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Apache Spark vs BlazingSQL: What are the differences?
Introduction:
Apache Spark and BlazingSQL are both powerful tools for data processing and analysis, but they have key differences that set them apart. Below are the main disparities between the two technologies.
1. Performance: Apache Spark is known for its in-memory processing capabilities, making it faster than traditional disk-based systems like Hadoop. On the other hand, BlazingSQL leverages GPU acceleration, enabling it to handle large datasets with significant speed improvements compared to CPU-based processing.
2. Data Source Compatibility: Apache Spark supports a wide range of data sources through its connectors, enabling users to work with various file formats and databases seamlessly. In contrast, BlazingSQL is primarily designed for working with GPU-accelerated data sources, making it a convenient choice for GPU-oriented workflows.
3. Ease of Use: Apache Spark's complex API and learning curve can be challenging for beginners, requiring a solid understanding of distributed computing concepts. In comparison, BlazingSQL offers a more SQL-focused interface, making it easier for SQL users to transition to GPU-accelerated computing without extensive retraining.
4. Scalability: Spark is well-known for its scalability, allowing users to process massive datasets across clusters of machines efficiently. While BlazingSQL can also scale effectively with the use of GPUs, it may have limitations in scalability when compared to Spark in certain distributed computing scenarios.
5. Community Support: Apache Spark has a robust community with a wealth of resources, forums, and libraries available for users to leverage. In contrast, BlazingSQL's community is relatively newer and may have a smaller user base, which can impact the availability of support and resources for users encountering issues.
6. Ecosystem Integration: While both Apache Spark and BlazingSQL can integrate with various data processing and visualization tools, Spark's mature ecosystem and integration capabilities with other big data technologies like Hadoop and Kafka give it an edge in the broader data ecosystem. On the other hand, BlazingSQL's integration options may be more limited in comparison.
In Summary, Apache Spark and BlazingSQL differ in terms of performance, data source compatibility, ease of use, scalability, community support, and ecosystem integration, offering users distinct advantages and considerations based on their specific data processing needs.
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 BlazingSQL
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 BlazingSQL
Cons of Apache Spark
- Speed4
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What is BlazingSQL?
What is Apache Spark?
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What companies use Apache Spark?
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