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Apache Impala vs Apache Spark: What are the differences?
Introduction: In this article, we will explore the key differences between Apache Impala and Apache Spark, two popular open-source big data processing frameworks.
Data Processing Model: One major difference between Impala and Spark is their data processing model. Impala is designed for interactive SQL queries and provides real-time, low-latency querying capabilities on large datasets stored in Hadoop Distributed File System (HDFS). On the other hand, Spark offers a more flexible and general-purpose data processing model, supporting various data manipulation tasks like batch processing, streaming, machine learning, and graph processing.
Data Storage: Another significant difference lies in their data storage options. Impala relies on HDFS for storing data, while Spark can work with various data sources, including HDFS, Apache Parquet, Apache Avro, Apache Cassandra, and more. This versatility allows Spark to integrate with a wide range of data storage technologies, making it suitable for diverse use cases.
Query Optimization: Impala and Spark employ different query optimization techniques. Impala uses a cost-based optimizer that evaluates various query execution plans and selects the most efficient one based on statistics and heuristics. Spark, on the other hand, utilizes a rule-based optimizer that applies a set of predefined rules to optimize query plans. While both approaches have their strengths, Impala's cost-based optimization can often lead to faster query execution.
Concurrency and Scalability: When it comes to handling concurrent queries and scaling to larger datasets, the two frameworks differ in their approach. Impala is built to support high concurrency, allowing multiple users to execute queries simultaneously. It achieves this through an MPP (Massively Parallel Processing) architecture that leverages distributed computing resources. Meanwhile, Spark uses a shared-nothing architecture that partitions the data across a cluster and processes it in parallel. This design enables Spark to scale horizontally by adding more nodes to the cluster.
Language Support: Impala mainly supports SQL for querying and processing data, making it suitable for users familiar with SQL-based analytics. On the other hand, Spark provides support for multiple programming languages, such as Scala, Java, Python, and R. This broader language support makes Spark more flexible, allowing developers to choose the language they are most comfortable with for data processing tasks.
Familiarity with Apache Hadoop: Another key difference is the level of familiarity required with Apache Hadoop. Impala is tightly integrated with the Hadoop ecosystem and relies on Hadoop services like HDFS and Hive for metadata management. Therefore, users familiar with Hadoop can leverage their existing knowledge to work with Impala. In contrast, while Spark can also integrate with Hadoop, it can be used as a standalone framework as well, reducing the dependency on Hadoop-specific components.
In Summary, Apache Impala is optimized for interactive SQL querying with a focus on low-latency, real-time performance and tight integration with the Hadoop ecosystem. In contrast, Apache Spark offers a more versatile and general-purpose data processing framework, supporting various data manipulation tasks, multiple data sources, and programming languages, with a focus on scalability and flexibility in distributed computing.
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 Apache Impala
- Super fast11
- Massively Parallel Processing1
- Load Balancing1
- Replication1
- Scalability1
- Distributed1
- High Performance1
- Open Sourse1
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 Apache Impala
Cons of Apache Spark
- Speed4
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