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Amazon Redshift Spectrum vs Apache Spark vs Presto: What are the differences?
# Introduction
This Markdown code provides a comparison between Amazon Redshift Spectrum, Apache Spark, and Presto in terms of specific key differences.
1. **Architecture**: Amazon Redshift Spectrum is an extension of Amazon Redshift, enabling users to query data directly in Amazon S3 without the need for data movement. Apache Spark is a distributed computing framework that operates on top of Hadoop. Presto, on the other hand, is a distributed SQL query engine for querying diverse data sources.
2. **Data Processing**: Amazon Redshift Spectrum allows users to run queries against data stored in Amazon S3 using the same SQL syntax as Amazon Redshift. Apache Spark offers in-memory processing for improved performance and supports various data sources and formats. Presto is designed for high-speed interactive queries against data sources like Hadoop, S3, and relational databases.
3. **Data Sources**: Amazon Redshift Spectrum primarily works with data stored in Amazon S3, while Apache Spark can process data from various sources such as HDFS, Cassandra, HBase, and more. Presto supports connectivity to multiple data sources, including traditional databases, NoSQL databases, and cloud storage.
4. **Scalability**: Amazon Redshift Spectrum enables users to scale compute and storage independently, offering flexibility in managing big data workloads. Apache Spark is known for its scalability and fault tolerance, allowing it to handle large-scale data processing tasks efficiently. Presto provides horizontal scalability by adding more nodes to the cluster for increased performance.
5. **Performance**: Amazon Redshift Spectrum leverages Redshift's columnar storage and parallel processing capabilities for fast query performance on large datasets. Apache Spark offers in-memory processing and caching to improve performance for iterative algorithms and interactive queries. Presto is optimized for interactive queries, providing low-latency responses even on massive datasets.
6. **Community Support**: Amazon Redshift Spectrum is fully managed by Amazon Web Services with comprehensive documentation and support. Apache Spark and Presto both have strong open-source communities backing them, providing frequent updates, plugins, and support resources for users.
In Summary, this comparison highlights the key differences between Amazon Redshift Spectrum, Apache Spark, and Presto in terms of architecture, data processing, data sources, scalability, performance, and community support.
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 Amazon Redshift Spectrum
- Good Performance1
- Great Documentation1
- Economical1
Pros of Presto
- Works directly on files in s3 (no ETL)18
- Open-source13
- Join multiple databases12
- Scalable10
- Gets ready in minutes7
- MPP6
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 Amazon Redshift Spectrum
Cons of Presto
Cons of Apache Spark
- Speed4
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What is Amazon Redshift Spectrum?
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