Do Spark Streaming and Spark Structured Streaming use the same micro-batch scheduler engine? Does Spark Structured Streaming have lower latency than Spark Streaming?
Do Spark Streaming and Spark Structured Streaming use same micro-batch scheduler engine
Certainly not. They're different internally, but share the same high-level concepts of a stream and a record.
While in Spark Structured Streaming you can get as close to how it was in Spark Streaming using DataStreamWriter.foreach or DataStreamWriter.foreachBatch methods.
The main difference is how to describe a streaming pipeline. In Spark Structured Streaming you use Spark SQL's Dataset API while Spark Streaming bet on Spark Core's RDD API. Both end up as a RDD-based computation, but Spark SQL uses higher-level abstractions (e.g. Dataset API).
Do they both use a "micro-batch scheduler engine"? Yes, but Spark Structured Streaming is trying to leverage some data sources that can be queried continuously (and no micro-batching).
does Spark Structured Streaming have lower latency than Spark Streaming?
That'd be hard to answer. The creators of Spark Streaming decided to develop Spark Structured Streaming and hope to get better at query performance and expressiveness. Spark Streaming is no longer recommended.
Structered Streaming is mostly a higher-level abstraction that allows you to define your streaming logic then it uses Spark SQL engine for execution on the same micro-batch engine.
By default Structured Streaming uses micro-batch engine, however if you are using Spark 2.3+, then you can have the continuous mode where you can get down to 1 millisecond latency
Related
When will Spark Structured Streaming's Continuous Mode be no longer experimental?
I am looking at processing CDC feeds via KAFKA, but note questions about this mode showing data loss from KAFKA.
The last months I've been using quite a lot Structured Streaming for implementing Stream Jobs (after using Kafka a lot). After reading the book Stream Processing with Apache Spark i was having this question: Is there any point or use cases where i would use Spark Streaming instead of Structured Streaming? Should i invest some time getting into it or since im already using Spark Structured Streaming i should stick with it and there is no benefit on the previous API.
Would appreciate any opinion/insight
Hi Sharing my personal experience.
Structured streaming is the future for spark based streaming implementation. It provides higher level of abstraction and other great features. However there are few restrictions.
i have had to switch to spark streaming on few occasions due to the flexibility offered by it. One recent example is, we had to perform Joins with static reference data, however Outer joins are not supported in Structured streaming. This can be accomplished with Spark streaming.
With the newer spark version 2.4, Structured streaming is much improved with support for foreachBatch sink which gives similar flexibility offered by spark streaming.
My personal thought is having the knowledge of spark streaming is helpful and you might have to use it depending on your use case.
I have come across three popular streaming techniques that are Spark Streaming, Structured Streaming and Kafka Streaming.
I have gone through various sites but not getting this answer, are these three the same thing or different?
If not same what is the basic difference.
I am not looking for an in depth answer. But an answer to above question (yes or no) and a little intro to each of them so that I can explore more. :)
Thanks in advance
Subrat
I guess you are referring to Kafka Streams when you say "Kafka Streaming".
Kafka Streams is a JVM library, part of Apache Kafka. It is a way of processing data in Kafka topics providing an abstraction layer. Applications running KafkaStreams library can be run anywhere (not just in the Kafka cluster, actually, it is not recommended to). They'll consume, process and produce data to/from the Kafka cluster.
Spark Streaming is a part of Apache Spark distributed data processing library, that provides Stream (as oppposed to batch) processing. Spark initially provided batch computation only, so a specific layer Spark Streaming was provided for stream processing. Spark Streaming can be fed with Kafka data, but it can be connected to other sources as well.
Structured Streaming, within the realm of Apache Spark, is a different approach that came to overcome certain limitations to stream processing of the previous approach that Spark Streaming was using. It was added to Spark from a certain version onwards(2.0 IIRC).
I have a Spark Structured Streaming application which receives Kafka messages. For each such message it retrieves initial data from DB and performs calculations. I want to use GraphX (or GraphFrame) to build graph for each message and perform calculations. I understand how to do it with simple batch job, but how to use GraphX with Spark Streaming?
We have discussed the questions below:
What is the difference between Apache Spark and Apache Flink? [closed]
What does “streaming” mean in Apache Spark and Apache Flink?
What is the difference between mini-batch vs real time streaming in practice (not theory)?
But Spark Structured Streaming was added at Spark2.2, it brings a lot of changes for streaming, and it is outstanding.
Can we say Spark Strutured Streaming is a streaming processing, or still batch processing?
Now what is the big difference between Apache Flink and Apache Spark Structured Streaming?
Currently:
Spark Structured Streaming has still microbatches used in background. However, it supports event-time processing, quite low latency (but not as low as Flink), supports SQL and type-safe queries on the streams in one API; no distinction, every Dataset can be queried both with SQL or with typesafe operators. It has end-to-end exactly-one semantics (at least they says it ;) ). The throughput is better than in Flink (there were some benchmarks with different results, but look at Databricks post about the results).
In near future:
Spark Continous Processing Mode is in progress and it will give Spark ~1ms latency, comparable to those from Flink. However, as I said, it's still in progress. The API is ready for non-batch jobs, so it's easier to do than in previous Spark Streaming.
The main difference:
Spark relies on micro-batching now and Flink is has pre-scheduled operators. That means, Flink's latency is lower, but Spark Community works on Continous Processing Mode, which will work similar (as far as I understand) to receivers.