InfluxDB vs Presto

Overview

InfluxDB

Stacks1.0K

Followers1.2K

Votes175

Presto

Stacks394

Followers1.0K

Votes66

InfluxDB vs Presto: What are the differences?

Introduction: InfluxDB and Presto are both popular database systems, but they serve different purposes and have distinct features. Here we will outline the key differences between InfluxDB and Presto.

Data Model: InfluxDB is a time-series database designed for high-performance storage and retrieval of time-series data, making it ideal for use cases like monitoring, IoT, and real-time analytics. On the other hand, Presto is a distributed SQL query engine that enables querying data where it resides, making it more versatile for a wider range of use cases beyond time-series data.
Query Language: InfluxDB uses its own query language called InfluxQL, optimized for time-series data operations like aggregation and downsampling. In contrast, Presto supports standard SQL, making it familiar and accessible to most data analysts and engineers. This flexibility allows Presto to handle complex analytical queries across various data sources easily.
Scalability: InfluxDB is horizontally scalable and can handle large amounts of time-series data using clustering and sharding techniques. Presto, on the other hand, is built for interactive querying on large datasets across distributed storage systems like Hadoop, S3, and more. This architecture makes Presto well-suited for ad-hoc analysis and big data processing.
Use Cases: While InfluxDB is tailored for time-series data management and monitoring applications, Presto is commonly used in data warehousing, business intelligence, and analytics scenarios. InfluxDB excels in IoT sensor data, metrics monitoring, and anomaly detection, while Presto shines in complex queries on structured or semi-structured data sets.
Data Sources: InfluxDB is optimized for handling time-series data from sources like sensors, devices, servers, and applications, storing and analyzing this data efficiently. In contrast, Presto can query data from various sources including relational databases, Hadoop, cloud storage, and more, enabling cross-source analysis and reporting capabilities.
Performance: InfluxDB is optimized for high ingest rates and fast query responses on time-series data, ensuring rapid data retrieval and monitoring capabilities. Presto focuses on distributed query processing, providing parallel execution and scalability for complex analytical workloads on large datasets, ensuring high performance in data exploration and analysis tasks.

In Summary, InfluxDB and Presto differ in their data models, query languages, scalability, use cases, data sources, and performance characteristics, catering to specific requirements for time-series data management and complex querying tasks in different environments.

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Advice on InfluxDB, Presto

Ashish

Tech Lead, Big Data Platform at Pinterest

Nov 27, 2019

Needs adviceon

Apache Hive

Presto

Amazon EC2

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

3.72M views3.72M

Comments

Anonymous

Apr 21, 2020

Needs advice

We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.

So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily

381k views381k

Comments

Karthik

CPO at Cantiz

Nov 5, 2019

Decided

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.

225k views225k

Comments

Detailed Comparison

InfluxDB	Presto
InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.	Distributed SQL Query Engine for Big Data
Time-Centric Functions;Scalable Metrics; Events;Native HTTP API;Powerful Query Language;Built-in Explorer	-
Statistics
Stacks 1.0K	Stacks 394
Followers 1.2K	Followers 1.0K
Votes 175	Votes 66
Pros & Cons
Pros 59 Time-series data analysis 30 Easy setup, no dependencies 24 Fast, scalable & open source 21 Open source 20 Real-time analytics Cons 4 Instability 1 HA or Clustering is only in paid version 1 Proprietary query language	Pros 18 Works directly on files in s3 (no ETL) 13 Open-source 12 Join multiple databases 10 Scalable 7 Gets ready in minutes
Integrations
No integrations available	PostgreSQL Kafka Redis MySQL Hadoop Microsoft SQL Server

What are some alternatives to InfluxDB, Presto?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

InfluxDB vs Presto: What are the differences?

Data Model: InfluxDB is a time-series database designed for high-performance storage and retrieval of time-series data, making it ideal for use cases like monitoring, IoT, and real-time analytics. On the other hand, Presto is a distributed SQL query engine that enables querying data where it resides, making it more versatile for a wider range of use cases beyond time-series data.
Query Language: InfluxDB uses its own query language called InfluxQL, optimized for time-series data operations like aggregation and downsampling. In contrast, Presto supports standard SQL, making it familiar and accessible to most data analysts and engineers. This flexibility allows Presto to handle complex analytical queries across various data sources easily.
Scalability: InfluxDB is horizontally scalable and can handle large amounts of time-series data using clustering and sharding techniques. Presto, on the other hand, is built for interactive querying on large datasets across distributed storage systems like Hadoop, S3, and more. This architecture makes Presto well-suited for ad-hoc analysis and big data processing.
Use Cases: While InfluxDB is tailored for time-series data management and monitoring applications, Presto is commonly used in data warehousing, business intelligence, and analytics scenarios. InfluxDB excels in IoT sensor data, metrics monitoring, and anomaly detection, while Presto shines in complex queries on structured or semi-structured data sets.
Data Sources: InfluxDB is optimized for handling time-series data from sources like sensors, devices, servers, and applications, storing and analyzing this data efficiently. In contrast, Presto can query data from various sources including relational databases, Hadoop, cloud storage, and more, enabling cross-source analysis and reporting capabilities.
Performance: InfluxDB is optimized for high ingest rates and fast query responses on time-series data, ensuring rapid data retrieval and monitoring capabilities. Presto focuses on distributed query processing, providing parallel execution and scalability for complex analytical workloads on large datasets, ensuring high performance in data exploration and analysis tasks.

InfluxDB vs Presto

Overview