A while back I had the problem that outputting directly parquets to S3 isn't really feasible and I needed a caching layer before I finally copy the parquets to S3 see this post
I know that HADOOP-13786 should fix this problem and it seems to be implemented in HDFS >3.1.0
Now the question is how do I use it in spark 2.3.3 as far as I understand it spark 2.3.3 comes with hdfs 2.8.5. I usually use flintrock to orchestrate my cluster on AWS. Is it just a matter of setting HDFS to 3.1.1 in the flintrock config and then I get all the goodies? Or do I still for example have to set something in code like I did before. For example like this:
conf = SparkConf().setAppName(appname)\
.setMaster(master)\
.set('spark.executor.memory','13g')\
.set('spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version','2')\
.set('fs.s3a.fast.upload','true')\
.set('fs.s3a.fast.upload.buffer','disk')\
.set('fs.s3a.buffer.dir','/tmp/s3a')
(I know this is the old code and probably no longer relevant)
You'll need Hadoop 3.1, and a build of Spark 2.4 which has this PR applied: https://github.com/apache/spark/pull/24970
Some downstream products with their own Spark builds do this (HDP-3.1), but it's not (yet) in the apache builds.
With that you then need to configure parquet to use the new bridging committer (Parquet only allows subclasses of the Parquet committer), and select the specific S3A committer of three (long story) to use. The Staging committer is the one I'd recommend as its (a) based on the one Netflix use and (b) the one I've tested the most.
There's no fundamental reason why the same PR can't be applied to Spark 2.3, just that nobody has tried.
what is the default behavior to Map Shuffling in the newer version of Spark?
I learned that configuration of spark.shuffle.consolidateFile is used to reduce memory cost by write buffer. But I cannot find the configuration anymore. I checked configuration, and from Spark 1.6.0, this configuration has been removed. So what is the default behavior to Map Shuffling in the newer version of Spark?
I learned that hash shuffle has been replaced by sort shuffle. So this configuration makes no sense anymore.
I could sound naive asking this question but this is a problem that I have recently faced in my project. Need some better understanding on it.
df.persist(StorageLevel.MEMORY_AND_DISK)
Whenever we use such persist on a HBase read - the same data is returned again and again for the other subsequent batches of the streaming job but HBase is updated for every batch run.
HBase Read Code:
val df = sqlContext.read.options(Map(HBaseTableCatalog.tableCatalog -> schema)).format(dbSetup.dbClass).load().persist(StorageLevel.MEMORY_AND_DISK)
I replaced persist(StorageLevel.MEMORY_AND_DISK) with cache() and it was returning updated records from HBase table as expected.
The reason we tried to use persist(StorageLevel.MEMORY_AND_DISK) is to ensure that the in-memory storage does not get full and we do not end up doing all transformations all over again during the execution of a particular stream.
Spark Version - 1.6.3
HBase Version - 1.1.2.2.6.4.42-1
Could someone explain me this and help me get a better understanding?
As you mentioned you are looking for a reason "why" therefore I'm answering this because otherwise this question will remain unanswered as there's no rational reason these days to run spark 1.6.3 to sense what happens with that specific HBASE version.
Internally, spark calls persist() when you use cache() and it behaves differently on RDDs than on Datasets(or Dataframes).
On RDDs it uses MEMORY_ONLY and on Datasets, MEMORY_AND_DISK.I cant see what you've coded(fully) but generally I can say, you shouldn't have face the difference between the two ways of cache and persist and your issue is simply a version incompatibility btw those or simply a bug that wasn't fixed by Apache.
There are several places to check to see what's wrong
In this link https://spark.apache.org/releases/spark-release-1-6-3.html you can find that maintainance of the code is hapening in branch 1.6 so this is the place to find the code https://github.com/apache/spark/blob/branch-1.6/core/src/main/scala/org/apache/spark/CacheManager.scala
Hope it helped.
I am running a Spark application that has a series of Spark SQL statements that are executed one after the other. The SQL queries are quite complex and the application is working (generating output). These days, I am working towards improving the performance of processing within Spark.
Please suggest whether Tungsten encoding has to be enabled separately or it kicks in automatically while running Spark SQL?
I am using Cloudera 5.13 for my cluster (2 node).
It is enabled by default in spark 2.X (and maybe 1.6: but i'm not sure on that).
In any case you can do this
spark.sql.tungsten.enabled=true
That can be enabled on the spark-submit as follows:
spark-submit --conf spark.sql.tungsten.enabled=true
Tungsten should be enabled if you see a * next to the plan:
Also see: How to enable Tungsten optimization in Spark 2?
Tungsten became the default in Spark 1.5 and can be enabled in an earlier version by setting the spark.sql.tungsten.enabled = true.
Even without Tungsten, SparkSQL uses a columnar storage format with Kyro serialization to minimize storage cost.
To make sure your code benefits as much as possible from Tungsten optimizations try to use the default Dataset API with Scala (instead of RDD).
Dataset brings the best of both worlds with a mix of relational (DataFrame) and functional (RDD) transformations. DataSet APIs are the most up to date and adds type-safety along with better error handling and far more readable unit tests.
One of the great benefits of the Parquet data storage format is that it's columnar. If I've got a 'wide' dataset with hundreds of columns, but my query only touches a few of those, then it's possible read only the data that stores those few columns, and skip the rest.
Presumably this feature works by reading a bit of metadata at the head of a parquet file that indicates the locations on the filesystem for each column. The reader can then seek on disk to read in only the necessary columns.
Does anyone know whether spark's default parquet reader correctly implements this kind of selective seeking on S3? I think it's supported by S3, but there's a big difference between theoretical support and an implementation that properly exploits that support.
This needs to be broken down
Does the Parquet code get the predicates from spark (yes)
Does parquet then attempt to selectively read only those columns, using the Hadoop FileSystem seek() + read() or readFully(position, buffer, length) calls? Yes
Does the S3 connector translate these File Operations into efficient HTTP GET requests? In Amazon EMR: Yes. In Apache Hadoop, you need hadoop 2.8 on the classpath and set the properly spark.hadoop.fs.s3a.experimental.fadvise=random to trigger random access.
Hadoop 2.7 and earlier handle the aggressive seek() round the file badly, because they always initiate a GET offset-end-of-file, get surprised by the next seek, have to abort that connection, reopen a new TCP/HTTPS 1.1 connection (slow, CPU heavy), do it again, repeatedly. The random IO operation hurts on bulk loading of things like .csv.gz, but is critical to getting ORC/Parquet perf.
You don't get the speedup on Hadoop 2.7's hadoop-aws JAR. If you need it you need to update hadoop*.jar and dependencies, or build Spark up from scratch against Hadoop 2.8
Note that Hadoop 2.8+ also has a nice little feature: if you call toString() on an S3A filesystem client in a log statement, it prints out all the filesystem IO stats, including how much data was discarded in seeks, aborted TCP connections &c. Helps you work out what's going on.
2018-04-13 warning:: Do not try to drop the Hadoop 2.8+ hadoop-aws JAR on the classpath along with the rest of the hadoop-2.7 JAR set and expect to see any speedup. All you will see are stack traces. You need to update all the hadoop JARs and their transitive dependencies.
DISCLAIMER: I don't have a definitive answer and don't want to act as an authoritative source either, but have spent some time on parquet support in Spark 2.2+ and am hoping that my answer can help us all to get closer to the right answer.
Does Parquet on S3 avoid pulling the data for unused columns from S3 and only retrieve the file chunks it needs, or does it pull the whole file?
I use Spark 2.3.0-SNAPSHOT that I built today right from the master.
parquet data source format is handled by ParquetFileFormat which is a FileFormat.
If I'm correct, the reading part is handled by buildReaderWithPartitionValues method (that overrides the FileFormat's).
buildReaderWithPartitionValues is used exclusively when FileSourceScanExec physical operator is requested for so-called input RDDs that are actually a single RDD to generate internal rows when WholeStageCodegenExec is executed.
With that said, I think that reviewing what buildReaderWithPartitionValues does may get us closer to the final answer.
When you look at the line you can get assured that we're on the right track.
// Try to push down filters when filter push-down is enabled.
That code path depends on spark.sql.parquet.filterPushdown Spark property that is turned on by default.
spark.sql.parquet.filterPushdown Enables Parquet filter push-down optimization when set to true.
That leads us to parquet-hadoop's ParquetInputFormat.setFilterPredicate iff the filters are defined.
if (pushed.isDefined) {
ParquetInputFormat.setFilterPredicate(hadoopAttemptContext.getConfiguration, pushed.get)
}
The code gets more interesting a bit later when the filters are used when the code falls back to parquet-mr (rather than using the so-called vectorized parquet decoding reader). That's the part I don't really understand (except what I can see in the code).
Please note that the vectorized parquet decoding reader is controlled by spark.sql.parquet.enableVectorizedReader Spark property that is turned on by default.
TIP: To know what part of the if expression is used, enable DEBUG logging level for org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat logger.
In order to see all the pushed-down filters you could turn INFO logging level of org.apache.spark.sql.execution.FileSourceScanExec logger on. You should see the following in the logs:
INFO Pushed Filters: [pushedDownFilters]
I do hope that if it's not close to be a definitive answer it has helped a little and someone picks it up where I left off to make it one soon. Hope dies last :)
parquet reader of spark is just like any other InputFormat,
None of the inputFormat have any thing special for S3. The input formats can read from LocalFileSystem , Hdfs and S3 no special optimization done for that.
Parquet InpuTFormat depending on the columns you ask will selectively read the columns for you .
If you want to be dead sure (although push down predicates works in latest spark version) manually select the columns and write the transformation and actions , instead of depending on SQL
No, predicate pushdown is not fully supported. This, of course, depends on:
Specific use case
Spark version
S3 connector type and version
In order to check your specific use case, you can enable DEBUG log level in Spark, and run your query. Then, you can see whether there are "seeks" during S3 (HTTP) requests as well as how many requests to were actually sent. Something like this:
17/06/13 05:46:50 DEBUG wire: http-outgoing-1 >> "GET /test/part-00000-b8a8a1b7-0581-401f-b520-27fa9600f35e.snappy.parquet HTTP/1.1[\r][\n]"
....
17/06/13 05:46:50 DEBUG wire: http-outgoing-1 << "Content-Range: bytes 0-7472093/7472094[\r][\n]"
....
17/06/13 05:46:50 DEBUG wire: http-outgoing-1 << "Content-Length: 7472094[\r][\n]"
Here's example of an issue report that was opened recently due to inability of Spark 2.1 to calculate COUNT(*) of all the rows in a dataset based on metadata stored in Parquet file: https://issues.apache.org/jira/browse/SPARK-21074