What is the use of groupByKey in Spark?
What is the use of groupByKey in Spark?
In Spark, the groupByKey function is a frequently used transformation operation that performs shuffling of data. It receives key-value pairs (K, V) as an input, group the values based on key and generates a dataset of (K, Iterable ) pairs as an output.
What is the difference between groupByKey and reduceByKey in Spark?
Both reduceByKey and groupByKey result in wide transformations which means both triggers a shuffle operation. The key difference between reduceByKey and groupByKey is that reduceByKey does a map side combine and groupByKey does not do a map side combine.
Why is groupByKey better than reduceByKey?
reduceByKey() works better with larger datasets when compared to groupByKey() . In reduceByKey() , pairs on the same machine with the same key are combined (by using the function passed into reduceByKey() ) before the data is shuffled.
What is aggregateByKey Spark?
Function aggregateByKey is one of the aggregate function (Others are reduceByKey & groupByKey) available in Spark. This is the only aggregation function which allows multiple type of aggregation(Maximun, minimun, average, sum & count) at the same time.
What is caching in Spark?
By caching you create a checkpoint in your spark application and if further down the execution of application any of the tasks fail your application will be able to recompute the lost RDD partition from the cache.
What is the difference between RDD and DataFrame in Spark?
3.2. RDD – RDD is a distributed collection of data elements spread across many machines in the cluster. RDDs are a set of Java or Scala objects representing data. DataFrame – A DataFrame is a distributed collection of data organized into named columns. It is conceptually equal to a table in a relational database.
What is shuffling in Spark?
Shuffling is a mechanism Spark uses to redistribute the data across different executors and even across machines. Spark shuffling triggers for transformation operations like gropByKey() , reducebyKey() , join() , groupBy() e.t.c. Spark Shuffle is an expensive operation since it involves the following. Disk I/O.
What is difference between reduce and reduceByKey in Spark?
Basically, reduce must pull the entire dataset down into a single location because it is reducing to one final value. reduceByKey on the other hand is one value for each key. And since this action can be run on each machine locally first then it can remain an RDD and have further transformations done on its dataset.
What is Spark reduceByKey?
In Spark, the reduceByKey function is a frequently used transformation operation that performs aggregation of data. It receives key-value pairs (K, V) as an input, aggregates the values based on the key and generates a dataset of (K, V) pairs as an output.
Which is better cache or persist?
The only difference between cache() and persist() is ,using Cache technique we can save intermediate results in memory only when needed while in Persist() we can save the intermediate results in 5 storage levels(MEMORY_ONLY, MEMORY_AND_DISK, MEMORY_ONLY_SER, MEMORY_AND_DISK_SER, DISK_ONLY).
What is serialization in Spark?
Serialization is used for performance tuning on Apache Spark. All data that is sent over the network or written to the disk or persisted in the memory should be serialized. Serialization plays an important role in costly operations. PySpark supports custom serializers for performance tuning.
Why DF is faster than RDD?
RDD – RDD API is slower to perform simple grouping and aggregation operations. DataFrame – DataFrame API is very easy to use. It is faster for exploratory analysis, creating aggregated statistics on large data sets. DataSet – In Dataset it is faster to perform aggregation operation on plenty of data sets.
Why Dataset is faster than RDD?
RDD is slower than both Dataframes and Datasets to perform simple operations like grouping the data. It provides an easy API to perform aggregation operations. It performs aggregation faster than both RDDs and Datasets. Dataset is faster than RDDs but a bit slower than Dataframes.
What is lazy evaluation in Spark?
Lazy evaluation means that if you tell Spark to operate on a set of data, it listens to what you ask it to do, writes down some shorthand for it so it doesn’t forget, and then does absolutely nothing. It will continue to do nothing, until you ask it for the final answer.
How can I improve my Spark performance?
Spark Performance Tuning – Best Guidelines & Practices
- Use DataFrame/Dataset over RDD.
- Use coalesce() over repartition()
- Use mapPartitions() over map()
- Use Serialized data format’s.
- Avoid UDF’s (User Defined Functions)
- Caching data in memory.
- Reduce expensive Shuffle operations.
- Disable DEBUG & INFO Logging.
What is difference between MAP and flatMap in Spark?
Spark map function expresses a one-to-one transformation. It transforms each element of a collection into one element of the resulting collection. While Spark flatMap function expresses a one-to-many transformation. It transforms each element to 0 or more elements.
What is the difference between reduce and reduceByKey?
What is StreamingContext in Spark?
public class StreamingContext extends Object implements Logging. Main entry point for Spark Streaming functionality. It provides methods used to create DStream s from various input sources. It can be either created by providing a Spark master URL and an appName, or from a org. apache.
When should I use persist in Spark?
Spark RDD persistence is an optimization technique which saves the result of RDD evaluation in cache memory. Using this we save the intermediate result so that we can use it further if required. It reduces the computation overhead.
Why parquet is best for Spark?
It is well-known that columnar storage saves both time and space when it comes to big data processing. Parquet, for example, is shown to boost Spark SQL performance by 10X on average compared to using text, thanks to low-level reader filters, efficient execution plans, and in Spark 1.6. 0, improved scan throughput!
What is difference between Dataset and DataFrame in Spark?
In Spark, datasets are an extension of dataframes. Basically, it earns two different APIs characteristics, such as strongly typed and untyped. Datasets are by default a collection of strongly typed JVM objects, unlike dataframes. Moreover, it uses Spark’s Catalyst optimizer.
Which is better RDD or Dataframe?
Why is RDD called lazy evaluation?
We can think Spark RDD as the data, that we built up through transformation. Since transformations are lazy in nature, so we can execute operation any time by calling an action on data. Hence, in lazy evaluation data is not loaded until it is necessary.
How do you salt a Spark?
How to use SALT in Spark
- Add a new field and populate it with random numbers.
- Combine this new field and the existing keys as a composite key, perform any transformation.
- Once the processing is done, combine the final result.
What is StreamingContext & JavaStreamingContext?
public class JavaStreamingContext extends Object implements java.io.Closeable. A Java-friendly version of StreamingContext which is the main entry point for Spark Streaming functionality. It provides methods to create JavaDStream and JavaPairDStream from input sources. The internal org.