What is a StreamingContext Object?
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.
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.
What is a DStream?
Discretized Stream or DStream is the basic abstraction provided by Spark Streaming. It represents a continuous stream of data, either the input data stream received from source, or the processed data stream generated by transforming the input stream.
What is Spark Streaming used for?
Spark Streaming is an extension of the core Spark API that allows data engineers and data scientists to process real-time data from various sources including (but not limited to) Kafka, Flume, and Amazon Kinesis. This processed data can be pushed out to file systems, databases, and live dashboards.
What is checkpointing in Spark Streaming?
What is Spark Streaming Checkpoint. A process of writing received records at checkpoint intervals to HDFS is checkpointing. It is a requirement that streaming application must operate 24/7. Hence, must be resilient to failures unrelated to the application logic such as system failures, JVM crashes, etc.
What is the difference between Spark Streaming and structured Streaming?
Both the Apache Spark streaming and the structured streaming models use micro- (or mini-) batching as their primary processing mechanisms. But it is the detail that changes. Ergo, Apache Spark uses DStreams, while structured streaming uses DataFrames to process these streams of data pouring into the analytics engine.
What is partition in Spark RDD?
A partition in spark is an atomic chunk of data (logical division of data) stored on a node in the cluster. Partitions are basic units of parallelism in Apache Spark. RDDs in Apache Spark are collection of partitions.
What is DStream and RDD?
A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous sequence of RDDs (of the same type) representing a continuous stream of data (see spark. RDD for more details on RDDs).
Which of the following is a DStream?
DStream is a continuous stream of data. It receives input from various sources like Kafka, Flume, Kinesis, or TCP sockets. It can also be a data stream generated by transforming the input stream. At its core, DStream is a continuous stream of RDD (Spark abstraction).
Why do we need SparkContext?
SparkContext is the primary point of entry for Spark capabilities. A SparkContext represents a Spark cluster’s connection that is useful in building RDDs, accumulators, and broadcast variables on the cluster. It enables your Spark Application to connect to the Spark Cluster using Resource Manager.
What is checkpointing in structured Streaming?
A checkpoint helps build fault-tolerant and resilient Spark applications. In Spark Structured Streaming, it maintains intermediate state on HDFS compatible file systems to recover from failures. To specify the checkpoint in a streaming query, we use the checkpointLocation parameter.
Does Apache Spark provide checkpointing?
Types of Checkpointing in Apache Spark There are two types of Apache Spark checkpointing: Reliable Checkpointing – It refers to that checkpointing in which the actual RDD is saved in reliable distributed file system, e.g. HDFS. To set the checkpoint directory call: SparkContext. setCheckpointDir(directory: String).
Is Spark Streaming obsolete?
Now that the Direct API of Spark Streaming (we currently have version 2.3. 2) is deprecated and we recently added the Confluent platform (comes with Kafka 2.2. 0) to our project we plan to migrate these applications.
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.
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 DStream and what is the difference between batch and DStream in Spark Streaming?
The input data stream is divided into the batches of data and then generates the final stream of the result in batches. Spark DStream (Discretized Stream) is the basic abstraction of Spark Streaming. DStream is a continuous stream of data.