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PostgREST vs Presto: What are the differences?
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Adaptability to use cases: PostgREST is specifically designed to provide a REST API to access PostgreSQL databases, simplifying the process for developers to interact with the data. On the other hand, Presto is a distributed SQL query engine that allows querying data where it resides, making it more suitable for complex analytical queries across multiple data sources.
Query functionality: PostgREST translates HTTP requests into SQL queries to interact with PostgreSQL databases, enabling CRUD operations easily through REST API endpoints. In contrast, Presto focuses on executing fast distributed SQL queries on large datasets, enabling analytics queries across heterogeneous data sources with high performance.
Scalability: PostgREST is designed to work directly with PostgreSQL databases, limiting its scalability to the capabilities of the underlying database. Presto, being a distributed query engine, can scale horizontally across multiple nodes to process and analyze massive datasets efficiently.
Usage scenario: PostgREST is commonly used for building RESTful APIs on top of single PostgreSQL databases, providing a straightforward way to access and manipulate data. Presto, on the other hand, is favored for complex analytical queries that involve data from various sources, making it a suitable tool for business intelligence and data analytics tasks.
In Summary, PostgREST is a specialized tool for building REST APIs on PostgreSQL databases, while Presto excels in executing distributed SQL queries for analytical purposes across heterogeneous data sources.
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 PostgREST
- Fast, simple, powerful REST APIs from vanilla Postgres4
- JWT authentication2
- Very fast1
- Declarative role based security at the data layer1
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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