现代信息系统应该是避不开大数据处理的。作为一个通用的系统集成工具也必须具备大数据存储和读取能力。cassandra是一种分布式的数据库，具备了分布式数据库高可用性（high-availability）特性，对于一个实时大型分布式集成系统来说是核心支柱。与传统的关系数据库对比，cassandra从数据存储结构、读取方式等可以说是皆然不同的。如：cassandra库表设计是反范式的（denormalized）、表结构设计是反过来根据query要求设计的，等等。幸运的是自版本3.0后cassandra提供了CQL来支持数据库操作。简单来说CQL就是cassandra的SQL。CQL是一种query语言，在语法上与SQL相近。最重要的是CQL用SQL的呈现方式来描述cassandra底层数据的存储方式，让熟悉了关系数据库SQL编程人员能够容易开始使用cassandra。与SQL一样，CQL也是一种纯文本语言，可以通过多种终端接口软件包括java-client来运行CQL脚本。 目前在市面上有一些现成的cassandra客户端编程软件，有些为了实现类型安全（type-safety）还提供了Linq-DSL（language-integrated-query），但因为我们需要面向各种cassandra数据库用户，所以还是决定提供一种CQL脚本运算环境，也就是说Cassandra-Engine接受CQL脚本然后运算得出结果。
和JDBC的运算结构很相似：CQL运算也是先构建statement然后execute。与JDBC不同的是：CQL还提供non-blocking脚本运算：
/**
* Executes the provided query asynchronously.
* <p/>
* This method does not block. It returns as soon as the query has been
* passed to the underlying network stack. In particular, returning from
* this method does not guarantee that the query is valid or has even been
* submitted to a live node. Any exception pertaining to the failure of the
* query will be thrown when accessing the [email protected] ResultSetFuture}.
* <p/>
* Note that for queries that don't return a result (INSERT, UPDATE and
* DELETE), you will need to access the ResultSetFuture (that is, call one of
* its [email protected] get} methods to make sure the query was successful.
*
* @param statement the CQL query to execute (that can be any [email protected] Statement}).
* @return a future on the result of the query.
* @throws UnsupportedFeatureException if the protocol version 1 is in use and
* a feature not supported has been used. Features that are not supported by
* the version protocol 1 include: BatchStatement, ResultSet paging and binary
* values in RegularStatement.
*/
ResultSetFuture executeAsync(Statement statement);
executeAsync返回结果ResultSsetFuture是个google-guava-future。我们可以用隐式转换（implicit conversion）把它转换成scala-future来使用：
implicit def listenableFutureToFuture[T](
listenableFuture: ListenableFuture[T]): Future[T] = {
val promise = Promise[T]()
Futures.addCallback(listenableFuture, new FutureCallback[T] {
def onFailure(error: Throwable): Unit = {
promise.failure(error)
()
}
def onSuccess(result: T): Unit = {
promise.success(result)
()
}
})
promise.future
}
有了这个隐式实例executeAsync返回结果自动转成Future[?]，如下：
def cqlSingleUpdate(ctx: CQLContext)(
implicit session: Session, ec: ExecutionContext): Future[Boolean] = {
...
session.executeAsync(boundStmt).map(_.wasApplied())
}
我们还是通过某种Context方式来构建完整可执行的statement：
case class CQLContext(
statements: Seq[String],
parameters: Seq[Seq[Object]] = Nil,
consistency: Option[CQLContext.CONSISTENCY_LEVEL] = None
) { ctx =>
def setConsistencyLevel(_consistency: CQLContext.CONSISTENCY_LEVEL): CQLContext =
ctx.copy(consistency = Some(_consistency))
def setCommand(_statement: String, _parameters: Object*): CQLContext =
ctx.copy(statements = Seq(_statement), parameters = Seq(_parameters))
def appendCommand(_statement: String, _parameters: Object*): CQLContext =
ctx.copy(statements = ctx.statements :+ _statement,
parameters = ctx.parameters ++ Seq(_parameters))
}
与JDBCContext不同的是这个consistencyLevel。因为数据是重复分布在多个集群节点上的，所以需要通过consistencyLevel来注明分布式数据的读写方式：
def consistencyLevel: CONSISTENCY_LEVEL => ConsistencyLevel = consistency => {
consistency match {
case ALL => ConsistencyLevel.ALL
case ONE => ConsistencyLevel.ONE
case TWO => ConsistencyLevel.TWO
case THREE => ConsistencyLevel.THREE
case ANY => ConsistencyLevel.ANY
case EACH_QUORUM => ConsistencyLevel.EACH_QUORUM
case LOCAL_ONE => ConsistencyLevel.LOCAL_ONE
case QUORUM => ConsistencyLevel.QUORUM
case SERIAL => ConsistencyLevel.SERIAL
case LOCAL_SERIAL => ConsistencyLevel.LOCAL_SERIAL
}
}
CQL statement 分simplestatement, preparedstatement和boundstatement。boundstatement可以覆盖所有类型的CQL statement构建要求。下面是一个构建boundstatement的例子：
val prepStmt = session.prepare(ctx.statement)
var boundStmt = prepStmt.bind()
if (ctx.parameter != Nil) {
val params = processParameters(ctx.parameter)
boundStmt = prepStmt.bind(params:_*)
}
CQL statement参数类型比较复杂，包括date,timestamp等都必须经过processParameters函数进行预处理：
case class CQLDate(year: Int, month: Int, day: Int)
case object CQLTodayDate
case class CQLDateTime(year: Int, Month: Int,
day: Int, hour: Int, minute: Int, second: Int, millisec: Int = 0)
case object CQLDateTimeNow
def processParameters(params: Seq[Object]): Seq[Object] = {
params.map { obj =>
obj match {
case CQLDate(yy, mm, dd) => LocalDate.fromYearMonthDay(yy, mm, dd)
case CQLTodayDate =>
val today = java.time.LocalDate.now()
LocalDate.fromYearMonthDay(today.getYear, today.getMonth.getValue, today.getDayOfMonth)
case CQLDateTimeNow => Instant.now()
case CQLDateTime(yy, mm, dd, hr, ms, sc, mi) =>
Instant.parse(f"$yy%4d-$mm%2d-$dd%2dT$hr%2d:$ms%2d:$sc%2d$mi%3d")
case p@_ => p
}
}
}
CassandraEngine更新运算分为单条update和批次update。批次update与事物处理有异曲同工之效：批次中任何一条脚本运算失败则回滚所有更新：
def cqlExecute(ctx: CQLContext)(
implicit session: Session, ec: ExecutionContext): Future[Boolean] = {
if (ctx.statements.size == 1)
cqlSingleUpdate(ctx)
else
cqlMultiUpdate(ctx)
}
def cqlSingleUpdate(ctx: CQLContext)(
implicit session: Session, ec: ExecutionContext): Future[Boolean] = {
val prepStmt = session.prepare(ctx.statements.head)
var boundStmt = prepStmt.bind()
if (ctx.statements != Nil) {
val params = processParameters(ctx.parameters.head)
boundStmt = prepStmt.bind(params:_*)
}
ctx.consistency.foreach {consistency =>
boundStmt.setConsistencyLevel(consistencyLevel(consistency))}
session.executeAsync(boundStmt).map(_.wasApplied())
}
def cqlMultiUpdate(ctx: CQLContext)(
implicit session: Session, ec: ExecutionContext): Future[Boolean] = {
val commands: Seq[(String,Seq[Object])] = ctx.statements zip ctx.parameters
var batch = new BatchStatement()
commands.foreach { case (stm, params) =>
val prepStmt = session.prepare(stm)
if (params == Nil)
batch.add(prepStmt.bind())
else {
val p = processParameters(params)
batch.add(prepStmt.bind(p: _*))
}
}
ctx.consistency.foreach {consistency =>
batch.setConsistencyLevel(consistencyLevel(consistency))}
session.executeAsync(batch).map(_.wasApplied())
}
CassandraEngine update返回运算状态Future[Boolean]。下面是一段update示范：
val createCQL ="""
CREATE TABLE testdb.members (
id UUID primary key,
name TEXT,
description TEXT,
birthday DATE,
created_at TIMESTAMP,
picture BLOB
)"""
val ctxCreate = CQLContext().setCommand(createCQL)
val ctxInsert = CQLContext().setCommand("insert into testdb.members(id,name,description,birthday,created_at,picture)" +
" values(uuid(),?,?,?,?,?)", "alan xu", "alan-xu", CQLDate(1966, 11, 27), CQLDateTimeNow, cqlFileToBytes("/users/tiger/Nobody.png"))
val createData = for {
createTable <- cqlExecute(ctxCreate)
insertData <- cqlExecute(ctxInsert)
} yield(createTable, insertData)
createData.onComplete {
case Success((c,i)) => println(s"Create Table: $c, Insert Data $i")
case Failure(e) => println(e.getMessage)
}
在上面的例子里我们用for-comprehension实现了连续运算。注意在这个例子里已经包括了date,datetime,blob等输入参数类型。
fetch-query的statement构建信息如下：
case class CQLQueryContext[M](
statement: String,
parameter: Seq[Object] = Nil,
consistency: Option[CQLContext.CONSISTENCY_LEVEL] = None,
extractor: Row => M
)
fetch-query运算也是用execute方式实现的：
/**
* Executes the provided query.
* <p/>
* This method blocks until at least some result has been received from the
* database. However, for SELECT queries, it does not guarantee that the
* result has been received in full. But it does guarantee that some
* response has been received from the database, and in particular
* guarantees that if the request is invalid, an exception will be thrown
* by this method.
*
* @param statement the CQL query to execute (that can be any [email protected] Statement}).
* @return the result of the query. That result will never be null but can
* be empty (and will be for any non SELECT query).
* @throws NoHostAvailableException if no host in the cluster can be
* contacted successfully to execute this query.
* @throws QueryExecutionException if the query triggered an execution
* exception, i.e. an exception thrown by Cassandra when it cannot execute
* the query with the requested consistency level successfully.
* @throws QueryValidationException if the query if invalid (syntax error,
* unauthorized or any other validation problem).
* @throws UnsupportedFeatureException if the protocol version 1 is in use and
* a feature not supported has been used. Features that are not supported by
* the version protocol 1 include: BatchStatement, ResultSet paging and binary
* values in RegularStatement.
*/
ResultSet execute(Statement statement);
返回结果ResultSet经过转换后成为scala collection：
def fetchResultPage[C[_] <: TraversableOnce[_],A](ctx: CQLQueryContext[A], pageSize: Int = 100)(
implicit session: Session, cbf: CanBuildFrom[Nothing, A, C[A]]): (ResultSet, C[A])= {
val prepStmt = session.prepare(ctx.statement)
var boundStmt = prepStmt.bind()
if (ctx.parameter != Nil) {
val params = processParameters(ctx.parameter)
boundStmt = prepStmt.bind(params:_*)
}
ctx.consistency.foreach {consistency =>
boundStmt.setConsistencyLevel(consistencyLevel(consistency))}
val resultSet = session.execute(boundStmt.setFetchSize(pageSize))
(resultSet,(resultSet.asScala.view.map(ctx.extractor)).to[C])
}
fetchResultPage是分页读取的，可以用fetchMoreResults持续读取：
/**
* Force fetching the next page of results for this result set, if any.
* <p/>
* This method is entirely optional. It will be called automatically while
* the result set is consumed (through [email protected] #one}, [email protected] #all} or iteration)
* when needed (i.e. when [email protected] getAvailableWithoutFetching() == 0} and
* [email protected] isFullyFetched() == false}).
* <p/>
* You can however call this method manually to force the fetching of the
* next page of results. This can allow to prefetch results before they are
* strictly needed. For instance, if you want to prefetch the next page of
* results as soon as there is less than 100 rows readily available in this
* result set, you can do:
*
* ResultSet rs = session.execute(...);
* Iterator<Row> iter = rs.iterator();
* while (iter.hasNext()) {
* if (rs.getAvailableWithoutFetching() == 100 && !rs.isFullyFetched())
* rs.fetchMoreResults();
* Row row = iter.next()
* ... process the row ...
* }
*
* This method is not blocking, so in the example above, the call to [email protected]
* fetchMoreResults} will not block the processing of the 100 currently available
* rows (but [email protected] iter.hasNext()} will block once those rows have been processed
* until the fetch query returns, if it hasn't yet).
* <p/>
* Only one page of results (for a given result set) can be
* fetched at any given time. If this method is called twice and the query
* triggered by the first call has not returned yet when the second one is
* performed, then the 2nd call will simply return a future on the currently
* in progress query.
*
* @return a future on the completion of fetching the next page of results.
* If the result set is already fully retrieved ([email protected] isFullyFetched() == true}),
* then the returned future will return immediately but not particular error will be
* thrown (you should thus call [email protected] #isFullyFetched()} to know if calling this
* method can be of any use}).
*/
ListenableFuture<S> fetchMoreResults();下面是分页持续读取的实现：
def fetchMorePages[C[_] <: TraversableOnce[_],A](resultSet: ResultSet, timeOut: Duration)(
extractor: Row => A)(implicit cbf: CanBuildFrom[Nothing, A, C[A]]): (ResultSet,Option[C[A]]) =
if (resultSet.isFullyFetched) {
(resultSet, None)
} else {
try {
val result = Await.result(resultSet.fetchMoreResults(), timeOut)
(result, Some((result.asScala.view.map(extractor)).to[C]))
} catch { case e: Throwable => (resultSet, None) }
}
我们用这两个函数来读取上面用cqlInsert脚本加入cassandra的数据：
//data model
case class Member(
id: String,
name: String,
description: Option[String] = None,
birthday: LocalDate,
createdAt: java.util.Date,
picture: Option[ByteBuffer] = None)
//data row converter
val toMember = (rs: Row) => Member(
id = rs.getUUID("id").toString,
name = rs.getString("name"),
description = {
val d = rs.getString("description")
if (d == null)
None
else
Some(d)
Some(rs.getColumnDefinitions.toString)
},
birthday = rs.getDate("birthday"),
createdAt = rs.getTimestamp("created_at"),
picture = {
val pic = rs.getBytes("picture")
if (pic == null)
None
else
Some(pic)
}
)
try {
val qtx = CQLQueryContext(statement = "select * from testdb.members", extractor = toMember)
val (resultSet, vecResults) = fetchResultPage[Vector, Member](qtx)
var vecMembers: Vector[Member] = vecResults
var isExh = resultSet.isExhausted
var nextPage: (ResultSet, Option[Vector[Member]]) = (resultSet, Some(vecResults))
while (!isExh) {
nextPage = fetchMorePages[Vector,Member](nextPage._1,1 second)(toMember)
nextPage._2.foreach {vec =>
vecMembers = vecMembers ++ vec
}
isExh = resultSet.isExhausted
}
vecMembers.foreach { m =>
println(s"id: ${m.id}-name:${m.name}-${m.description} birthday: ${m.birthday.toString}")
println(s"created_at: ${cqlDateTimeString(m.createdAt,"yyyy-MM-dd HH:mm:ss.SSS")}")
m.picture match {
case Some(buf) =>
val fname = s"/users/tiger/pic-${m.name}.png"
cqlBytesToFile(buf,fname)
println(s"saving picture to $fname")
case _ => println("empty picture!")
}
}
} catch {
case e: Exception => println(e.getMessage)
}
在上面的示范里我们还引用了一些helper函数：
def cqlFileToBytes(fileName: String): ByteBuffer = {
val fis = new FileInputStream(fileName)
val b = new Array[Byte](fis.available + 1)
val length = b.length
fis.read(b)
ByteBuffer.wrap(b)
}
def cqlBytesToFile(bytes: ByteBuffer, fileName: String)(
implicit mat: Materializer): Future[IOResult] = {
val source = StreamConverters.fromInputStream(() => ByteBufferInputStream(bytes))
source.runWith(FileIO.toPath(Paths.get(fileName)))
}
def cqlDateTimeString(date: java.util.Date, fmt: String): String = {
val outputFormat = new java.text.SimpleDateFormat(fmt)
outputFormat.format(date)
}
def useJava8DateTime(cluster: Cluster) = {
//for jdk8 datetime format
cluster.getConfiguration().getCodecRegistry()
.register(InstantCodec.instance)
}
还需要一个ByteBufferInputStream类型来实现blob内容的读取：
class ByteBufferInputStream(buf: ByteBuffer) extends InputStream {
override def read: Int = {
if (!buf.hasRemaining) return -1
buf.get
}
override def read(bytes: Array[Byte], off: Int, len: Int): Int = {
val length: Int = Math.min(len, buf.remaining)
buf.get(bytes, off, length)
length
}
}
object ByteBufferInputStream {
def apply(buf: ByteBuffer): ByteBufferInputStream = {
new ByteBufferInputStream(buf)
}
}
下面就是本次讨论示范源代码：
build.sbt
name := "learn_cassandra"
version := "0.1"
scalaVersion := "2.12.4"
libraryDependencies := Seq(
"com.datastax.cassandra" % "cassandra-driver-core" % "3.4.0",
"com.datastax.cassandra" % "cassandra-driver-extras" % "3.4.0",
"com.typesafe.akka" %% "akka-actor" % "2.5.4",
"com.typesafe.akka" %% "akka-stream" % "2.5.4",
"com.lightbend.akka" %% "akka-stream-alpakka-cassandra" % "0.16",
"org.scalikejdbc" %% "scalikejdbc" % "3.2.1",
"org.scalikejdbc" %% "scalikejdbc-test" % "3.2.1" % "test",
"org.scalikejdbc" %% "scalikejdbc-config" % "3.2.1",
"org.scalikejdbc" %% "scalikejdbc-streams" % "3.2.1",
"org.scalikejdbc" %% "scalikejdbc-joda-time" % "3.2.1",
"com.h2database" % "h2" % "1.4.196",
"mysql" % "mysql-connector-java" % "6.0.6",
"org.postgresql" % "postgresql" % "42.2.0",
"commons-dbcp" % "commons-dbcp" % "1.4",
"org.apache.tomcat" % "tomcat-jdbc" % "9.0.2",
"com.zaxxer" % "HikariCP" % "2.7.4",
"com.jolbox" % "bonecp" % "0.8.0.RELEASE",
"com.typesafe.slick" %% "slick" % "3.2.1",
"ch.qos.logback" % "logback-classic" % "1.2.3")
CassandraEngine.scala
import com.datastax.driver.core._
import scala.concurrent._
import com.google.common.util.concurrent.{FutureCallback, Futures, ListenableFuture}
import scala.collection.JavaConverters._
import scala.collection.generic.CanBuildFrom
import scala.concurrent.duration.Duration
object CQLContext {
// Consistency Levels
type CONSISTENCY_LEVEL = Int
val ANY: CONSISTENCY_LEVEL = 0x0000
val ONE: CONSISTENCY_LEVEL = 0x0001
val TWO: CONSISTENCY_LEVEL = 0x0002
val THREE: CONSISTENCY_LEVEL = 0x0003
val QUORUM : CONSISTENCY_LEVEL = 0x0004
val ALL: CONSISTENCY_LEVEL = 0x0005
val LOCAL_QUORUM: CONSISTENCY_LEVEL = 0x0006
val EACH_QUORUM: CONSISTENCY_LEVEL = 0x0007
val LOCAL_ONE: CONSISTENCY_LEVEL = 0x000A
val LOCAL_SERIAL: CONSISTENCY_LEVEL = 0x000B
val SERIAL: CONSISTENCY_LEVEL = 0x000C
def apply(): CQLContext = CQLContext(statements = Nil)
def consistencyLevel: CONSISTENCY_LEVEL => ConsistencyLevel = consistency => {
consistency match {
case ALL => ConsistencyLevel.ALL
case ONE => ConsistencyLevel.ONE
case TWO => ConsistencyLevel.TWO
case THREE => ConsistencyLevel.THREE
case ANY => ConsistencyLevel.ANY
case EACH_QUORUM => ConsistencyLevel.EACH_QUORUM
case LOCAL_ONE => ConsistencyLevel.LOCAL_ONE
case QUORUM => ConsistencyLevel.QUORUM
case SERIAL => ConsistencyLevel.SERIAL
case LOCAL_SERIAL => ConsistencyLevel.LOCAL_SERIAL
}
}
}
case class CQLQueryContext[M](
statement: String,
parameter: Seq[Object] = Nil,
consistency: Option[CQLContext.CONSISTENCY_LEVEL] = None,
extractor: Row => M
)
case class CQLContext(
statements: Seq[String],
parameters: Seq[Seq[Object]] = Nil,
consistency: Option[CQLContext.CONSISTENCY_LEVEL] = None
) { ctx =>
def setConsistencyLevel(_consistency: CQLContext.CONSISTENCY_LEVEL): CQLContext =
ctx.copy(consistency = Some(_consistency))
def setCommand(_statement: String, _parameters: Object*): CQLContext =
ctx.copy(statements = Seq(_statement), parameters = Seq(_parameters))
def appendCommand(_statement: String, _parameters: Object*): CQLContext =
ctx.copy(statements = ctx.statements :+ _statement,
parameters = ctx.parameters ++ Seq(_parameters))
}
object CQLEngine {
import CQLContext._
import CQLHelpers._
def fetchResultPage[C[_] <: TraversableOnce[_],A](ctx: CQLQueryContext[A], pageSize: Int = 100)(
implicit session: Session, cbf: CanBuildFrom[Nothing, A, C[A]]): (ResultSet, C[A])= {
val prepStmt = session.prepare(ctx.statement)
var boundStmt = prepStmt.bind()
if (ctx.parameter != Nil) {
val params = processParameters(ctx.parameter)
boundStmt = prepStmt.bind(params:_*)
}
ctx.consistency.foreach {consistency =>
boundStmt.setConsistencyLevel(consistencyLevel(consistency))}
val resultSet = session.execute(boundStmt.setFetchSize(pageSize))
(resultSet,(resultSet.asScala.view.map(ctx.extractor)).to[C])
}
def fetchMorePages[C[_] <: TraversableOnce[_],A](resultSet: ResultSet, timeOut: Duration)(
extractor: Row => A)(implicit cbf: CanBuildFrom[Nothing, A, C[A]]): (ResultSet,Option[C[A]]) =
if (resultSet.isFullyFetched) {
(resultSet, None)
} else {
try {
val result = Await.result(resultSet.fetchMoreResults(), timeOut)
(result, Some((result.asScala.view.map(extractor)).to[C]))
} catch { case e: Throwable => (resultSet, None) }
}
def cqlExecute(ctx: CQLContext)(
implicit session: Session, ec: ExecutionContext): Future[Boolean] = {
if (ctx.statements.size == 1)
cqlSingleUpdate(ctx)
else
cqlMultiUpdate(ctx)
}
def cqlSingleUpdate(ctx: CQLContext)(
implicit session: Session, ec: ExecutionContext): Future[Boolean] = {
val prepStmt = session.prepare(ctx.statements.head)
var boundStmt = prepStmt.bind()
if (ctx.statements != Nil) {
val params = processParameters(ctx.parameters.head)
boundStmt = prepStmt.bind(params:_*)
}
ctx.consistency.foreach {consistency =>
boundStmt.setConsistencyLevel(consistencyLevel(consistency))}
session.executeAsync(boundStmt).map(_.wasApplied())
}
def cqlMultiUpdate(ctx: CQLContext)(
implicit session: Session, ec: ExecutionContext): Future[Boolean] = {
val commands: Seq[(String,Seq[Object])] = ctx.statements zip ctx.parameters
var batch = new BatchStatement()
commands.foreach { case (stm, params) =>
val prepStmt = session.prepare(stm)
if (params == Nil)
batch.add(prepStmt.bind())
else {
val p = processParameters(params)
batch.add(prepStmt.bind(p: _*))
}
}
ctx.consistency.foreach {consistency =>
batch.setConsistencyLevel(consistencyLevel(consistency))}
session.executeAsync(batch).map(_.wasApplied())
}
}
object CQLHelpers {
import java.nio.ByteBuffer
import java.io._
import java.nio.file._
import com.datastax.driver.core.LocalDate
import com.datastax.driver.extras.codecs.jdk8.InstantCodec
import java.time.Instant
import akka.stream.scaladsl._
import akka.stream._
implicit def listenableFutureToFuture[T](
listenableFuture: ListenableFuture[T]): Future[T] = {
val promise = Promise[T]()
Futures.addCallback(listenableFuture, new FutureCallback[T] {
def onFailure(error: Throwable): Unit = {
promise.failure(error)
()
}
def onSuccess(result: T): Unit = {
promise.success(result)
()
}
})
promise.future
}
case class CQLDate(year: Int, month: Int, day: Int)
case object CQLTodayDate
case class CQLDateTime(year: Int, Month: Int,
day: Int, hour: Int, minute: Int, second: Int, millisec: Int = 0)
case object CQLDateTimeNow
def processParameters(params: Seq[Object]): Seq[Object] = {
params.map { obj =>
obj match {
case CQLDate(yy, mm, dd) => LocalDate.fromYearMonthDay(yy, mm, dd)
case CQLTodayDate =>
val today = java.time.LocalDate.now()
LocalDate.fromYearMonthDay(today.getYear, today.getMonth.getValue, today.getDayOfMonth)
case CQLDateTimeNow => Instant.now()
case CQLDateTime(yy, mm, dd, hr, ms, sc, mi) =>
Instant.parse(f"$yy%4d-$mm%2d-$dd%2dT$hr%2d:$ms%2d:$sc%2d$mi%3d")
case p@_ => p
}
}
}
class ByteBufferInputStream(buf: ByteBuffer) extends InputStream {
override def read: Int = {
if (!buf.hasRemaining) return -1
buf.get
}
override def read(bytes: Array[Byte], off: Int, len: Int): Int = {
val length: Int = Math.min(len, buf.remaining)
buf.get(bytes, off, length)
length
}
}
object ByteBufferInputStream {
def apply(buf: ByteBuffer): ByteBufferInputStream = {
new ByteBufferInputStream(buf)
}
}
class FixsizedByteBufferOutputStream(buf: ByteBuffer) extends OutputStream {
override def write(b: Int): Unit = {
buf.put(b.toByte)
}
override def write(bytes: Array[Byte], off: Int, len: Int): Unit = {
buf.put(bytes, off, len)
}
}
object FixsizedByteBufferOutputStream {
def apply(buf: ByteBuffer) = new FixsizedByteBufferOutputStream(buf)
}
class ExpandingByteBufferOutputStream(var buf: ByteBuffer, onHeap: Boolean) extends OutputStream {
private val increasing = ExpandingByteBufferOutputStream.DEFAULT_INCREASING_FACTOR
override def write(b: Array[Byte], off: Int, len: Int): Unit = {
val position = buf.position
val limit = buf.limit
val newTotal: Long = position + len
if(newTotal > limit){
var capacity = (buf.capacity * increasing)
while(capacity <= newTotal){
capacity = (capacity*increasing)
}
increase(capacity.toInt)
}
buf.put(b, 0, len)
}
override def write(b: Int): Unit= {
if (!buf.hasRemaining) increase((buf.capacity * increasing).toInt)
buf.put(b.toByte)
}
protected def increase(newCapacity: Int): Unit = {
buf.limit(buf.position)
buf.rewind
val newBuffer =
if (onHeap) ByteBuffer.allocate(newCapacity)
else ByteBuffer.allocateDirect(newCapacity)
newBuffer.put(buf)
buf.clear
buf = newBuffer
}
def size: Long = buf.position
def capacity: Long = buf.capacity
def byteBuffer: ByteBuffer = buf
}
object ExpandingByteBufferOutputStream {
val DEFAULT_INCREASING_FACTOR = 1.5f
def apply(size: Int, increasingBy: Float, onHeap: Boolean) = {
if (increasingBy <= 1) throw new IllegalArgumentException("Increasing Factor must be greater than 1.0")
val buffer: ByteBuffer =
if (onHeap) ByteBuffer.allocate(size)
else ByteBuffer.allocateDirect(size)
new ExpandingByteBufferOutputStream(buffer,onHeap)
}
def apply(size: Int): ExpandingByteBufferOutputStream = {
apply(size, ExpandingByteBufferOutputStream.DEFAULT_INCREASING_FACTOR, false)
}
def apply(size: Int, onHeap: Boolean): ExpandingByteBufferOutputStream = {
apply(size, ExpandingByteBufferOutputStream.DEFAULT_INCREASING_FACTOR, onHeap)
}
def apply(size: Int, increasingBy: Float): ExpandingByteBufferOutputStream = {
apply(size, increasingBy, false)
}
}
def cqlFileToBytes(fileName: String): ByteBuffer = {
val fis = new FileInputStream(fileName)
val b = new Array[Byte](fis.available + 1)
val length = b.length
fis.read(b)
ByteBuffer.wrap(b)
}
def cqlBytesToFile(bytes: ByteBuffer, fileName: String)(
implicit mat: Materializer): Future[IOResult] = {
val source = StreamConverters.fromInputStream(() => ByteBufferInputStream(bytes))
source.runWith(FileIO.toPath(Paths.get(fileName)))
}
def cqlDateTimeString(date: java.util.Date, fmt: String): String = {
val outputFormat = new java.text.SimpleDateFormat(fmt)
outputFormat.format(date)
}
def useJava8DateTime(cluster: Cluster) = {
//for jdk8 datetime format
cluster.getConfiguration().getCodecRegistry()
.register(InstantCodec.instance)
}
}
CQLEngineDemo.scala
import scala.util._
import akka.actor.ActorSystem
import akka.stream.ActorMaterializer
import com.datastax.driver.core._
import CQLEngine._
import CQLHelpers._
import com.datastax.driver.core.LocalDate
import java.nio.ByteBuffer
import scala.concurrent.duration._
object CQLEngineDemo extends App {
//#init-mat
implicit val cqlsys = ActorSystem("cqlSystem")
implicit val mat = ActorMaterializer()
implicit val ec = cqlsys.dispatcher
val cluster = new Cluster
.Builder()
.addContactPoints("localhost")
.withPort(9042)
.build()
useJava8DateTime(cluster)
implicit val session = cluster.connect()
val createCQL ="""
CREATE TABLE testdb.members (
id UUID primary key,
name TEXT,
description TEXT,
birthday DATE,
created_at TIMESTAMP,
picture BLOB
)"""
val ctxCreate = CQLContext().setCommand(createCQL)
val ctxInsert = CQLContext().setCommand("insert into testdb.members(id,name,description,birthday,created_at,picture)" +
" values(uuid(),?,?,?,?,?)", "alan xu", "alan-xu", CQLDate(1966, 11, 27), CQLDateTimeNow, cqlFileToBytes("/users/tiger/Nobody.png"))
val createData = for {
createTable <- cqlExecute(ctxCreate)
insertData <- cqlExecute(ctxInsert)
} yield(createTable, insertData)
createData.onComplete {
case Success((c,i)) => println(s"Create Table: $c, Insert Data $i")
case Failure(e) => println(e.getMessage)
}
scala.io.StdIn.readLine()
//data model
case class Member(
id: String,
name: String,
description: Option[String] = None,
birthday: LocalDate,
createdAt: java.util.Date,
picture: Option[ByteBuffer] = None)
//data row converter
val toMember = (rs: Row) => Member(
id = rs.getUUID("id").toString,
name = rs.getString("name"),
description = {
val d = rs.getString("description")
if (d == null)
None
else
Some(d)
Some(rs.getColumnDefinitions.toString)
},
birthday = rs.getDate("birthday"),
createdAt = rs.getTimestamp("created_at"),
picture = {
val pic = rs.getBytes("picture")
if (pic == null)
None
else
Some(pic)
}
)
try {
val qtx = CQLQueryContext(statement = "select * from testdb.members", extractor = toMember)
val (resultSet, vecResults) = fetchResultPage[Vector, Member](qtx)
var vecMembers: Vector[Member] = vecResults
var isExh = resultSet.isExhausted
var nextPage: (ResultSet, Option[Vector[Member]]) = (resultSet, Some(vecResults))
while (!isExh) {
nextPage = fetchMorePages[Vector,Member](nextPage._1,1 second)(toMember)
nextPage._2.foreach {vec =>
vecMembers = vecMembers ++ vec
}
isExh = resultSet.isExhausted
}
vecMembers.foreach { m =>
println(s"id: ${m.id}-name:${m.name}-${m.description} birthday: ${m.birthday.toString}")
println(s"created_at: ${cqlDateTimeString(m.createdAt,"yyyy-MM-dd HH:mm:ss.SSS")}")
m.picture match {
case Some(buf) =>
val fname = s"/users/tiger/pic-${m.name}.png"
cqlBytesToFile(buf,fname)
println(s"saving picture to $fname")
case _ => println("empty picture!")
}
}
} catch {
case e: Exception => println(e.getMessage)
}
scala.io.StdIn.readLine()
session.close()
cluster.close()
cqlsys.terminate()
}
原创文章，作者：ItWorker，如若转载，请注明出处：https://blog.ytso.com/industrynews/12811.html