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Apache Hive vs Apache Spark: What are the differences?
Introduction
This Markdown code provides a comparison between Apache Hive and Apache Spark, highlighting their key differences.
Data Processing: Apache Hive is a data warehouse infrastructure built on top of Hadoop for querying and analyzing large datasets stored in distributed storage, using a SQL-like language called HiveQL. On the other hand, Apache Spark is a general-purpose cluster computing system that provides in-memory processing capabilities, supporting various programming languages for data processing and performing tasks like batch processing, real-time streaming, and machine learning.
Processing Speed: While Apache Hive runs on top of Hadoop MapReduce, which is known for its slow processing speed due to disk-based data processing, Apache Spark utilizes in-memory processing, resulting in significantly faster data processing. This difference in processing speed makes Apache Spark more suitable for real-time and interactive analysis.
Ease of Use: Apache Hive focuses on providing a user-friendly abstraction layer for data analysts familiar with SQL, making it easier to process and analyze large datasets using existing SQL knowledge. Apache Spark, on the other hand, requires a deeper understanding of programming concepts and offers greater flexibility for developers to design their data processing pipelines using various APIs and programming languages like Scala, Java, Python, and R.
Iterative Processing: Apache Spark provides support for iterative processing, which is essential for machine learning algorithms and graph analytics. This capability enables Spark to cache data in memory and reuse it across multiple iterations, resulting in faster performance for iterative workloads. Apache Hive, being a batch processing framework, lacks this ability for efficient iterative processing.
Real-time Streaming: While Apache Spark comes with built-in support for real-time streaming data processing through its streaming API, Apache Hive is primarily designed for batch processing and lacks native support for real-time streaming. Apache Hive can still be used for real-time analysis by integrating with other tools like Apache Kafka or Apache Storm.
Community Ecosystem: Apache Spark has gained significant popularity in recent years and has a vibrant and active community contributing various libraries, tools, and frameworks around it. This makes it easier for developers to find support, documentation, and reusable components for their data processing needs. Although Apache Hive also has a strong community, it may not match the extensive ecosystem surrounding Apache Spark.
In summary, Apache Hive is a SQL-based data warehouse infrastructure for batch processing and querying large datasets, whereas Apache Spark is a general-purpose cluster computing system with in-memory processing capabilities, supporting real-time and iterative processing, and offering a more diverse programming model. Apache Spark outperforms Apache Hive in terms of speed, flexibility, and real-time analysis capabilities, while Apache Hive excels in its user-friendly SQL interface and compatibility with existing SQL knowledge.
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 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 Hive
Cons of Apache Spark
- Speed4
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What is Apache Hive?
What is Apache Spark?
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