da1be15cc6
tdas
https://issues.apache.org/jira/browse/SPARK-7326
The problem most likely resides in DStream.slice() implementation, as shown below.
def slice(fromTime: Time, toTime: Time): Seq[RDD[T]] = {
if (!isInitialized) {
throw new SparkException(this + " has not been initialized")
}
if (!(fromTime - zeroTime).isMultipleOf(slideDuration)) {
logWarning("fromTime (" + fromTime + ") is not a multiple of slideDuration ("
+ slideDuration + ")")
}
if (!(toTime - zeroTime).isMultipleOf(slideDuration)) {
logWarning("toTime (" + fromTime + ") is not a multiple of slideDuration ("
+ slideDuration + ")")
}
val alignedToTime = toTime.floor(slideDuration, zeroTime)
val alignedFromTime = fromTime.floor(slideDuration, zeroTime)
logInfo("Slicing from " + fromTime + " to " + toTime +
" (aligned to " + alignedFromTime + " and " + alignedToTime + ")")
alignedFromTime.to(alignedToTime, slideDuration).flatMap(time => {
if (time >= zeroTime) getOrCompute(time) else None
})
}
Here after performing floor() on both fromTime and toTime, the result (alignedFromTime - zeroTime) and (alignedToTime - zeroTime) may no longer be multiple of the slidingDuration, thus making isTimeValid() check failed for all the remaining computation.
The fix is to add a new floor() function in Time.scala to respect the zeroTime while performing the floor :
def floor(that: Duration, zeroTime: Time): Time = {
val t = that.milliseconds
new Time(((this.millis - zeroTime.milliseconds) / t) * t + zeroTime.milliseconds)
}
And then change the DStream.slice to call this new floor function by passing in its zeroTime.
val alignedToTime = toTime.floor(slideDuration, zeroTime)
val alignedFromTime = fromTime.floor(slideDuration, zeroTime)
This way the alignedToTime and alignedFromTime are *really* aligned in respect to zeroTime whose value is not really a 0.
Author: Wesley Miao <wesley.miao@gmail.com>
Author: Wesley <wesley.miao@autodesk.com>
Closes #5871 from wesleymiao/spark-7326 and squashes the following commits:
82a4d8c [Wesley Miao] [SPARK-7326] [STREAMING] Performing window() on a WindowedDStream dosen't work all the time
48b4dc0 [Wesley] [SPARK-7326] [STREAMING] Performing window() on a WindowedDStream doesn't work all the time
6ade399 [Wesley] [SPARK-7326] [STREAMING] Performing window() on a WindowedDStream doesn't work all the time
2611745 [Wesley Miao] [SPARK-7326] [STREAMING] Performing window() on a WindowedDStream doesn't work all the time
(cherry picked from commit
|
||
---|---|---|
assembly | ||
bagel | ||
bin | ||
build | ||
conf | ||
core | ||
data/mllib | ||
dev | ||
docker | ||
docs | ||
ec2 | ||
examples | ||
external | ||
extras | ||
graphx | ||
launcher | ||
mllib | ||
network | ||
project | ||
python | ||
R | ||
repl | ||
sbin | ||
sbt | ||
sql | ||
streaming | ||
tools | ||
unsafe | ||
yarn | ||
.gitattributes | ||
.gitignore | ||
.rat-excludes | ||
CONTRIBUTING.md | ||
LICENSE | ||
make-distribution.sh | ||
NOTICE | ||
pom.xml | ||
README.md | ||
scalastyle-config.xml | ||
tox.ini |
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples
directory.
To run one of them, use ./bin/run-example <class> [params]
. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run all automated tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.