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Apache Spark vs Redis: What are the differences?
Introduction
Apache Spark and Redis are two popular technologies used in the field of distributed computing. While both are designed to handle large-scale data processing, they have key differences in terms of their architecture, functionality, and use cases.
Architecture: Apache Spark is a distributed computing framework that runs on a cluster of computers, allowing for parallel processing and data storage across multiple nodes. Redis, on the other hand, is an in-memory data structure store that can be used as a database, cache, or message broker. It operates as a single server or a cluster of servers, with data stored primarily in memory for high-speed access.
Data Processing: Apache Spark is primarily designed for big data processing and analytics. It provides a high-level API for distributed data processing, making it easier to write complex data transformations and analytics algorithms. Redis, on the other hand, excels at rapid data access and manipulation. It provides a rich set of data types and operations, allowing for efficient handling of structured and unstructured data in real-time.
Scalability: Apache Spark is highly scalable and can handle large-scale data processing tasks by distributing the workload across multiple nodes in a cluster. It can efficiently process and analyze huge volumes of data in parallel. Redis, on the other hand, is designed for high-speed data access and can handle millions of concurrent operations. It can be scaled horizontally by adding more servers to the cluster to handle increasing data loads.
Persistence: Apache Spark can persist data to disk or external storage systems, allowing for fault tolerance and data recovery in case of failures. It can also cache intermediate results in memory for faster processing. Redis, on the other hand, primarily stores data in memory for high-speed access but can also persist data to disk for durability. It offers different persistence options, including snapshots and append-only files.
Data Manipulation: Apache Spark provides a wide range of data manipulation and analytics capabilities, including SQL queries, data streaming, machine learning, and graph processing. It allows for complex data transformations and analysis within a single framework. Redis, on the other hand, offers a rich set of data manipulation operations for structured and unstructured data, including sorting, set operations, pub/sub messaging, and geospatial support.
Use Cases: Apache Spark is commonly used for big data processing, analytics, and machine learning. It is suitable for batch processing, real-time streaming, and interactive data analysis. Redis, on the other hand, is widely used as a high-performance cache, message broker, and session store. It is commonly used in web applications, real-time analytics, and high-speed data processing.
In summary, Apache Spark is a distributed computing framework for big data processing and analytics, while Redis is an in-memory data structure store for rapid data access and manipulation. They differ in terms of their architecture, data processing capabilities, scalability, persistence options, data manipulation operations, and use cases.
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
Pros of Redis
- Performance886
- Super fast542
- Ease of use513
- In-memory cache444
- Advanced key-value cache324
- Open source194
- Easy to deploy182
- Stable164
- Free155
- Fast121
- High-Performance42
- High Availability40
- Data Structures35
- Very Scalable32
- Replication24
- Great community22
- Pub/Sub22
- "NoSQL" key-value data store19
- Hashes16
- Sets13
- Sorted Sets11
- NoSQL10
- Lists10
- Async replication9
- BSD licensed9
- Bitmaps8
- Integrates super easy with Sidekiq for Rails background8
- Keys with a limited time-to-live7
- Open Source7
- Lua scripting6
- Strings6
- Awesomeness for Free5
- Hyperloglogs5
- Transactions4
- Outstanding performance4
- Runs server side LUA4
- LRU eviction of keys4
- Feature Rich4
- Written in ANSI C4
- Networked4
- Data structure server3
- Performance & ease of use3
- Dont save data if no subscribers are found2
- Automatic failover2
- Easy to use2
- Temporarily kept on disk2
- Scalable2
- Existing Laravel Integration2
- Channels concept2
- Object [key/value] size each 500 MB2
- Simple2
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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Cons of Redis
- Cannot query objects directly15
- No secondary indexes for non-numeric data types3
- No WAL1
Cons of Apache Spark
- Speed4