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Apache Hive vs Presto: What are the differences?
Apache Hive vs Presto: Key Differences
Apache Hive and Presto are both query engines used to process and analyze big data. However, there are several key differences between these two technologies.
Architecture: Apache Hive is built on top of Hadoop and is designed for batch processing. It uses a traditional two-step process where query compilation and execution are separate. On the other hand, Presto is built for interactive analysis and has a distributed architecture that allows it to query data from various data sources in real-time.
Performance: While both Hive and Presto are designed for querying large datasets, Presto is known for its high performance. Presto uses optimizations such as pipelining and vectorized execution, which enable it to process queries faster than Hive, especially for ad hoc and interactive queries.
Language Support: Hive uses a SQL-like query language called HiveQL, which is based on SQL but also includes extensions for querying structured and semi-structured data. Presto, on the other hand, supports ANSI SQL and has a more comprehensive SQL feature set compared to Hive.
Data Sources: Hive primarily works with data stored in Hadoop Distributed File System (HDFS) and Apache HBase, although it can also integrate with other data sources through connectors. Presto, on the other hand, has a wide variety of built-in connectors that allow it to query data from different sources such as Hadoop, relational databases, and NoSQL databases.
Scalability: Both Hive and Presto are scalable, but Presto is known for its ability to scale horizontally with ease. Presto's distributed architecture allows it to handle large workloads by adding more nodes to the cluster, while Hive's batch processing approach may require additional configuration for horizontal scaling.
Community and Ecosystem: Hive has been around for a longer time and has a larger user community and ecosystem. It has a well-established set of tools and frameworks that integrate with Hive, such as Apache Pig, Apache Spark, and Apache Tez. Presto, although relatively newer, is gaining popularity and has a growing community and ecosystem.
In summary, Apache Hive and Presto have significant differences in their architecture, performance, language support, data source compatibility, scalability, and community/ecosystem size. These differences make each technology more suitable for specific use cases and environments.
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 Presto
- Works directly on files in s3 (no ETL)18
- Open-source13
- Join multiple databases12
- Scalable10
- Gets ready in minutes7
- MPP6