I have been running spark statefull structured streaming in EMR production, before statestore was running on HDFS backend and accumulating Few GB;s like (2.5 gb) in hdfs directories, Later when I moved to rocksdb backend with 3.2.1, It accumulates close to 45GB in last 10days.
Also I using default rocksDB spark configurations. Im sure, It was clearing the state after the processing time out, Even now I see only 25MB and 6.5 million records in spark UI in overall.
How can we pruge rocks db statestore beyond some storage limit ?
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I need to load lots of vertices and edges to JanusGraph with Cassandra backend from other storage. I've read about bulk loading and Spark configuring (https://docs.janusgraph.org/advanced-topics/bulk-loading/ and https://docs.janusgraph.org/advanced-topics/hadoop/) .
It's clear how to configure JanusGraph for Spark usage but I'm still not sure how to use Spark then and if Spark can help to speed up inserting into graph.
Please give some use cases and code example of using Hadoop MapReduce or Spark to speed up bulk loading data to Janusgraph (Java or Python are preferred). Any help welcome!
I worked on POC project recently to Bulk Load data into JanusGraph using Apache Spark. We were getting pretty good performance loading data into using Spark. Setup and sample code is provided in the article below.
https://medium.com/#nitinpoddar/bulk-loading-data-into-janusgraph-ace7d146af05
https://medium.com/#nitinpoddar/bulk-loading-data-into-janusgraph-part-2-ca946db26582
Alternatively, you can write a Kafka consumer application to load data from your Kafka to JanusGraph. The amount of parallelism will be restricted to the number of partitions of the source/input topic from which your application is reading data. The application will be single-threaded but you can scale your application to the number of input topics. Each instance of your application can open up a connection and write to JanusGraph using a transaction. You can batch transactions with some batch size to spread the load.
I have a setup where the incoming data from Kafka cluster is processed by Apache Spark streaming job.
Version Info :-
Kafka = 0.8.x
Spark Version = 2.3.1
Recently when the capacity of Kafka cluster was increased(by adding new nodes) we suddenly saw an exponential increase in disk usage of spark clusters(most of the space was occupied by spark temp files)
I am not sure if these are related and wanted some pointers to address/debug the same.
As a precaution we have increased the disk space of spark clusters to avoid "No space left on device" error.
I am trying to understand if spark is an alternative to the vanilla MapReduce approach for analysis of BigData. Since spark saves the operations on the data in the memory so while using the HDFS as storage system for spark , does it take the advantage of distributed storage of the HDFS? For instance suppose i have 100GB CSV file stored in HDFS, now i want to do analysis on it. If i load that from HDFS to spark , will spark load the complete data in-memory to do the transformations or it will use the distributed environment for doing its jobs that HDFS provides for Storage which is leveraged by the MapReduce programs written in hadoop. If not then what is the advantage of using spark over HDFS ?
PS: I know spark spills on the disks if there is RAM overflow but does this spill occur for data per node(suppose 5 GB per node) of the cluster or for the complete data(100GB)?
Spark jobs can be configured to spill to local executor disk, if there is not enough memory to read your files. Or you can enable HDFS snapshots and caching between Spark stages.
You mention CSV, which is just a bad format to have in Hadoop in general. If you have 100GB of CSV, you could just as easily have less than half that if written in Parquet or ORC...
At the end of the day, you need some processing engine, and some storage layer. For example, Spark on Mesos or Kubernetes might work just as well as on YARN, but those are separate systems, and are not bundled and tied together as nicely as HDFS and YARN. Plus, like MapReduce, when using YARN, you are moving the execution to the NodeManagers on the datanodes, rather than pulling over data over the network, which you would be doing with other Spark execution modes. The NameNode and ResourceManagers coordinate this communication for where data is stored and processed
If you are convinced that MapReduceV2 can be better than Spark, I would encourage looking at Tez instead
We are running Spark/Hadoop on a different set of nodes than Cassandra. We have 10 Cassandra nodes and multiple spark cores but Cassandra is not running on Hadoop. Performance in fetching data from Cassandra through spark(in yarn client mode) is not very good and bulk data reads from HDFS are faster(6 mins in Cassandra to 2 mins in HDFS). Changing Spark-Cassandra parameters is not helping much also.
Will deploying Hadoop on top of Cassandra solve this issue and majorly impact read performance ?
Without looking at your code, bulk reads in an analytics/Spark capacity, are always going to be faster when directly going to the file VS. reading from a database. The database offers other advantages such as schema enforcement, availability, distribution control, etc but I think the performance differences you're seeing are normal.
I have around 100 GB of data in CSV format on which I intend to do some transformation like aggregation, data splitting and after that do some clustering using ML package of Apache Spark.
I have tried it by uploading data on MYSQ trying to automate the process on python but it's taking too much time to build any solution.
What is the configuration I need to setup and how I should start with the spark?
I am new in spark. I am planning to use cloud services.
I'm going to recommend you learn to use spark locally with a small subset of the data; you can run it standalone with a few tens moving to hundreds of MB. Its limited, but you can learn the tooling without paying. Your first spark dataframe query could be sampling the source data and saving it into a more efficient query format.
CSV isn't a great format for big data; Spark likes Parquet and for 2.3+ ORC). Embrace them for better perf.
Play with "notebooks"; Apache Zeppelin is one you can install and run locally.
Like I say, learn to play with small amounts. Spark is very interactive & working with small datasets is an easy way to learn fast.
There are many ways to do that but it depends on your case. As far as I know, HDFS with default configuration(without any specific tuning) works fine. Majority of Hadoop tuning guides are focused on YARN side. So, let me make a plan like below:
Generally speaking, you can put your (raw) data in HDFS and load them in Apache Spark and save them in Parquet/ORC like below:
from pyspark.sql.types import StructType,StructField,StringType
myschema = StructType([StructField("FirstName",StringType(),True),StructField("LastName",StringType(),True)])
mydf = spark.read.format("com.databricks.spark.csv").option("header","true").schema(myschema).option("delimiter",",").load("hdfs://hadoopmaster:9000/user/hduser/mydata.csv")
mydf.count()
mydf.repartition(6).write.format("parquet").save("hdfs://hadoopmaster:9000/user/hduser/DataInParquet")
newdf = spark.read.parquet("hdfs://hadoopmaster:9000/user/hduser/DataInParquet")
newdf.count()
Finally, compare mydf.count() with newdf.count(). That will run faster than raw format. In addition, your data size will decrease from 100GB to ~24GB.
If you are new to hadoop, spark and interested to setup hadoop environment in cloud. I would suggest you to go with Elastic Map Reduce(EMR) powered by AWS. You can create On demand spark cluster with the user defined configuration to process a wide range of data sets.
https://aws.amazon.com/emr/
https://aws.amazon.com/emr/details/spark/
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark-launch.html
Or
You can setup a hadoop cluster on top of EC2 instance or in any cloud platform with the required number of nodes with sufficient RAM and CPU. Storage optimized instances is preferred over here to analyze a large data set.
We do not need to bother about storage cost, For storage optimized instances, AWS offers free ephemeral storage data disk with size 1 - 2TB depends on instance size.
Note: Data in the ephemeral storage will be lost when the VM is rebooted. We can persist the processed data in S3 at the cheapest cost.
When it comes to cluster configuration, the list of things to be checked.
Spark on YARN is preferred
Set minimum and maximum core and memory in yarn node manager container settings for your spark executors.
Enable dynamic memory allocation in spark
Set container size to the maximum and spark memory fraction to maximum to avoid shuffling multiple times and frequent spilling and cached data eviction.
Use kryo serialization to get high performance.
Enable compression for map outputs before shuffling.
Enable spark web UI to track your application tasks and its stages.
Apache Spark Config Reference: https://spark.apache.org/docs/2.1.0/configuration.html