in-house project. All Cassandra integration at websudos goes through phantom.
- Flavian Alexandru flavian@newzly.com(maintainer)
- Tomasz Perek tomasz.perek@newzly.com
- Viktor Taranenko (viktortnk)
- Bartosz Jankiewicz (@bjankie1)
- Eugene Zhulenev (@ezhulenev)
Special thanks to Viktor Taranenko from WhiskLabs, who gave us the original idea.
Copyright 2013 - 2014 websudos.
Contributions are most welcome!
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.
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
ParallelTestExecutionif 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.