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Apache Spark vs Memcached: What are the differences?
Introduction:
Apache Spark and Memcached are both widely used in the field of big data and distributed computing. However, they serve different purposes and have distinct features that set them apart from each other.
1. Scalability: Apache Spark is designed for processing large-scale data analytics workloads, enabling parallel processing on a distributed computing cluster. On the other hand, Memcached is primarily used for caching frequently accessed data in memory to improve the overall performance of web applications.
2. Programming Paradigm: Apache Spark supports multiple programming languages such as Java, Scala, Python, and R, making it more flexible for developers with diverse coding backgrounds. In contrast, Memcached is a simple key-value store with a limited set of commands and data types, designed for fast data retrieval.
3. Fault Tolerance: Apache Spark inherently provides fault tolerance by storing intermediate data in resilient distributed datasets (RDDs), enabling fault recovery in case of node failures during computation. This ensures reliable data processing across distributed systems. Conversely, Memcached does not have built-in fault tolerance mechanisms and may require additional configurations for data redundancy.
4. Data Processing: Apache Spark is equipped with powerful libraries for machine learning (MLlib), stream processing (Spark Streaming), graph processing (GraphX), and SQL queries (Spark SQL), making it a versatile tool for various data processing tasks. On the other hand, Memcached focuses on caching data in memory and does not provide extensive data processing capabilities beyond key-value storage.
5. Storage Management: Apache Spark can leverage various storage options such as in-memory processing, disk-based persistence, and external storage systems like Hadoop HDFS, enabling efficient data storage and retrieval. In contrast, Memcached relies solely on memory caching and does not support persistent storage, limiting its capacity for long-term data retention.
6. Use Cases: Apache Spark is commonly used for big data analytics, real-time processing, machine learning, and interactive querying in data-driven applications. On the other hand, Memcached is often utilized in web applications for caching database queries, session storage, and content delivery networks to improve performance and scalability.
In Summary, Apache Spark is a versatile distributed computing framework with advanced data processing capabilities, while Memcached is a high-performance caching system designed for improving web application performance through in-memory data storage.
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 Memcached
- Fast object cache139
- High-performance129
- Stable91
- Mature65
- Distributed caching system33
- Improved response time and throughput11
- Great for caching HTML3
- Putta2
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 Memcached
- Only caches simple types2
Cons of Apache Spark
- Speed4
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