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Apache Spark vs Kudu: What are the differences?
Introduction: Apache Spark and Kudu are both big data technologies used for processing and storing large amounts of data. However, they have some key differences in terms of their architecture and use cases.
Data Storage: Apache Spark is primarily an in-memory data processing engine that is used for batch and stream processing. It does not have built-in storage capabilities and relies on external storage systems like Hadoop Distributed File System (HDFS) or cloud storage. On the other hand, Kudu is a columnar storage engine that is designed for fast analytics on rapidly changing data. It provides a high-throughput, low-latency storage solution for structured data.
Data Model: Apache Spark is built around the concept of Resilient Distributed Datasets (RDDs) and DataFrames/Datasets. RDDs are fault-tolerant, immutable collections of records that can be operated on in parallel. DataFrames/Datasets are higher-level APIs that provide a more structured and efficient way of working with data. Kudu, on the other hand, stores data in tables with rows and columns, similar to traditional relational databases. It provides ACID-compliant transactions and supports complex queries and aggregations.
Use Cases: Apache Spark is well-suited for a wide range of data processing tasks, including ETL (extract, transform, load), machine learning, graph processing, and real-time analytics. It is highly scalable and can run on a cluster of machines. Kudu, on the other hand, is optimized for fast analytical queries on large volumes of data. It is ideal for applications that require low-latency access to data, such as real-time reporting, online analytical processing (OLAP), and time-series analysis.
Fault Tolerance: Apache Spark provides fault tolerance through lineage information and resilient distributed datasets (RDDs). If a partition of an RDD is lost, Spark can recompute it using the lineage information. Kudu, on the other hand, provides fault tolerance through data replication and distribution. Data stored in Kudu can be replicated across multiple nodes in a cluster to ensure high availability and reliability.
Consistency: Apache Spark offers eventual consistency, which means that updates to data may not be immediately reflected in all nodes of the cluster. This can lead to inconsistencies in the data while processing. On the contrary, Kudu provides strong consistency, ensuring that all reads and writes are immediately consistent across all nodes. This makes Kudu suitable for transactional workloads where data consistency is critical.
Data Processing Paradigm: Apache Spark follows a batch and stream processing paradigm and supports various processing engines like batch, streaming, SQL, machine learning, and graph processing. In contrast, Kudu is primarily focused on fast analytical queries and is more suitable for OLAP workloads that involve complex queries and aggregations on structured data.
In Summary, Apache Spark and Kudu differ in terms of data storage, data model, use cases, fault tolerance, consistency, and data processing paradigm, making them suitable for different types of big data processing tasks.
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 Kudu
- Realtime Analytics10
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 Kudu
- Restart time1
Cons of Apache Spark
- Speed4
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What is Apache Kudu?
What is Apache Spark?
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