A Gentle Introduction to Spark Streaming

One of the core components of the Big Data ecosystem is Spark Streaming. Big Data management is done using a software framework created by the Apache Spark Foundation. In short, it ingests data in real-time from Twitter and other sources, analyses it using functions and algorithms, and then pushes it out to be stored in databases and other locations.

 

Spark is rated as a very quick engine for processing large amounts of data and is discovered to be 100 times quicker than MapReduce. It is so because it makes use of distributed data processing, which divides the data into smaller chunks so that it may be calculated in parallel across the workstations, saving time. Additionally, it speeds up the calculation by using in-memory processing rather than disk-based processing.

 

60% of all sensory data, according to IBM, is worthless if nothing is done with it within a few milliseconds. The inability to access real-time information will result in a loss of billions of dollars given that the market for big data and analytics has reached $125 billion and that a sizable portion of this market will be ascribed to IoT in the future.

 

These applications can be used by a telco to determine how many of its users have used WhatsApp in the last 30 minutes, a retailer to count the number of people who have today posted positive comments about their products on social media, or a law enforcement agency to locate a suspect using information from traffic CCTV.

 

What is Spark Streaming?

 

The streaming engine used by Spark before was called Spark Streaming. Spark Streaming is a historic project that is no longer updated. Structured Streaming is a more recent and user-friendly streaming engine in Spark. For your streaming apps and pipelines, you have to use Spark Structured Streaming.

 

An infinite series of data that arrive constantly is referred to as a data stream. For additional processing, streaming separates continually flowing input data into discrete pieces. Low latency processing and analysis of streaming data are known as stream processing. 

 

Since its addition to Apache Spark in 2013, Spark Streaming has offered scalable, high-throughput, and fault-tolerant stream processing of real-time data streams.

 

Kafka, Apache Flume, Amazon Kinesis, or TCP sockets are just a few examples of the various sources from which data may be ingested. Complex algorithms can be processed using high-level functions like map, reduce, join, and window. Finally, it is possible to push processed data to databases, filesystems, and real-time dashboards.

 

Due to their high latency, batch processing systems like Apache Hadoop are not suitable for requirements requiring processing to be done in a timely manner. Storm ensures that a record will be processed if it hasn't been, however, this might cause inconsistencies as record processing may be repeated. 

 

If a Storm-running node fails, the state is lost. A storm is typically used for stream processing instead of Hadoop's batch processing, which increases code size, the amount of defects that must be fixed, development work, presents a learning curve, and causes other problems. This is what distinguishes Apache Spark from Hadoop for big data.

 

Spark Streaming offers a scalable, effective, robust, and integrated (with batch processing) approach that aids in resolving these problems. In order to serve both batch and streaming workloads, Spark has created a single engine. For batch and streaming, Spark's unified Spark programming paradigm and single execution engine provide certain distinct advantages over other conventional streaming systems.

 

The fusion of various data processing skills is a major factor in Spark Streaming's quick acceptance. As a result, using a single framework to address all processing requirements is relatively simple for developers. Additionally, a very broad range of static data sources is accessible using Apache Spark SQL, allowing data from streaming sources to be combined with them.

 

Also Read | What is Data Ingestion?

 

Spark Streaming Architecture:

 

Spark Streaming discretizes the streaming data into tiny, sub-second micro-batches rather than processing each record one at a time. Therefore, data is simultaneously accepted by Spark Streaming receivers, and it is then buffered in the memory of Spark workers nodes. Then, to process the batches and output the outcomes to other systems, the latency-optimized Spark engine executes brief jobs.

 

In contrast to the conventional continuous operator paradigm, where the computation is explicitly allocated to a node, Spark jobs are distributed among the workers dynamically according to the proximity of the data and the resources that are available. Better load balancing and quicker fault recovery are made possible by this. 

 

The fundamental abstraction of a fault-tolerant dataset in Spark is the Resilient Distributed Dataset (RDD), and each batch of data is an RDD. This makes it possible to process the streaming data using any Spark code or module.

 

It can be difficult to process one data stream at a time, thus Spark Streaming discretizes the data into manageable little sub-batches. This is due to the concurrent data buffers that Spark workers get from the Spark Streaming receiver. As a consequence, the entire system processes the batches concurrently before compiling the final results. The Spark engine will then perform these quick jobs in batches, and other systems will get the results.

 

The processing in the Spark Streaming architecture is dependent on the location of the data and the availability of the resources rather than being statically allocated and loaded to a node. As a result, loading times are getting shorter as compared to earlier conventional systems. The adoption of the data locality concept makes it simpler to identify faults and repair them.

 

Working of Spark Streaming:

 

The streams of live input data are split up into batches via Spark streaming. In order to create the final stream batches, Spark Engine is employed to process these batches. The Apache Spark Discretized Stream represents the stream's data, which is broken up into discrete chunks (Spark DStream). 

 

The fundamental data abstraction of Spark is the RDD, which is used to create DStreams. Spark Streaming may be readily connected with any Apache Spark component, including Spark SQL and Spark MLib.

 

Scaling the live data streams is made possible via Spark Streaming. It is one of the main Spark API extensions. Additionally, it allows for high-throughput and fault-tolerant stream processing. Real-time processing and live data streaming are accomplished through the usage of Spark Streaming. 

 

Major global corporations like Pinterest, Netflix, and Uber use the Spark Streaming service. Additionally, real-time data analysis is offered through Spark Streaming. Data processing is done quickly and lives on the Spark Streaming platform as a whole.

 

Also Read: A Guide to Application Programming Interface (API)

 

Goals of Spark Streaming:

 

The following objectives are possible for Spark Streaming thanks to this architecture:

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Goals of Spark Streaming

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1. Dynamic load balancing:

 

The data is split up into tiny micro-batches, enabling precise resource allocation for computations. Let's take a look at a straightforward workload where processing requires splitting the incoming data stream using a key. 

 

If one of the partitions is more computationally intensive than the others in the conventional record-at-a-time method, the node to which that partition is assigned will turn into a bottleneck and slow down the pipeline. 

 

The workload of the job will automatically be distributed across the workers, with some processing a few larger tasks while others processing a greater number of the shorter jobs under Spark Streaming.

 

2. Quickly failing and recovering slowly:

 

When a node fails, conventional systems must restart the unsuccessful operator on a different node in order to recompute the lost data. The pipeline cannot move forward until the new node has caught up after the replay since there is only one node handling the recomputation. 

 

Spark discretizes computing into manageable jobs that can be executed anywhere without compromising accuracy. In order to complete the recomputation and recover from the failure faster than with the conventional strategy, we can divide failed jobs equally across all the other nodes in the cluster.

 

3. Integration for batch, streaming, and interactive analytics:

 

A DStream in Spark is simply a collection of RDDs that enables batch and streaming workloads to coexist in harmony. Each batch of streaming data can have any number of Apache Spark functions applied to it. The streams of streaming data may be interactively accessed since they are kept in the worker memory of Spark.

 

4. Use of interactive SQL and advanced analytics:

 

MLlib (machine learning), SQL, DataFrames, and GraphX are just a few of the extensive libraries that are compatible with Spark. RDDs produced by DStreams may be transformed into DataFrames and queried using SQL. MLlib may be used to create machine learning models that can be applied to streaming data.

 

5. Efficiency:

 

The ability of Spark Streaming to batch data and use the Spark engine results in almost greater throughput than other streaming systems. With Spark Streaming, latencies as low as a few hundred milliseconds may be achieved.

 

Advantages of Spark Streaming:

 

A large number of programming languages are supported by Spark. Spark is hence very adaptable, allowing programmers from a wide range of backgrounds to utilize it. Java, Python, R, Scala, and SQL are supported by Spark.

 

Spark can do a variety of tasks, from streaming to machine learning to data management, thanks to its satellite code libraries. It is quite easy to scale Spark. Spark users can operate on a single laptop, a small business network, or a vast network of computer clusters spanning many nations.

 

Additional benefits include:

 

• Spark processes massive amounts of data quickly.

• It is compatible with many different computer jobs.

• Spark accesses Hadoop (properly, Apache Hadoop) systems to read and write data.

• Compared to MapReduce, Spark is more effective.

• When performing sophisticated calculations on data sources that are at rest, Spark outperforms MapReduce.

• Many believe that Spark is the obvious replacement for MapReduce.

• Hadoop is up to 40 times slower than Spark.

• Data from several sources can be consumed by Spark.

• Spark has the ability to send findings to live dashboards, file systems, and databases. Even more output locations can be set up by users.

 

Discrete streams (DStreams) are used by Spark Streaming to create fault-tolerant streaming. DStreams has a better track record than conventional replication and backup techniques for fault recovery. Stragglers are accepted by DStream. 

 

The system can handle streaming data constantly thanks to Spark Structured Streaming, which employs a continuous streaming technique. Due to the fact that Structured Streaming waits for all the data to come before altering the result, it avoids several issues with managing errors and stragglers.

 

The RDD (Resilient Distributed Dataset) pieces that makeup Spark Streaming's DStreams are organized sequentially. DStream analysis and stream processing are slower than its DataFrames rival while being fault-tolerant. It implies that, as compared to Dataframes, DStreams are less trustworthy in delivering messages.

 

DataFrames are used by Structured Streaming to handle and analyze data. Because it was created using the Apache Spark engine, it is capable of doing this. For experienced software engineers who have lots of experience dealing with distributed systems, Spark Structured Streaming is the superior interface.

 

A potent tool for creating streaming applications that work with massive data is Apache Spark Streaming. The dominant big data streaming processor, Spark Streaming, is on track to supplant Map Reduce rather soon. The focus on Spark Streaming is perhaps preferable for data scientists and developers who have the option to work with either.

 

Users of the Spark engine have a choice between the Spark Streaming and Spark Structured Streaming streaming-processing models. Spark Streaming can have trouble with stragglers even if it is fault-tolerant. However, Spark Structured Streaming is also robust.

 

In addition, stragglers are easily handled because of the manner it is built. Even better, you can simply integrate the task with your other data processing apps utilizing Structured Streaming with just a few small changes to the code. The disadvantage is that working with Spark Structured Streaming is more difficult than working with Spark Streaming.

 

Also Read | A Gentle Introduction to PySpark

 

Conclusion:

 

Spark streaming, therefore, resolves every drawback of conventional streaming solutions. We can now simultaneously process batch and streaming workloads. 

 

Therefore, using a single framework to handle all processing requirements has grown popular among developers. So, using streaming also improves system performance and efficiency.