Would like to know the relation between Spark executors, cores and Elasticsearch batch size and how to tune Spark job optimally to get better index throughput.
I have 3.5B data in Parquet format and I would like to ingest them to Elasticsearch and I'm not getting more than 20K index rate. Sometimes I got 60K-70K but it comes down immediately and the average I got was around 15K-25K indexes per second.
Little bit more details about my input:
Around 22,000 files in Parquet format
It contains around 3.2B records (around 3TB in size)
Currently running 18 executors (3 executors per node)
Details about my current ES setup:
8 nodes, 1 master and 7 data nodes
Index with 70 shards
Index contains 49 fields (none of them are analyzied)
No replication
"indices.store.throttle.type" : "none"
"refresh_interval" : "-1"
es.batch.size.bytes: 100M (I tried with 500M also)
I'm very new in Elasticsearch so not sure how to tune my Spark job to get better performance.
Related
I am working on Apache Spark standalone cluster with 2 executors, each having 1g heap space and 8 cores each.
I load input file having size 2.7Gb into a dataframe df. This was successfully done using 21 tasks, that is I used 21 partitions in total across my whole cluster.
Now I tried writing this out to csv using only 1 partition, so that I get all my records in 1 csv file.
df.coalesce(1).write.option("header","true").csv("output.csv")
I expected to get an OOM error since the total usable memory for an executor is less than 2.7Gb. But this did not happen.
How did my task not break despite the data being larger than a single partition? What exactly is happening here under the hood?
The original csv file is of size 2.7GB in its raw format (text-based, no compression). When you read that file with Spark it splits up the data into multiple partitions based on the configuration spark.files.maxPartitionBytes which defaults to 128MB. Doing the math leads to 2700MB / 128MB = 21 partitions.
Spark keeps the data in-memory but in its own storage format which is called "Vectorized Parquet" and using a default compression "lz4".
Therefore, the 2.7GB will fit into the provided 1GB memory.
Keep in mind, that not all 100% of the 1GB is available to use for data store/processing. There is a clear design to the executors memory that can be configured by the configuration spark.memory.fraction and spark.memory.storageFraction. I have written an article on medium about the Executor Memory Layout.
Here is a picture that helps to understand the Memory Layout:
I do a fair amount of ETL using Apache Spark on EMR.
I'm fairly comfortable with most of the tuning necessary to get good performance, but I have one job that I can't seem to figure out.
Basically, I'm taking about 1 TB of parquet data - spread across tens of thousands of files in S3 - and adding a few columns and writing it out partitioned by one of the date attributes of the data - again, parquet formatted in S3.
I run like this:
spark-submit --conf spark.dynamicAllocation.enabled=true --num-executors 1149 --conf spark.driver.memoryOverhead=5120 --conf spark.executor.memoryOverhead=5120 --conf spark.driver.maxResultSize=2g --conf spark.sql.shuffle.partitions=1600 --conf spark.default.parallelism=1600 --executor-memory 19G --driver-memory 19G --executor-cores 3 --driver-cores 3 --class com.my.class path.to.jar <program args>
The size of the cluster is dynamically determined based on the size of the input data set, and the num-executors, spark.sql.shuffle.partitions, and spark.default.parallelism arguments are calculated based on the size of the cluster.
The code roughly does this:
va df = (read from s3 and add a few columns like timestamp and source file name)
val dfPartitioned = df.coalesce(numPartitions)
val sqlDFProdDedup = spark.sql(s""" (query to dedup against prod data """);
sqlDFProdDedup.repartition($"partition_column")
.write.partitionBy("partition_column")
.mode(SaveMode.Append).parquet(outputPath)
When I look at the ganglia chart, I get a huge resource spike while the de-dup logic runs and some data shuffles, but then the actual writing of the data only uses a tiny fraction of the resources and runs for several hours.
I don't think the primary issue is partition skew, because the data should be fairly distributed across all the partitions.
The partition column is essentially a day of the month, so each job typically only has 5-20 partitions, depending on the span of the input data set. Each partition typically has about 100 GB of data across 10-20 parquet files.
I'm setting spark.sql.files.maxRecordsPerFile to manage the size of those output files.
So, my big question is: how can I improve the performance here?
Simply adding resources doesn't seem to help much.
I've tried making the executors larger (to reduce shuffling) and also to increase the number of CPUs per executor, but that doesn't seem to matter.
Thanks in advance!
Zack, I have a similar use case with 'n' times more files to process on a daily basis. I am going to assume that you are using the code above as is and trying to improve the performance of the overall job. Here are couple of my observations:
Not sure what the coalesce(numPartitions) number actually is and why its being used before de-duplication process. Your spark-submit shows you are creating 1600 partitions and thats good enough to start with.
If you are going to repartition before write then the coalesce above may not be beneficial at all as re-partition will shuffle data.
Since you claim writing 10-20 parquet files it means you are only using 10-20 cores in writing in the last part of your job which is the main reason its slow. Based on 100 GB estimate the parquet file ranges from approx 5GB to 10 GB, which is really huge and I doubt one will be able to open them on their local laptop or EC2 machine unless they use EMR or similar (with huge executor memory if reading whole file or spill to disk) because the memory requirement will be too high. I will recommend creating parquet files of around 1GB to avoid any of those issues.
Also if you create 1GB parquet file, you will likely speed up the process 5 to 10 times as you will be using more executors/cores to write them in parallel. You can actually run an experiment by simply writing the dataframe with default partitions.
Which brings me to the point that you really don't need to use re-partition as you want to write.partitionBy("partition_date") call. Your repartition() call is actually forcing the dataframe to only have max 30-31 partitions depending upon the number of days in that month which is what is driving the number of files being written. The write.partitionBy("partition_date") is actually writing the data in S3 partition and if your dataframe has say 90 partitions it will write 3 times faster (3 *30). df.repartition() is forcing it to slow it down. Do you really need to have 5GB or larger files?
Another major point is that Spark lazy evaluation is sometimes too smart. In your case it will most likely only use the number of executors for the whole program based on the repartition(number). Instead you should try, df.cache() -> df.count() and then df.write(). What this does is that it forces spark to use all available executor cores. I am assuming you are reading files in parallel. In your current implementation you are likely using 20-30 cores. One point of caution, as you are using r4/r5 machines, feel free to up your executor memory to 48G with 8 cores. I have found 8cores to be faster for my task instead of standard 5 cores recommendation.
Another pointer is to try ParallelGC instead of G1GC. For the use case like this when you are reading 1000x of files, I have noticed it performs better or not any worse than G1Gc. Please give it a try.
In my workload, I use coalesce(n) based approach where 'n' gives me a 1GB parquet file. I read files in parallel using ALL the cores available on the cluster. Only during the write part my cores are idle but there's not much you can do to avoid that.
I am not sure how spark.sql.files.maxRecordsPerFile works in conjunction with coalesce() or repartition() but I have found 1GB seems acceptable with pandas, Redshift spectrum, Athena etc.
Hope it helps.
Charu
Here are some optimizations for faster running.
(1) File committer - this is how Spark will read the part files out to the S3 bucket. Each operation is distinct and will be based upon
spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version 2
Description
This will write the files directly to part files instead or initially loading them to temp files and copying them over to their end-state part files.
(2) For file size you can derive it based upon getting the average number of bytes per record. Below I am figuring out the number of bytes per record to figure the number of records for 1024 MBs. I would try it first with 1024MBs per partition, then move upwards.
import org.apache.spark.util.SizeEstimator
val numberBytes : Long = SizeEstimator.estimate(inputDF.rdd)
val reduceBytesTo1024MB = numberBytes/123217728
val numberRecords = inputDF.count
val recordsFor1024MB = (numberRecords/reduceBytesTo1024MB).toInt + 1
(3) [I haven't tried this] EMR Committer - if you are using EMR 5.19 or higher, since you are outputting Parquet. You can set the Parquet optimized writer to TRUE.
spark.sql.parquet.fs.optimized.committer.optimization-enabled true
I am using pyspark dataframe on dataproc cluster to generate features and writing parquet files as output to the Google Cloud Storage. There are two problems I am facing-
I have provided 22 executers, 3 cores per exec and ~13G RAM per executer. However only 10 executers are fired when I submit the jobs. The dataproc cluster contains 10 worker nodes and 8 cores per node and 30 GB ram per node.
When I write the individual feature files and record the total time, it is significantly lower then the time taken to write all the features together in a single file. I have tried changing the partitions but doesn't help either.
This is how I write the parquet file:
df.select([feature_lst]).write.parquet(gcs_path+outfile,mode='overwrite')
data size - 20M+ records, 30+ numerical features
Spark UI image:
The current stage is when I write all features together- significantly higher than all of previous stages combined.
If someone can provide any insight into the above two issues I will be grateful.
I have a 3 Node cluster on same DC and same rack. Keyspace has Replication Factor with 2, I have a spark application which is taking data form Kafka and Now I'am saving the RDD to Cassandra with
rdd.saveToCassandra("db_name", "table_name")
I'm consuming with Time interval of 10 Seconds and every batch will have 10k records and size per batch is around 2.5 MB
In Spark Conf I have setup
.set("spark.cassandra.output.consistency.level", "ONE")
Application takes around 2-3 seconds to insert. Why so? I would like to optimise. Earlier when I was using 1 Node machine with RF-1 , I was able to insert at a rate of 0.8-1 second/ batch. So, why this much delay after increase in node and RF.
Is there any other setting do I need to make in Spark Conf or cassandra side to increase write speed.
I have a cluster and I execute wholeTextFiles which should pull about a million text files who sum up to approximately 10GB total
I have one NameNode and two DataNode with 30GB of RAM each, 4 cores each. The data is stored in HDFS.
I don't run any special parameters and the job takes 5 hours to just read the data. Is that expected? are there any parameters that should speed up the read (spark configuration or partition, number of executors?)
I'm just starting and I've never had the need to optimize a job before
EDIT: Additionally, can someone explain exactly how the wholeTextFiles function works? (not how to use it, but how it was programmed). I'm very interested in understand the partition parameter, etc.
EDIT 2: benchmark assessment
So I tried repartition after the wholeTextFile, the problem is the same because the first read is still using the pre-defined number of partitions, so there are no performance improvements. Once the data is loaded the cluster performs really well... I have the following warning message when dealing with the data (for 200k files), on the wholeTextFile:
15/01/19 03:52:48 WARN scheduler.TaskSetManager: Stage 0 contains a task of very large size (15795 KB). The maximum recommended task size is 100 KB.
Would that be a reason of the bad performance? How do I hedge that?
Additionally, when doing a saveAsTextFile, my speed according to Ambari console is 19MB/s. When doing a read with wholeTextFiles, I am at 300kb/s.....
It seems that by increase the number of partitions in wholeTextFile(path,partitions), I am getting better performance. But still only 8 tasks are running at the same time (my number of CPUs). I'm benchmarking to observe the limit...
To summarize my recommendations from the comments:
HDFS is not a good fit for storing many small files. First of all, NameNode stores metadata in memory so the amount of files and blocks you might have is limited (~100m blocks is a max for typical server). Next, each time you read file you first query NameNode for block locations, then connect to the DataNode storing the file. Overhead of this connections and responses is really huge.
Default settings should always be reviewed. By default Spark starts on YARN with 2 executors (--num-executors) with 1 thread each (--executor-cores) and 512m of RAM (--executor-memory), giving you only 2 threads with 512MB RAM each, which is really small for the real-world tasks
So my recommendation is:
Start Spark with --num-executors 4 --executor-memory 12g --executor-cores 4 which would give you more parallelism - 16 threads in this particular case, which means 16 tasks running in parallel
Use sc.wholeTextFiles to read the files and then dump them into compressed sequence file (for instance, with Snappy block level compression), here's an example of how this can be done: http://0x0fff.com/spark-hdfs-integration/. This will greatly reduce the time needed to read them with the next iteration