FAQ

What is TimescaleDB?

TimescaleDB is the only open source time-series database that supports full SQL. Optimized for fast ingest and complex queries, TimescaleDB is easy to use like a traditional relational database, yet scales in ways previously reserved for NoSQL databases. In particular, this makes TimescaleDB an ideal candidate for operational analytics. TimescaleDB Open Source is distributed under the Apache 2.0 license. [Top]

Why build another time-series database?

Time-series data is cropping up in more and more places: monitoring and DevOps, sensor data and IoT, financial data, logistics data, app usage data, and more. Often this data is high in volume and complex in nature (e.g., multiple measurements and labels associated with a single time). This means that storing time-series data demands both scale and efficient complex queries. Yet achieving both of these properties has remained elusive. Users have typically been faced with the trade-off between the horizontally scalability of NoSQL and the query power of relational databases. We needed something that offered both, so we built it. [Top]

Why should I use TimescaleDB?

As time becomes a more critical dimension along which data is measured, TimescaleDB enables developers and organizations to harness more of its power: analyzing the past, understanding the present, and predicting the future. Unifying time-series data and relational data at the query level removes data silos, and makes demos and prototypes easier to get off the ground. The combination of scalability and a full SQL interface empowers a broad variety of people across an organization (e.g., developers, product managers, business analysts, etc.) to directly ask questions of the data. In other words, by supporting a query language already in wide use, TimescaleDB ensures that your questions are limited by your imagination, not the database. [Top]

What can I use TimescaleDB for?

TimescaleDB is ideal for time-series workloads that would benefit from a SQL interface. SQL carries a variety of benefits: a query language that most developers already know; rich set of functions and utilities; and a broad ecosystem of tools, connectors, and visualization options. Also, since SQL JOINS are natively supported in TimescaleDB, data from different sources can be combined at query time (e.g., combining relational data stored in PostgreSQL tables with time-series data stored in TimescaleDB hypertables). This ability to store relational data alongside time-series data enables developers to simplify their stack, potentially reducing complex polyglot architectures to a single operational analytical database.

Owing to these advantages, TimescaleDB is currently deployed across a variety of industries, including manufacturing, energy, utilities, mining, oil and gas, finance, ad tech, smart spaces, and more. Use cases include complex monitoring and analytics; predicting the performance and behavior of applications, models, consumers and connected machines; powering operational analytical workflows and dashboards; for QA and performance testing. [Top]

Why should I choose Timescale Cloud?

If you want all the benefits of TimescaleDB without the hassle of installing, maintaining, and administering the database itself, then Timescale Cloud is the best choice for you.

Timescale Cloud manages all of the operational elements of your database so you can focus on building your applications and not making sure the infrastructure works. We ensure you have a secure, high availability environment where we manage the infrastructure all the way down to setting up replications, point-in-time recovery, read replicas, backups, and more. [Top]

Which cloud providers and regions are supported by Timescale Cloud?

Timescale Cloud is available in the following Clouds and Regions:

Amazon Web Services (AWS): N. Virginia (us-east-1), Ohio (us-east-2), N. California (us-west-1), Oregon (us-west-2), São Paulo (sa-east-1), Stockholm (eu-north-1), Ireland (eu-west-1), London (eu-west-2), Paris (eu-west-3), Frankfurt (eu-central-1), Canada (ca-central-1), Singapore (ap-southeast-1), Sydney (ap-southeast-2), Tokyo (ap-northeast-1), Mumbai (ap-south-1), Seoul (ap-northeast-2)
Microsoft Azure (Azure): East US, East US 2, West Europe, Southeast Asia
Google Cloud Platform (GCP): Northern Virginia, Los Angeles, South Carolina, Iowa, Oregon, São Paulo, Zürich, London, Frankfurt, Finland, Belgium, Netherlands, Montréal, Sydney, Taiwan, Mumbai, Hong Kong, Tokyo, Singapore [Top]

What is the difference between Timescale, TimescaleDB, and Timescale Cloud?

Timescale is the company. Timescale builds a category-defining time-series database called TimescaleDB. Timescale hosts and manages TimescaleDB on behalf of its customers via a product called Timescale Cloud. [Top]

Do you really support “all of SQL”?

Yes, all of SQL, including: secondary indexes, JOINs, window functions. In fact, to the outside world, TimescaleDB looks like a PostgreSQL database: You connect to the database as if it’s PostgreSQL, and you can administer the database as if it’s PostgreSQL. Any tools and libraries that connect with PostgreSQL will automatically work with TimescaleDB. [Top]

Why SQL?

SQL is the most widely-used query language in the world for interacting with a database and manipulating data. We wanted TimescaleDB to be easy to use and powerful. Because SQL is so widely-used, it allows an entire organization to access their data, lending different perspectives to the analysis of that data and empowering people in their respective roles. It also allows for easy migration of data residing in PostgreSQL tables to TimescaleDB hypertables. Put another way: we wanted to ensure that your queries were only limited by your imagination, not by the query language. For a deeper dive into why we’re positive on SQL, read this blog post: Why SQL is beating NoSQL, and what this means for the future of data [Top]

What SQL features are supported?

We support all of SQL, including secondary indices, complex predicates, JOINs, window functions, CTEs, etc. Additionally, we extend SQL to introduce new semantics that make time-series manipulation easier. Behind the scenes, we also optimize the PostgreSQL query planner to enable the database to properly reason about time-series data, which in some cases leads to over 10,000x improvement in query latency. [Top]

How do I write data?

Just via normal SQL, but here are some insert examples. [Top]

How do I read data?

Just via normal SQL, but here are some query examples. [Top]

What are my compression options?

Since v1.5, TimescaleDB has supported native compression that uses a hybrid row/columnar approach combined with type-specific compression algorithms (e.g., different compression algorithms for timestamps, integers, floats, strings, or other data). Many users see between a 90-98% reduction in their storage footprint, leading to significant cost savings (and other query performance improvements). Note that compression must be explicitly turned on and configured for a hypertable; compression is by default off. For more details about how to use TimescaleDB ncompression, please see our compression docs or a longer technical deep-dive on our blog . [Top]

How far can TimescaleDB scale?

We’ve first focused on scaling TimescaleDB up on a single node. In our internal benchmarks on standard cloud VMs we regularly test TimescaleDB to 10+ billion rows, while sustaining insert rates of 100-200k rows / second (1-2 million metric inserts / second).

With more powerful hardware, users have scaled TimescaleDB to 500 billion rows of data while sustaining 400k row inserts / second. In this case, customers took advantage of the fact that we allow users to elastically add disks (i.e., RAID), in order to scale up capacity on a single node. As disks are added, chunks are redistributed across them, in similar fashion to how they would be distributed across nodes.

With that said, the principal design decisions implemented for scaling up are much the same for allowing TimescaleDB to scale out horizontally in a linear fashion across many servers. TimescaleDB is designed to combine the scalability of popular NoSQL databases, with the native query complexity supported by RDBMS systems. Read on for more details on clustering. [Top]

How does TimescaleDB scale?

TimescaleDB’s architecture leverages two key properties of time-series data:

Time-series data is largely immutable. New data continually arrives, typically as writes (inserts) to the latest time intervals, not as updates to existing records.
Workloads have a natural partitioning across both time and space.

TimescaleDB leverages these properties by automatically partitioning data into two-dimensional “chunks” (i.e., smaller PostgreSQL tables), performing operations and optimizing query planning across all chunks. This partitioning of the data into chunks ensures that recent tables’ indexes are kept in memory as data is inserted into the database. Yet all this complexity is abstracted away from the user and they are exposed to a single table interface (a “hypertable”) that functions exactly as a normal table in PostgreSQL does. For more information, see this blog post: Time-series data: Why (and how) to use a relational database instead of NoSQL. [Top]

Is there a clustered version and how can I try it?

We often find that there may be a few different things people are looking for when they ask about support for “clustering”:

High availability: As a PostgreSQL extension, hot standbys of TimescaleDB can be set up using streaming replication. This is done just as one would do setting up read replicas with vanilla PostgreSQL (although we do not recommend using logical replication).
Scale the amount of available storage: TimescaleDB allows you to elastically add disks to scale-up the capacity on a single hypertable.
Increase insert rates: Depending on your use case, we have users inserting 100-400K row inserts / second on a single node.
Increase query throughput: Along with ensuring high availability, replicas can be queried in parallel with the master node to increase query throughput.
Query parallelization: Today TimescaleDB takes advantage of the enhanced parallelization support offered in PostgreSQL 10.

To recap, we currently support read-only clustering via PostgreSQL streaming replication for high availability and for increasing query throughput. TimescaleDB’s single node scalability (to over 500 billion rows / 50TB), achieved by elastically adding disk space (especially simple using network attached disks in cloud environments), applies to these read-only clusters.

Full, scale-out clustering (i.e., for data volumes > 500 billion rows and ingest rates > 300K row inserts / second) is in the works and will leverage the automated partitioning capabilities already available in single-node TimescaleDB today. [Top]

What are hypertables and chunks?

Our documentation describes these design elements in more depth. [Top]

How should I configure chunking?

See the Best Practices section of our documentation. [Top]

How are hypertable chunks determined across the space dimension?

All hypertable chunks are partitioned automatically across time, which is necessary for right-sizing the chunks such that the B-trees for a table’s indexes can reside in memory during inserts to avoid thrashing that would otherwise occur while modifying arbitrary locations in those trees. In addition, the user has the option when creating the hypertable to partition across the space dimension (partition key) on something like a device id, customer id, or other unique id. Space partitions use hashing: Every distinct item is hashed to one of N buckets. The main purpose of space partitioning is to enable parallel I/O to the same time interval or to build smaller tables when regularly performing a range query for a single device/customer/ticker. For more details on space partitioning, see Best Practices. [Top]

Why would I use TimescaleDB over vanilla PostgreSQL?

Read our TimescaleDB-PostgreSQL benchmarks:

To summarize, TimescaleDB offers:

Ease-of-use: TimescaleDB is far easier to use because creating partitions (or what we call “chunks”) is automatically performed for the user. All of the complexity of automatic partitioning is abstracted away behind a “hypertable”, which users interact with just as they would with a PostgreSQL table.
Much higher ingest scale: TimescaleDB sees throughput more than 20X that of PostgreSQL once tables reach moderate size (e.g., 10s of millions of rows). While vanilla PostgreSQL is suitable for time-series data at low volumes, it does not scale well to the volume of data that most time-series applications produce, especially when running on a single server. In particular, vanilla PostgreSQL has poor write performance for moderate tables, and this problem only becomes worse over time as data volume grows linearly in time. These problems emerge when table indexes can no longer fit in memory, as each insert will translate to many disk fetches to swap in portions of the indexes’ B-Trees. TimescaleDB solves this through its heavy utilization of time-space partitioning, even when running on a single machine. So all writes to recent time intervals are only to tables that remain in memory, and updating any secondary indexes is also fast as a result.
Superior (or similar) query performance: Queries that can reason specifically about time ordering can be much more performant (1000s of times faster) in TimescaleDB. On single disk machines, at least, many simple queries that just perform indexed lookups or table scans are similarly performant between PostgreSQL and TimescaleDB.
Much faster data deletion: To save space or to implement data retention policies, vanilla PostgreSQL requires expensive “vacuuming” operations to defragment the disk storage associated with such tables. TimescaleDB avoids vacuuming operations and easily enforces data retention policies by specifying the data you wish to be deleted that is older than a specified time period. For more information, see Data Retention.
Extended time-oriented features: TimescaleDB includes time-series specific features not included in vanilla PostgreSQL and entirely unique to TimescaleDB (e.g., time_bucket,first and last), with more to come. [Top]

How compatible is TimescaleDB with PostgreSQL?

TimescaleDB is implemented as an extension to PostgreSQL that introduces transparent scalability and performance optimizations, as well as time-series specific features (e.g., arbitrary aggregations, data retention policies). TimescaleDB connects with any and all third party tools that communicate with standard PostgreSQL connectors. TimescaleDB supports the same extensions, tools and drivers that PostgreSQL supports. You can continue to run your existing PostgreSQL databases and work with your current visualization and reporting tools. [Top]

How does TimescaleDB handle geospatial data?

As an extension of PostgreSQL, TimescaleDB works well with PostGIS. For example, see our tutorial using PostGIS and TimescaleDB on NYC taxicab data. We are actively exploring the extent of TimescaleDB’s geospatial capabilities (i.e., partitioning by location). If you have a use case with a geospatial component, please contact us and we’d be happy to discuss. [Top]

Is TimescaleDB currently being used in production?

Yes. TimescaleDB is currently deployed in production across a variety of industries including manufacturing, energy, utilities, mining, oil and gas, finance, ad tech, smart spaces, and more. [Top]

When is TimescaleDB a good choice?

TimescaleDB is a good choice:

If you, and more of your organization, want to make standard SQL queries on time-series data, even at scale. Most (all?) NoSQL databases require learning either a new query language or using something that’s at best “SQL-ish” (which still breaks compatibility with existing tools and causes some degree of mental friction).
If you need to (or would like) only to manage one database for your relational and time-series data. Otherwise, users often need to silo data into two databases: a “normal” relational one, and a second time-series one.
If you want JOINs, which allow you to query relational and time-series data together at the database layer and might entirely remove the need to develop this capability at the application layer (read: frees up developer resources).
If you want query performance for a varied set of queries. More complex queries are often slow or full table scans on NoSQL databases, while some data stores can’t even support many natural queries.
If you want to simplify database management. TimescaleDB can be managed just like PostgreSQL and inherits its support for varied datatypes and indexes (B-tree, hash, range, BRIN, GiST, GIN).
If support for geospatial data is desirable. Data stored in TimescaleDB can leverage PostGIS’s geometric datatypes, indexes, and queries.
If you want greater optionality when it comes to using third-party tools. TimescaleDB supports anything that speaks SQL (i.e., the entire PostgreSQL ecosystem).
If you already use and like PostgreSQL, and don’t want to have to “give it up” and move to a NoSQL system in order to scale to larger volumes of data.
If you already chose to abandon PostgreSQL or another relational database for a Hadoop/NoSQL system due to scaling concerns or issues. We will provide support for the migration back. [Top]

What if I my use case is simple key-value reads?

For this scenario, in-memory or column-oriented databases are designed for key-value storage with fast lookup and a relational database may not be ideal. However, these systems clearly do not scale to large data volumes and cannot perform well for more complex queries (whereas relational databases like TimescaleDB and PostgreSQL are better suited). [Top]

What if I have very sparse or unstructured data?

Good question. TimescaleDB leverages PostgreSQL support for JSON/JSONB formats and handles sparsity quite efficiently (bitmaps for NULL values). However, there are some best practices and suggestions that may apply to get optimal performance depending on your scenario. Please see discussion in these docs or join our Slack group. [Top]

What is the TimescaleDB open-source license?

Apache 2.0. [Top]

Is there a TimescaleDB community or group I can join?

Yes! We have a very active online community in Slack, and you can report any issues on our GitHub page. [Top]

Can I get support or a commercial license?

Yes. Please contact us for more information. [Top]

FAQ

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