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Apache Flink vs Apache Hive: What are the differences?
Introduction
In this article, we will explore the key differences between Apache Flink and Apache Hive.
Data Processing Model: Apache Flink is a stream processing and batch processing framework that supports real-time and batch data processing. It provides a unified API for both stream and batch processing, enabling complex event-driven applications. On the other hand, Apache Hive is a data warehouse infrastructure that provides data summarization, query, and analysis. It primarily focuses on batch processing and interactive queries on large datasets.
Language and Query Support: Apache Flink supports multiple programming languages like Java, Scala, and Python. It provides a high-level API for data stream and batch processing. It also supports complex event processing and advanced analytics. In contrast, Apache Hive uses a language called HiveQL, which is similar to SQL. HiveQL allows users to write SQL-like queries to query and analyze data stored in Hive. It does not support complex event processing or advanced analytics.
Data Storage: Apache Flink is a processing engine that does not provide its own storage system. It can seamlessly integrate with various storage systems like Hadoop Distributed File System (HDFS), Amazon S3, and Apache Kafka. On the other hand, Apache Hive uses a data warehouse infrastructure built on top of Hadoop. It stores data in a distributed file system like HDFS and provides a metastore for metadata management.
Processing Speed: Apache Flink is known for its low-latency and high-throughput processing capabilities. It can process large volumes of data in real-time, making it suitable for stream processing use cases. On the contrary, Apache Hive is designed for batch processing and interactive queries. It may not have the same level of processing speed as Apache Flink for real-time processing scenarios.
Optimizations and Query Execution: Apache Flink's query optimization techniques focus on optimizing complex event processing and stream processing. It performs optimizations like operator fusion, lazy evaluation, and state management. In comparison, Apache Hive's query optimization techniques are geared towards improving batch processing and interactive queries. It performs optimizations like predicate pushdown, column pruning, and query compilation.
Ecosystem and Integrations: Apache Flink has a growing ecosystem and integrates well with popular data processing tools and frameworks like Apache Kafka, Apache Spark, and Apache Hadoop. It can leverage the capabilities of these tools to enhance its functionality. On the other hand, Apache Hive has been around for a longer time and has a mature ecosystem with support for various data formats, file systems, and storage systems.
In summary, the key differences between Apache Flink and Apache Hive lie in their data processing models, language and query support, data storage, processing speed, optimizations and query execution, and ecosystem and integrations. Apache Flink is more focused on real-time processing, provides a unified API, and has a growing ecosystem. Apache Hive is primarily designed for batch processing, uses HiveQL for queries, and has a mature ecosystem.
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"
To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.
Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.
We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.
Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.
Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.
#BigData #AWS #DataScience #DataEngineering
The platform deals with time series data from sensors aggregated against things( event data that originates at periodic intervals). We use Cassandra as our distributed database to store time series data. Aggregated data insights from Cassandra is delivered as web API for consumption from other applications. Presto as a distributed sql querying engine, can provide a faster execution time provided the queries are tuned for proper distribution across the cluster. Another objective that we had was to combine Cassandra table data with other business data from RDBMS or other big data systems where presto through its connector architecture would have opened up a whole lot of options for us.
Pros of Apache Hive
Pros of Apache Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
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