This is lightweight cats-effect and fs2 IO wrapper for latest datastax 4.x driver.
Why 4.x?
4.x was re-written in immutable first design, within async first API, optimizations, fewer allocations, metrics improvements, and fully compatible with cassandra 3.x
- Be safe, type-safe.
- Be fast
- use minimal allocations
- minimize resources and abstractions overhead over original datastax driver, which is good
Cassandra4io is currently available for Scala 2.13 and 2.12.
libraryDependencies += ("com.ringcentral" %% "cassandra4io" % "0.1.14")import com.ringcentral.cassandra4io.CassandraSession
import com.datastax.oss.driver.api.core.CqlSession
import cats.effect._
import java.net.InetSocketAddress
val builder = CqlSession
.builder()
.addContactPoint(InetSocketAddress.createUnresolved("localhost", 9042))
.withLocalDatacenter("datacenter1")
.withKeyspace("awesome")
def makeSession[F[_]: Async]: Resource[F, CassandraSession[F]] =
CassandraSession.connect(builder)package com.ringcentral.cassandra4io.cql introduces typed way to deal with cql queries
import cats.effect.Sync
import cats.syntax.all._
import com.datastax.oss.driver.api.core.ConsistencyLevel
import com.ringcentral.cassandra4io.CassandraSession
import com.ringcentral.cassandra4io.cql._
case class Model(id: Int, data: String)
trait Dao[F[_]] {
def put(value: Model): F[Unit]
def get(id: Int): F[Option[Model]]
}
class DaoImpl[F[_]: Async](session: CassandraSession[F]) extends Dao[F] {
private def insertQuery(value: Model) =
cql"insert into table (id, data) values (${value.id}, ${value.data})"
.config(_.setConsistencyLevel(ConsistencyLevel.ALL))
private def selectQuery(id: Int) =
cql"select id, data from table where id = $id".as[Model]
override def put(value: Model) = insertQuery(value).execute(session).void
override def get(id: Int) = selectQuery(id).select(session).head.compile.last
}this syntax reuse implicit driver prepared statements cache
import cats.effect._
import scala.concurrent.duration._
import cats.syntax.all._
import scala.jdk.DurationConverters._
import com.datastax.oss.driver.api.core.ConsistencyLevel
import com.ringcentral.cassandra4io.CassandraSession
import com.ringcentral.cassandra4io.cql._
case class Model(id: Int, data: String)
trait Dao[F[_]] {
def put(value: Model): F[Unit]
def get(id: Int): F[Option[Model]]
}
object Dao {
private val insertQuery = cqlt"insert into table (id, data) values (${Put[Int]}, ${Put[String]})"
.config(_.setTimeout(1.second.toJava))
private val selectQuery = cqlt"select id, data from table where id = ${Put[Int]}".as[Model]
def apply[F[_]: Async](session: CassandraSession[F]) = for {
insert <- insertQuery.prepare(session)
select <- selectQuery.prepare(session)
} yield new Dao[F] {
override def put(value: Model) = insert(value.id, value.data).execute.void
override def get(id: Int) = select(id).config(_.setExecutionProfileName("default")).select.head.compile.last
}
} By default, cassandra4io encodes Option as a null value. Which is ok for most cases. But in Cassandra, there is a difference between a null value and an empty value. In java driver this difference is represented by BoundStatement#setToNull (default behavior) and BoundStatement#unset (setting an empty field). The main advantage of using unset instead of setToNull is that tombstone will not be created for an empty field.
To use the unset instead of the setToNull for your optional value in a cql interpolators you could add .usingUnset to your optional value. Like in the following example:
import com.ringcentral.cassandra4io.cql._
cql"insert into entities(foo, bar, baz) values (${e.foo}, ${e.bar}, ${e.baz.usingUnset}"Cassandra4IO provides support for Cassandra's User Defined Type (UDT) values. For example, given the following Cassandra schema:
create type basic_info(
weight double,
height text,
datapoints frozen<set<int>>
);
create table person_attributes(
person_id int,
info frozen<basic_info>,
PRIMARY KEY (person_id)
);Note: frozen means immutable
Here is how to insert and select data from the person_attributes table:
final case class BasicInfo(weight: Double, height: String, datapoints: Set[Int])
object BasicInfo {
implicit val cqlReads: Reads[BasicInfo] = FromUdtValue.deriveReads[BasicInfo]
implicit val cqlBinder: Binder[BasicInfo] = ToUdtValue.deriveBinder[BasicInfo]
}
final case class PersonAttribute(personId: Int, info: BasicInfo)We provide a set of typeclasses (FromUdtValue and ToUDtValue) under the hood that automatically convert your Scala
types into types that Cassandra can understand without having to manually convert your data-types into Datastax Java
driver's UdtValues.
class UDTUsageExample[F[_]: Async](session: CassandraSession[F]) {
val data = PersonAttribute(1, BasicInfo(180.0, "tall", Set(1, 2, 3, 4, 5)))
val insert: F[Boolean] =
cql"INSERT INTO cassandra4io.person_attributes (person_id, info) VALUES (${data.personId}, ${data.info})"
.execute(session)
val retrieve: fs2.Stream[F, PersonAttribute] =
cql"SELECT person_id, info FROM cassandra4io.person_attributes WHERE person_id = ${data.personId}"
.as[PersonAttribute]
.select(session)
}If you wanted to have additional control into how you map data-types to and from Cassandra rather than using FromUdtValue
& ToUdtValue, we expose the Datastax Java driver API to you for full control. Here is an example using BasicInfo:
object BasicInfo {
implicit val cqlReads: Reads[BasicInfo] = Reads[UdtValue].map { udtValue =>
BasicInfo(
weight = udtValue.getDouble("weight"),
height = udtValue.getString("height"),
datapoints = udtValue
.getSet[java.lang.Integer]("datapoints", classOf[java.lang.Integer])
.asScala
.toSet
.map { int: java.lang.Integer => Int.unbox(int) }
)
}
implicit val cqlBinder: Binder[BasicInfo] = Binder[UdtValue].contramapUDT { (info, constructor) =>
constructor
.newValue()
.setDouble("weight", info.weight)
.setString("height", info.height)
.setSet("datapoints", info.datapoints.map(Int.box).asJava, classOf[java.lang.Integer])
}
}Please note that we recommend using FromUdtValue and ToUdtValue to automatically derive this hand-written (and error-prone)
code.
Cassandra4IO Allows you to interpolate (i.e. using string interpolation) on values that are not valid CQL parameters using
++ or concat to build out your CQL query. For example, you can interpolate on the keyspace and table name using
the cqlConst interpolator like so:
val session: CassandraSession[IO] = ???
val keyspaceName = "cassandra4io"
val tableName = "person_attributes"
val keyspace = cqlConst"$keyspaceName."
val table = cqlConst"$tableName"
def insert(data: PersonAttribute) =
(cql"INSERT INTO " ++ keyspace ++ table ++ cql" (person_id, info) VALUES (${data.personId}, ${data.info})")
.execute(session)This allows you (the author of the application) to feed in parameters like the table name and keyspace through
configuration. Please be aware that you should not be taking your user's input and feeding this into cqlConst as
this will pose an injection risk.
Cassandra4io is released under the Apache License 2.0.
