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Apache Kylin vs Impala vs Presto: What are the differences?
<Apache Kylin vs Impala and Presto Comparison>
1. **Storage Compatibility**: Apache Kylin requires data to be stored in Apache Hadoop HDFS or cloud storage like S3, while Impala works directly with HDFS and HBase, and Presto can query data from various sources including HDFS, HBase, and relational databases.
2. **Query Performance**: Apache Kylin uses pre-built data cubes for accelerated querying, Impala offers real-time query performance due to its MPP architecture, and Presto excels in running ad-hoc queries efficiently.
3. **Data Processing Paradigm**: Apache Kylin utilizes OLAP cubes for fast query processing, Impala relies on in-memory processing for low-latency queries, and Presto follows a distributed SQL engine approach for query execution.
4. **Complex Query Support**: Apache Kylin is well-suited for complex queries requiring aggregation and filtering, Impala is suitable for interactive querying on large datasets, and Presto is ideal for complex join operations across different data sources.
5. **Ecosystem Integration**: Apache Kylin has robust integration with Hadoop ecosystem components like Hive and HBase, Impala integrates well with HDFS and HBase, and Presto can seamlessly connect with various data stores such as MySQL, Cassandra, and Kafka.
6. **Scalability and Concurrency**: Apache Kylin provides scalable querying using distributed cubes, Impala offers high concurrency with low latency due to its distributed architecture, and Presto boasts high scalability and concurrency for handling large workloads.
In Summary, Apache Kylin, Impala, and Presto differ in storage compatibility, query performance, data processing paradigm, complex query support, ecosystem integration, and scalability/concurrency features.
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.
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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 Kylin
- Star schema and snowflake schema support7
- Seamless BI integration5
- OLAP on Hadoop4
- Easy install3
- Sub-second latency on extreme large dataset3
- ANSI-SQL2
Pros of Apache Impala
- Super fast11
- Massively Parallel Processing1
- Load Balancing1
- Replication1
- Scalability1
- Distributed1
- High Performance1
- Open Sourse1
Pros of Presto
- Works directly on files in s3 (no ETL)18
- Open-source13
- Join multiple databases12
- Scalable10
- Gets ready in minutes7
- MPP6
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What is Apache Kylin?
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What is Presto?
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