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Apache Parquet vs Presto: What are the differences?
<Apache Parquet vs Presto>
1. **Data Format**: Apache Parquet is a columnar storage format optimized for large-scale queries, while Presto is a distributed SQL engine that can query various data sources. Parquet is ideal for analyzing large datasets efficiently by scanning only the columns needed for a query, saving significant time and resources.
2. **Storage Location**: Apache Parquet stores data in a structured format on disk, while Presto does not have its own storage mechanism and relies on querying data from various sources like Hadoop, S3, MySQL, etc. Parquet's efficient storage format enables faster data retrieval by reducing disk reads and improving query performance.
3. **Query Execution**: Presto boasts a high-performance query execution engine that processes complex SQL queries in a distributed manner across a cluster of machines, achieving excellent scalability. On the other hand, Parquet's focus is on storing data in an optimized way, allowing fast and efficient query processing by tools like Presto.
4. **Schema Evolution**: Apache Parquet supports schema evolution, allowing users to add new columns and modify existing ones without affecting compatibility with older data formats. Presto, on the other hand, requires schema changes to be defined explicitly before querying the data, making it less flexible in handling evolving data schemas.
5. **Data Compression**: Both Apache Parquet and Presto support various data compression algorithms to reduce storage space and improve query performance. Parquet, however, excels in compression efficiency by using advanced techniques like dictionary encoding and run-length encoding to further optimize storage and processing speed.
6. **Use Case**: While Apache Parquet is primarily used for storing and querying structured data efficiently in big data environments, Presto is suitable for interactive queries and analytics across different data sources, making it a versatile tool for ad-hoc analysis and real-time decision-making.
In Summary, Apache Parquet and Presto differ in their data format, storage location, query execution capabilities, schema evolution support, data compression techniques, and primary use cases.
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 Parquet
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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