Table of Contents
- 1 What is Apache GraphX?
- 2 What is unique feature of GraphX?
- 3 How the command Pregel works in GraphX?
- 4 How many tasks does spark run on each partition?
- 5 What is Pregel in big data?
- 6 What are partitions in Apache spark?
- 7 How the command Pregel works in graphx?
- 8 What is Pregel API?
- 9 What is rangerange partitioning in spark?
- 10 How does repartitionbyrange work in Apache Spark?
What is Apache GraphX?
GraphX is Apache Spark’s API for graphs and graph-parallel computation.
What is unique feature of GraphX?
Speed is one of the best features of GraphX. It provides comparable performance to the fastest specialized graph processing systems. It is fastest on comparing with the other graph systems. Even while retaining Spark’s flexibility, fault tolerance and ease of use.
Which programming languages can be used for using GraphX?
Support for Python and Java in addition to Scala APIs. Now we can use GraphX algorithms in all three languages.
How the command Pregel works in GraphX?
A Pregel computation takes a graph and a corresponding set of vertex states as its inputs. At each iteration, referred to as a superstep, each vertex can send a message to its neighbors, process messages it received in a previous superstep, and update its state.
How many tasks does spark run on each partition?
one task
Spark assigns one task per partition and each worker can process one task at a time.
Which programming languages can be used for using GraphX the Apache spark graph processing engine?
Apache Spark supports the following programming languages as an API: Java, Scala, and Python.
What is Pregel in big data?
The basic idea of Pregel is that we implement an algorithm that is executed on every vertex of a graph. It receives all messages from neighbor vertices and can optionally send messages to other vertices or update vertex value. Messages sent by this function will be received on the next iteration.
What are partitions in Apache spark?
In spark, the partition is an atomic chunk of data. Simply putting, it is a logical division of data stored on a node over the cluster. In apache spark, partitions are basic units of parallelism and RDDs, in spark are the collection of partitions.
What is co partitioning spark?
The RDD’s in spark are partitioned, using Hash Partitioner by default. Co-partitioned RDD’s uses same partitioner and thus have their data distributed across partitions in same manner. HashPartitioner will partition the data in the same way for both RDD’s,same data values in two different RDD will give same Hashvalue.
How the command Pregel works in graphx?
What is Pregel API?
Introduction. Pregel is a vertex-centric computation model to define your own algorithms via a user-defined compute function. Within that function, a node can receive messages from other nodes, typically its neighbors. Based on the received messages and its currently stored value, a node can compute a new value.
How to partition data in Apache Spark?
Versions: Apache Spark 2.4.0 The most popular partitioning strategy divides the dataset by the hash computed from one or more values of the record. However other partitioning strategies exist as well and one of them is range partitioning implemented in Apache Spark SQL with repartitionByRange method, described in this post.
What is rangerange partitioning in spark?
Range partitioning is one of 3 partitioning strategies in Apache Spark. As shown in the post, it can be used pretty easily in Apache Spark SQL module thanks to the repartitionBy method taking as parameters the number of targeted partitions and the columns used in the partitioning. In the 3rd section you can see some of the implementation details.
How does repartitionbyrange work in Apache Spark?
Apache Spark SQL implements range partitioning with repartitionByRange (numPartitions: Int, partitionExprs: Column*) added in 2.3.0 version. When called, the function creates numPartitions of partitions based on the columns specified in partitionExprs, like in this snippet: By default the partitioning expression is sorted in ascending order.
What is range partitioning and what are the advantages?
Thanks to range partitioning you can group similar items inside the same place, like or instance all orders for a given month. One of the advantages of this approach is the possibility to optimize the compression rate since the main idea behind the compression is to represent a repetitive value with fewer bits.