In summary, enabling backpressure is an important technique to make your spark streaming application production ready. It dynamically set the message ingestion rate based on previous batch performance, thus making your spark streaming application stable and efficient, without the pitfall of statically capped max rate.

What is back pressure in Spark?

Backpressure refers to the situation where a system is receiving data at a higher rate than it can process during a temporary load spike. If there is a sudden spike in traffic, this could cause bottlenecks in downstream dependencies, that slows down the stream processing.

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 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.

Is Spark Streaming real-time?

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.

Understanding Akka Streams, Back Pressure and Asynchronous Architecture

What is the difference between Spark and Spark Streaming?

Generally, Spark streaming is used for real time processing. But it is an older or rather you can say original, RDD based Spark structured streaming is the newer, highly optimized API for Spark. Users are advised to use the newer Spark structured streaming API for Spark.

What is ETL in Spark?

ETL refers to the transfer and transformation of data from one system to another using data pipelines. Data is extracted from a source, or multiple sources, often to move it to a unified platform such as a data lake or a data warehouse to deliver analytics and business intelligence.

What is checkpoint in Databricks?

Azure Databricks uses the checkpoint directory to ensure correct and consistent progress information. When a stream is shut down, either purposely or accidentally, the checkpoint directory allows Azure Databricks to restart and pick up exactly where it left off.

What is batch interval in Spark Streaming?

A batch interval tells spark that for what duration you have to fetch the data, like if its 1 minute, it would fetch the data for the last 1 minute. source: spark.apache.org. So the data would start pouring in a stream in batches, this continuous stream of data is called DStream.

What is sliding window in Spark?

Sliding Window controls transmission of data packets between various computer networks. Spark Streaming library provides windowed computations where the transformations on RDDs are applied over a sliding window of data.

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).

How do I improve my Spark application performance?

Apache Spark Performance Boosting

1. 1 — Join by broadcast. ...
2. 2 — Replace Joins & Aggregations with Windows. ...
3. 3 — Minimize Shuffles. ...
4. 4 — Cache Properly. ...
5. 5 — Break the Lineage — Checkpointing. ...
6. 6 — Avoid using UDFs. ...
7. 7 — Tackle with Skew Data — salting & repartition. ...
8. 8 — Utilize Proper File Formats — Parquet.

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.

What causes back pressure?

A common example of backpressure is that caused by the exhaust system (consisting of the exhaust manifold, catalytic converter, muffler and connecting pipes) of an automotive four-stroke engine, which has a negative effect on engine efficiency, resulting in a decrease of power output that must be compensated by ...

What is backpressure in Kafka?

Backpressure in Kafka Consumers

This pull-based mechanism of consuming allows the consumer to stop requesting new records when the application or downstream components are overwhelmed with load.

How do you handle failures in spark streaming?

If the system fails in the middle of applying the operation, it can recover by reading the log and reapplying the operations it had intended to do. Let us see how we use this concept to ensure the durability of the received data. Sources like Kafka and Flume use Receivers to receive data.

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 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.

What is Micro batching in Spark?

Micro batching is defined as the procedure in which the incoming stream of messages is processed by dividing them into group of small batches. This helps to achieve the performance benefits of batch processing; however, at the same time, it helps to keep the latency of processing of each message minimal.

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 checkpointing in structured streaming?

In Structured Streaming, if you enable checkpointing for a streaming query, then you can restart the query after a failure and the restarted query will continue where the failed one left off, while ensuring fault tolerance and data consistency guarantees.

What is the difference between persist and cache in Spark?

Both caching and persisting are used to save the Spark RDD, Dataframe, and Dataset's. But, the difference is, RDD cache() method default saves it to memory (MEMORY_ONLY) whereas persist() method is used to store it to the user-defined storage level.

What's the difference between ETL and ELT?

ETL is a time-intensive process; data is transformed before loading into a destination system. ELT is faster by comparison; data is loaded directly into a destination system, and transformed in-parallel.

Is PySpark good for ETL?

There are many ETL tools available in the market that can carry out this process. A standard ETL tool like PySpark, supports all basic data transformation features like sorting, mapping, joins, operations, etc. PySpark's ability to rapidly process massive amounts of data is a key advantage.

What is pipeline Spark?

A Pipeline is specified as a sequence of stages, and each stage is either a Transformer or an Estimator . These stages are run in order, and the input DataFrame is transformed as it passes through each stage. For Transformer stages, the transform() method is called on the DataFrame .