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Ambari vs Apache Spark: What are the differences?
Introduction
Apache Ambari and Apache Spark are two popular open-source software used in big data processing and management. While both serve different purposes, they play a significant role in the big data ecosystem. This article aims to highlight the key differences between Ambari and Spark.
Deployment and Management: Ambari is primarily used for cluster management and deployment of Apache Hadoop-based ecosystems. It provides a web-based interface to monitor, provision, and manage Hadoop clusters. On the other hand, Apache Spark focuses on distributed computing and processing large-scale datasets. It offers an easy-to-use API for data manipulation and analysis, but it does not provide cluster management capabilities like Ambari.
Ecosystem Support: Ambari is designed to manage the complete Hadoop ecosystem, including Hadoop Distributed File System (HDFS), Apache Hive, Apache HBase, and more. It provides a unified platform for managing and monitoring various components of the Hadoop stack. Conversely, Apache Spark is a standalone processing engine that can be integrated with different data processing frameworks like Hadoop, Cassandra, and more. It can leverage the functionalities of these ecosystems but does not have built-in management capabilities for them.
Processing Framework: Ambari focuses on managing and monitoring data processing activities across the Hadoop ecosystem. It provides tools for managing data ingestion, batch processing, and stream processing tasks. Apache Spark, on the other hand, is a powerful and fast distributed computing system that specializes in processing large-scale data. It provides a unified processing framework that supports batch processing, interactive queries, machine learning, and real-time streaming.
Language Support: Ambari is primarily used through its web-based user interface, making it language-independent. Users can interact with Ambari using any web browser. On the contrary, Apache Spark supports multiple programming languages, including Java, Scala, Python, and R. This language flexibility allows developers to choose the language they are most comfortable with for writing Spark applications.
Execution Model: Ambari provides a centralized management platform that coordinates the execution of tasks across the Hadoop ecosystem. It ensures that jobs are distributed and executed efficiently across the cluster. Apache Spark, on the other hand, follows a distributed computing model called Resilient Distributed Datasets (RDDs). RDDs enable fault tolerance and parallel processing by dividing the data into smaller partitions and processing them in parallel across a cluster of machines.
Real-time Processing: Apache Spark has built-in support for real-time streaming data processing. It provides a high-level streaming API that allows developers to process and analyze streaming data in near real-time. Ambari, on the other hand, primarily focuses on batch processing and does not have native support for real-time stream processing.
In summary, Ambari is a cluster management tool for Apache Hadoop-based ecosystems, providing deployment, monitoring, and management capabilities. Apache Spark, on the other hand, is a powerful distributed computing engine that supports batch processing, interactive queries, machine learning, and real-time streaming.
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 Ambari
- Ease of use2
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 Ambari
Cons of Apache Spark
- Speed4
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What is Ambari?
What is Apache Spark?
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