- Is Hadoop necessary for spark?
- Can we broadcast a DataFrame?
- How do I cache DataFrame in spark?
- Is spark cache an action?
- What is difference between cache and persist in spark?
- How can I improve my spark performance?
- How do I stop the spark shuffle?
- Why Your Spark applications are slow or failing?
- How do I test PySpark code?
- Is spark fast?
- What are spark jobs?
- Why is my spark job so slow?
- How do you optimize PySpark?
- Does spark cache automatically?
- What happens when spark driver fails?
- What is data skew and how do you fix it?
- How do I tune Apache spark jobs?
- When should I cache spark?
Is Hadoop necessary for spark?
Yes, Apache Spark can run without Hadoop, standalone, or in the cloud.
Spark doesn’t need a Hadoop cluster to work.
Spark is a meant for distributed computing.
In this case, the data is distributed across the computers and Hadoop’s distributed file system HDFS is used to store data that does not fit in memory..
Can we broadcast a DataFrame?
Spark can “broadcast” a small DataFrame by sending all the data in that small DataFrame to all nodes in the cluster. After the small DataFrame is broadcasted, Spark can perform a join without shuffling any of the data in the large DataFrame.
How do I cache DataFrame in spark?
Spark cache() method in Dataset class internally calls persist() method which in turn uses sparkSession. sharedState. cacheManager. cacheQuery to cache the result set of DataFrame or Dataset.
Is spark cache an action?
Is caching in spark a transformation or an action? Though cache() or persist() is just another function on RDD which marks RDD to be cached or persisted. The first time an RDD is evaluated as a consequence of an action, it will be persisted/cached. So, cache() or persist() is neither an action nor a transformation.
What is difference between cache and persist in spark?
Spark Cache vs Persist But, the difference is, RDD cache() method default saves it to memory (MEMORY_ONLY) whereas persist() method is used to store it to user-defined storage level. When you persist a dataset, each node stores it’s partitioned data in memory and reuses them in other actions on that dataset.
How can I improve my spark performance?
GroupByKey causes unnecessary shuffles and transfer of data over the network. Maintain the required size of the shuffle blocks – By default Spark shuffle block cannot exceed 2GB. The better use is to increase partitions and reduce its capacity to ~128MB per partition that will reduce the shuffle block size.
How do I stop the spark shuffle?
One way to avoid shuffles when joining two datasets is to take advantage of broadcast variables. When one of the datasets is small enough to fit in memory in a single executor, it can be loaded into a hash table on the driver and then broadcast to every executor.
Why Your Spark applications are slow or failing?
However, it becomes very difficult when Spark applications start to slow down or fail. Sometimes a well-tuned application might fail due to a data change, or a data layout change. Sometimes an application which was running well so far, starts behaving badly due to resource starvation.
How do I test PySpark code?
You can test PySpark code by running your code on DataFrames in the test suite and comparing DataFrame column equality or equality of two entire DataFrames. The quinn project has several examples. Create a tests/conftest.py file with this fixture, so you can easily access the SparkSession in your tests.
Is spark fast?
The biggest claim from Spark regarding speed is that it is able to “run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.” Spark could make this claim because it does the processing in the main memory of the worker nodes and prevents the unnecessary I/O operations with the disks.
What are spark jobs?
In a Spark application, when you invoke an action on RDD, a job is created. Jobs are the main function that has to be done and is submitted to Spark. The jobs are divided into stages depending on how they can be separately carried out (mainly on shuffle boundaries). Then, these stages are divided into tasks.
Why is my spark job so slow?
If you’re having trouble with your Spark applications, read on to see if these common memory management issues are plaguing your system. … However, it becomes very difficult when Spark applications start to slow down or fail. Sometimes a well-tuned application might fail due to a data change, or a data layout change.
How do you optimize PySpark?
PySpark execution logic and code optimizationDataFrames in pandas as a PySpark prerequisite. … PySpark DataFrames and their execution logic. … Consider caching to speed up PySpark. … Use small scripts and multiple environments in PySpark. … Favor DataFrame over RDD with structured data. … Avoid User Defined Functions in PySpark. … Number of partitions and partition size in PySpark.More items…•
Does spark cache automatically?
1 Answer. From the documentation: Spark also automatically persists some intermediate data in shuffle operations (e.g. reduceByKey), even without users calling persist. This is done to avoid recomputing the entire input if a node fails during the shuffle.
What happens when spark driver fails?
When the driver process fails, all the executors running in a standalone/yarn/mesos cluster are killed as well, along with any data in their memory. In case of Spark Streaming, all the data received from sources like Kafka and Flume are buffered in the memory of the executors until their processing has completed.
What is data skew and how do you fix it?
We can reduce data skew effect at the data uploading stage. The main idea is to clearly point to the skewed data (key) before their partitioning. This will allow the data to be distributed in a different way, which consider a data unevenness. As result, it will reduce the impact of data skew before calculations begin.
How do I tune Apache spark jobs?
a. Spark Data Structure TuningAvoid the nested structure with lots of small objects and pointers.Instead of using strings for keys, use numeric IDs or enumerated objects.If the RAM size is less than 32 GB, set JVM flag to –xx:+UseCompressedOops to make a pointer to four bytes instead of eight.
When should I cache spark?
An RDD that is not cached, nor checkpointed, is re-evaluated again each time an action is invoked on that RDD. … When to use caching: As suggested in this post, it is recommended to use caching in the following situations: RDD re-use in iterative machine learning applications. RDD re-use in standalone Spark applications.