Table of Contents
- 1 Why your Spark apps are slow or failing Part I?
- 2 Why is my Spark job Slow?
- 3 How does Spark deal with memory problems?
- 4 What is Spark storage memory?
- 5 How do I make my spark job faster?
- 6 How can I make a spark job faster?
- 7 How can I improve my memory overhead?
- 8 How do I turn off-heap memory in Spark?
- 9 Why is my spark application running out of memory?
- 10 What are the most common problems with spark?
Why your Spark apps are slow or failing Part I?
Spark’s default configuration may or may not be sufficient or accurate for your applications. Sometimes even a well-tuned application may fail due to OOM as the underlying data has changed. Out of memory issues can be observed for the driver node, executor nodes, and sometimes even for the node manager.
Why is my Spark job Slow?
memory values will help determine if the workload requires more or less memory. YARN container memory overhead can also cause Spark applications to slow down because it takes YARN longer to allocate larger pools of memory. What happens is YARN runs every Spark component, like drivers and executors, within containers.
How does Spark deal with memory problems?
I have a few suggestions:
- If your nodes are configured to have 6g maximum for Spark (and are leaving a little for other processes), then use 6g rather than 4g, spark.
- Try using more partitions, you should have 2 – 4 per CPU.
- Decrease the fraction of memory reserved for caching, using spark.
How does Spark determine driver memory?
Determine the memory resources available for the Spark application. Multiply the cluster RAM size by the YARN utilization percentage. Provides 5 GB RAM for available drivers and 50 GB RAM available for worker nodes. Discount 1 core per worker node to determine the executor core instances.
What is overhead memory in Spark?
Memory overhead is the amount of off-heap memory allocated to each executor. By default, memory overhead is set to either 10\% of executor memory or 384, whichever is higher. Be sure that the sum of the driver or executor memory plus the driver or executor memory overhead is always less than the value of yarn.
What is Spark storage memory?
Storage memory is used for storing all of the cached data, broadcast variables are also stored here. Any persist option which includes MEMORY in it, spark will store that data in this segment. Spark clears space for new cache requests by removing old cached objects based on Least Recently Used (LRU) mechanism.
How do I make my spark job faster?
Table of contents
- Introduction.
- Configuring number of Executors, Cores, and Memory :
- Avoid Long Lineage.
- Broadcasting.
- Partitioning your DataSet.
- Columnar File Formats.
- Use DataFrames/Datasets instead of RDDs :
- End Notes.
How can I make a spark job faster?
Spark as a framework would take care of many aspects of clustered computation, however, applying the below techniques can help achieve better parallelism.
- Sizing the YARN Resources.
- Choose the Right Join.
- Choose the Right Data Format.
How do I increase Spark memory?
You can do that by either:
- setting it in the properties file (default is $SPARK_HOME/conf/spark-defaults.conf ), spark.driver.memory 5g.
- or by supplying configuration setting at runtime $ ./bin/spark-shell –driver-memory 5g.
How do I allocate the executor memory in Spark?
According to the recommendations which we discussed above: Number of available executors = (total cores/num-cores-per-executor) = 150/5 = 30. Leaving 1 executor for ApplicationManager => –num-executors = 29. Number of executors per node = 30/10 = 3. Memory per executor = 64GB/3 = 21GB.
How can I improve my memory overhead?
You can increase memory overhead while the cluster is running, when you launch a new cluster, or when you submit a job.
How do I turn off-heap memory in Spark?
Off-heap:
- spark. memory. offHeap. enabled – the option to use off-heap memory for certain operations (default false)
- spark. memory. offHeap. size – the total amount of memory in bytes for off-heap allocation. It has no impact on heap memory usage, so make sure not to exceed your executor’s total limits (default 0)
Why is my spark application running out of memory?
Out of memory at the executor level This is a very common issue with Spark applications which may be due to various reasons. Some of the most common reasons are high concurrency, inefficient queries, and incorrect configuration. Let’s look at each in turn.
What is clustering manager in spark?
Cluster Manager : An external service for acquiring resources on the cluster (e.g. standalone manager, Mesos, YARN). Spark is agnostic to a cluster manager as long as it can acquire executor processes and those can communicate with each other.We are primarily interested in Yarn as the cluster manager.
How much memory does spark use for execution and storage?
Both execution & storage memory can be obtained from a configurable fraction of (total heap memory – 300MB). That setting is “spark.memory.fraction”. Default is 60\%. Out of which, by default, 50\% is assigned (configurable by “spark.memory.storageFraction”) to storage and rest assigned for execution.
What are the most common problems with spark?
The first and most common is memory management. If we were to get all Spark developers to vote, out of memory (OOM) conditions would surely be the number one problem everyone has faced. This comes as no big surprise as Spark’s architecture is memory-centric. Some of the most common causes of OOM are: