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Presto vs Vespa: What are the differences?
Presto and Vespa are both powerful tools used in the field of data analytics and search engines respectively. Here are the key differences between Presto and Vespa:
1. **Architecture**: Presto is a distributed SQL query engine while Vespa is a scalable big data serving engine. Presto focuses on interactive query execution, whereas Vespa is designed for serving and indexing large-scale data.
2. **Use Case**: Presto is commonly used for ad-hoc queries and data mining tasks where SQL compatibility is important. On the other hand, Vespa is used for building advanced search applications like recommendation systems, content delivery, and personalized search.
3. **Data Storage**: Presto does not provide data storage capabilities but instead relies on external sources like HDFS, S3, or relational databases. Vespa, on the other hand, comes with its own storage solution optimized for search operations including document indexing and retrieval.
4. **Scalability**: Presto can scale horizontally by adding more worker nodes to handle increased query loads. Vespa is inherently designed for horizontal scalability to handle massive amounts of data and queries efficiently.
5. **Query Language**: Presto supports traditional SQL for querying structured data stored in various data sources. Vespa, on the other hand, uses a specialized query language specifically tailored for searching and ranking documents based on relevancy.
6. **Real-time Capabilities**: Vespa is built for real-time data serving and can handle dynamic content updates along with search queries in milliseconds. Presto, while fast for analytical queries, may not provide real-time capabilities necessary for certain use cases like personalized recommendations.
In Summary, Presto and Vespa differ in architecture, use cases, data storage, scalability, query language, and real-time capabilities. Each tool excels in its specific domain and serves distinct purposes in the realm of data analytics and search engines.
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 Presto
- Works directly on files in s3 (no ETL)18
- Open-source13
- Join multiple databases12
- Scalable10
- Gets ready in minutes7
- MPP6
Pros of Vespa
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