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Feb 13, 2025
Time-series data is emerging in more and more applications, including IoT, DevOps, finance, retail, logistics, oil & gas, manufacturing, automotive, aerospace, SaaS, and even machine learning and AI.
In the past, time-series databases focused narrowly on metrics and monitoring; today, it’s become clear that software developers really need a true time-series database designed for a variety of operational workloads.
This benchmark will compare two leading time-series databases, TimescaleDB and InfluxDB, to help software developers choose the time-series database best suited for their needs.
For inserts:
For queries:
Overall, TimescaleDB shows better performance scaling and more flexibility, while InfluxDB works well for simpler use cases with low cardinality. However, we believe a benchmark should go beyond just performance. Keep reading to learn more. 👀
Before diving into the benchmark performance numbers, let's discuss other crucial evaluation aspects. There are several factors to consider when evaluating a time-series database for your workload:
You'll see that TimescaleDB outperforms InfluxDB in handling time-series data due to its flexibility, compatibility with existing tools, and performance with high-cardinality datasets and complex queries. It also boasts a broader ecosystem, superior operational management, and robust community support.
Typically, database comparisons focus on performance benchmarks. Yet performance is just a part of the overall picture. It doesn't matter how well a database performs in benchmarks if it lacks the data model, query language, or reliability required for your production workloads.
With that in mind, we begin by comparing TimescaleDB and InfluxDB across three qualitative dimensions—data model, query language, and reliability—before diving deeper into performance benchmarks. We then round out with a comparison of the database ecosystem, operational management, and company/community support.
Yes, we are the developers of TimescaleDB, so you might quickly disregard our comparison as biased. But if you let the analysis speak for itself, you’ll find that we stay objective .
Also, this comparison isn’t a purely theoretical activity for us. Our company began as an IoT platform, where we first used InfluxDB to store our sensor data. However, owing to most of the differences listed below, we found InfluxDB unsatisfactory. So, we built TimescaleDB as the first time-series database that satisfied our needs and then discovered others who needed it as well, which is when we decided to open source the database.
Today, the TimescaleDB developer community has come a long way, including organizations like AppDynamics, Bosch, Cisco, Comcast, Fujitsu, IBM, Schneider Electric, Samsung, Siemens, Uber, Warner Music, and thousands of others.
Ultimately, our goal is to help you decide the best time-series database for your needs.
Databases are opinionated. The way a database chooses to model and store your data determines what you can do with it.
When it comes to data models, TimescaleDB and InfluxDB have two very different opinions: TimescaleDB is a relational database, while InfluxDB is more of a custom, NoSQL, non-relational database. What this means is that TimescaleDB relies on the relational data model, commonly found in PostgreSQL, MySQL, SQL Server, Oracle, etc. On the other hand, InfluxDB has developed its own custom data model, which, for the purpose of this comparison, we’ll call the tagset data model.
The relational data model has been in use for several decades now. With the relational model in TimescaleDB, each time-series measurement is recorded in its own row, with a time field followed by any number of other fields, which can be floats, ints, strings, booleans, arrays, JSON blobs, geospatial dimensions, date/time/timestamps, currencies, binary data, or even more complex data types.
One can create indexes on any one field (standard indexes) or multiple fields (composite indexes), or on expressions like functions, or even limit an index to a subset of rows (partial index). Any of these fields can be used as a foreign key to secondary tables, which can then store additional metadata.
An example is below:
The advantage of this approach is that it is quite flexible. One can choose to have the following:
The disadvantage of this approach is that to get started, one needs to generally choose a schema and explicitly decide whether or not to have indexes.
Note: In the past several years, it’s been popular to criticize the relational model by claiming that it is not scalable. However, as we have already shown, this is simply not true: Relational databases can indeed scale very well for time-series data.
With the InfluxDB tagset data model, each measurement has a timestamp and an associated set of tags (tagset) and fields (fieldset). The fieldset represents the actual measurement reading values, while the tagset represents the metadata that describes the measurements.
Field data types are limited to floats, ints, strings, and booleans and cannot be changed without rewriting the data. Tagset values are indexed, while fieldset values are not. Also, tagset values are always represented as strings and cannot be updated.
An example is below:
The advantage of this approach is that if one’s data naturally fits the tagset model, then it is quite easy to get started, as one doesn’t have to worry about creating schemas or indexes.
Conversely, this model's disadvantage is that it is quite rigid and limited. It does not allow the creation of additional indexes or indexes on continuous fields (e.g., numerics), the update of metadata after the fact, the enforcement of data validation, etc.
In particular, even though this model may feel “schemaless”, there is actually an underlying schema that is auto-created from the input data, which may differ from the desired schema.
The tagset data model in InfluxDB is more limiting and thus might be easier to get started with for some. However, the relational model in TimescaleDB is more versatile and offers more functionality, flexibility, and control. This is especially important as your application evolves. When planning your system, you should consider both its current and future needs.
Note: It’s also possible to create relational schema that are equivalent to the tagset model for specific use cases, such as Prometheus metrics. For more on this, see the Timescale-Prometheus GitHub repository.
Generally in the world of database query languages, there have been two extremes: full SQL support on one end, and completely custom languages (sometimes known as “NoSQL”) on the other.
From the beginning, TimescaleDB has firmly existed at the SQL end of the spectrum, fully embracing the language from day one and later further extending it to simplify time-series analysis. This has enabled TimescaleDB to have a minimal learning curve for new users and allowed it to inherit the entire SQL ecosystem of third-party tools, connectors, and visualization options, which is larger than that of any other time-series database.
In contrast, InfluxDB began with a “SQL-like” query language (called InfluxQL), placing it in the middle of the spectrum, and then made a marked move towards the “custom” end with a new query language from InfluxDB called Flux. (Read the Flux announcement on Hacker News and our comparison of SQL vs. Flux.)
At a high level, here’s how the two language syntaxes compare, using a Flux query that performs math across measurements as an example:
SELECT time, (memUsed / procTotal / 1000000) as value
FROM measurements
WHERE time > now() - '1 hour';
SQL query
// Memory used (in bytes)
memUsed = from(bucket: "telegraf/autogen")
|> range(start: -1h)
|> filter(fn: (r) =>
r._measurement == "mem" and
r._field == "used"
)
// Total processes running
procTotal = from(bucket: "telegraf/autogen")
|> range(start: -1h)
|> filter(fn: (r) =>
r._measurement == "processes" and
r._field == "total"
)
// Join memory used with total processes and calculate
// the average memory (in MB) used for running processes.
join(
tables: {mem:memUsed, proc:procTotal},
on: ["_time", "_stop", "_start", "host"]
)
|> map(fn: (r) => ({
_time: r._time,
_value: (r._value_mem / r._value_proc) / 1000000
})
)Flux query
For most use cases, we believe that SQL is the right query language for a time-series database. SQL has a rich tradition and history, including familiarity among millions of developers and a vibrant ecosystem of tutorials, training, and community leaders. In short, choosing SQL means you’re never alone.
While Flux may make some tasks easier, there are significant trade-offs to adopting a custom query language. First, new query languages introduce significant overhead and reduce readability. They force a greater learning curve onto new users and possess a scarcity of compatible tools and community support.
And they may not even be a viable option: Rebuilding a system and re-educating a company to write and read a new query language is often not practically possible, particularly if the company already is using SQL-compatible tools on top of the database, such as Tableau, for visualization.
This is also why SQL is making a comeback as the query language of choice for data infrastructure in general. Indeed, SQL is well-documented and is the third-most commonly used programming language among developers.
Another cardinal rule for a database: It cannot lose or corrupt your data. This is a dimension where there is a stark difference in the approaches TimescaleDB and InfluxDB have taken, which has implications for reliability.
At its start, InfluxDB sought to completely write an entire database in Go. In fact, it doubled down on this decision with its 0.9 release, which again completely rewrote the backend storage engine. With InfluxDB 2.0 (currently in beta at the time of publishing), it’s (at least) the second complete rewrite attempted by the InfluxData team.
These design decisions have significant implications that affect reliability. First, InfluxDB has to implement the full suite of fault-tolerance mechanisms, including replication, high availability, and backup/restore. Second, InfluxDB is responsible for its on-disk reliability, e.g., making sure all its data structures are durable and resist data corruption across failures (and even failures during failure recovery).
We made a dramatically different architectural decision when building TimescaleDB: Built on PostgreSQL, TimescaleDB relies on the 25+ years of hard, careful engineering work that the entire PostgreSQL community has done to build a rock-solid database that supports millions of mission-critical applications worldwide.
In fact, this was at the core of our co-founder’s launch post about TimescaleDB: When Boring is Awesome. Stateless microservices may crash and reboot or trivially scale up and down. Actually, this is the entire “recovery-oriented computing” philosophy, as well as the thinking behind the new “serverless” design pattern. But your database needs to actually persist data and should not wake you up at 3 a.m. because it’s in some broken state.
First, programs can crash, servers can encounter hardware or power failures, and disks can fail or experience corruption. You can mitigate this risk (e.g., robust software engineering practices, uninterrupted power supplies, disk RAID, etc.) but not eliminate it completely; it’s a fact of life for systems.
In response, databases have been built with an array of mechanisms to further reduce such risk, including streaming replication to replicas, full-snapshot backup and recovery, streaming backups, robust data export tools, etc.
Given TimescaleDB’s design, it’s able to leverage the full spectrum of tools that the Postgres ecosystem offers and has rigorously tested: streaming replication for high availability and read-only replicas, pg_dump and pg_restore for full database snapshots, pg_basebackup and log shipping/streaming for incremental backups and arbitrary point-in-time recovery, pgBackrest or WAL-E for continuous archiving to cloud storage, and robust COPY FROM and COPY TO tools for quickly importing/exporting data with a variety of formats.
InfluxDB, on the other hand, has had to build all these tools from scratch. In fact, it doesn’t offer many of these capabilities even today. It initially offered replication and high availability in its open-source version but subsequently pulled this capability out of open source and into its enterprise product. InfluXDB backup tools have the ability to perform a full snapshot and recover to this point in time, and only recently added some support for a manual form of incremental backups.
That said, InfluxDB’s approach of performing incremental backups based on database time ranges seems quite risky from a correctness perspective, given that timestamped data may arrive out-of-order, and thus the incremental backups—since some time period would not reflect this late data.
And its ability to easily and safely export large volumes of data is also quite limited. We’ve heard from many users (including Timescale engineers in their past careers) that they had to write custom scripts to safely export data; asking for more than a few 10,000s data points would cause the database to out-of-memory error and crash.
Second, databases need to provide strong on-disk reliability and durability, so that once a database has committed to storing a write, it is safely persisted to disk. In fact, for very large data volumes, the same argument even applies to indexing structures, which could otherwise take hours or days to recover. There’s a good reason that file systems have moved from painful fsck recovery to journaling mechanisms.
In TimescaleDB, we made the conscious decision not to change the lowest levels of PostgreSQL storage (even in implementing hybrid row/columnar storage in TimescaleDB native compression) nor interfere with the proper function of its write-ahead log (WAL).
The WAL ensures that as soon as a write is accepted, it gets written to an on-disk log to ensure safety and durability, even before the data is written to its final location and all its indexes are safely updated. These data structures are critical for ensuring consistency and atomicity; they prevent data from becoming lost or corrupted and ensure safe recovery.
The database community (and PostgreSQL) has worked hard on this: What happens if your database crashes (and will subsequently try to recover) while it’s already recovering from another crash?
InfluxDB had to design and implement all recovery, reliability, and durability functionality from scratch. This is a notoriously hard problem in databases that typically takes many years or even decades to get correct. Some metrics stores might be okay with occasionally losing data, but we see TimescaleDB being used in settings where this is not acceptable.
InfluxDB forums, on the other hand, are rife with such complaints: “DB lost after restart,” “data loss during high ingest rate,” “data lost from InfluxDB databases,” “unresponsive due to corruption after disk disaster,” “data messed up after restoring multiple databases,” and so on.
These challenges and problems are not unique to InfluxDB, and every developer of a reliable, stateful service must grapple with them. Every database goes through a period when it sometimes loses data because it's really, really hard to get all the corner cases right.
Eventually, all those corner cases come to haunt some operators. But PostgreSQL went through this period in the 1990s, while InfluxDB is still figuring these things out today.
Now, let’s get into some hard numbers with a quantitative comparison of the two databases across a variety of insert and read workloads. Given how common high-cardinality datasets are within time series, we will first take a look at how InfluxDB and TimescaleDB handle this issue.
Note: We've released all the code and data used for the below benchmarks as part of the open-source Time Series Benchmark Suite (TSBS) (GitHub).
For comparing both insert and read latency performance, we used the following setup:
For insert performance, we used the following datasets and configuration:
The datasets were created using Time-Series Benchmarking Suite, using the cpu-only use case.
On insert performance, as the cardinality of the dataset increases, the results are fairly clear.
For workloads with extremely low cardinality, like the configuration with 100 devices, InfluxDB offers better insert performance than TimescaleDB.
However, as cardinality increases, InfluxDB performance drops dramatically due to its reliance on time-structured merge trees (which, similar to the log-structured merge trees it is modeled after, suffers with higher-cardinality datasets).
This, of course, should be no surprise, as high cardinality is a well-known Achilles heel for InfluxDB (source: GitHub, Forums). In comparison, TimescaleDB actually sees better performance as cardinality increases, with moderate drop off in terms of absolute insert rate, very quickly surpassing InfluxDB in terms of insert performance for the configurations of 4,000, 100,000, 1 million, and 10 million devices.
That said, it is worth doing an honest analysis of your insert needs. If your insert performance is far below these benchmarks (e.g., 2,000 rows / second), then insert performance will not be your bottleneck, and this comparison becomes moot.
Insert performance summary
For benchmarking read latency, we used the following setup for each database (the machine configuration is the same as the one used in the insert comparison):
tags_id and hostname columns and ordered by time descending and usage_user columns.On read (i.e., query) latency, the results are more complex. Unlike inserts, which primarily vary on cardinality size (and perhaps batch size), the universe of possible queries is essentially infinite, especially with a language as powerful as SQL.
Often, the best way to benchmark read latency is to do it with the actual queries you plan to execute. For this case, we use a broad set of queries to mimic the most common query patterns.
The results shown below are the average from 1,000 queries for each query type. Latencies in this chart are all shown as milliseconds, with an additional column showing the relative performance of TimescaleDB compared to InfluxDB (highlighted in orange when TimescaleDB is faster, in blue when InfluxDB is faster).
SIMPLE ROLLUPS
For simple rollups (i.e., groups), when aggregating one metric across a single host for 1 or 12 hours or multiple metrics across one or multiple hosts (either for 1 hour or 12 hours), TimescaleDB generally outperforms InfluxDB at both low and high cardinality. In particular, TimescaleDB exhibited 460 % of the performance of InfluxDB on configurations with 100 and 4,000 devices with 10 unique metrics being generated every read interval.
AGGREGATES
When calculating a simple aggregate for one device, performance is comparable between TimescaleDB and InfluxDB across any number of devices. But TimescaleDB significantly outperforms InfluxDB when it's necessary to aggregate more than one metric. In our benchmark, TimescaleDB demonstrates 168 % of the performance of InfluxDB when aggregating eight metrics across 100 devices and 156 % when aggregating eight metrics across 4,000 devices. Once again, TimescaleDB outperforms InfluxDB for high-end scenarios.
DOUBLE ROLLUPS
For double rollups aggregating metrics by time and another dimension (e.g., GROUPBY time, deviceId): When aggregating one metric, InfluxDB shows better performance than TimescaleDB, with TimescaleDB only 54% as good as InfluxDB for the 4,000 device config. However, as the number of metrics being aggregated increases, TimescaleDB achieves 188% of the performance of InfluxDB.
THRESHOLDS
When selecting rows based on a threshold, TimescaleDB outperforms InfluxDB. Timescale demonstrates between 350-860% the performance of InfluxDB when computing thresholds for a single device and 175-258% the performance of InfluxDB when computing thresholds for all devices for a random time window.
COMPLEX QUERIES
For complex queries that go beyond rollups or thresholds, the comparison is much more clear-cut: TimescaleDB vastly outperforms InfluxDB here (in some cases, over thousands of times faster). The absolute difference in performance here is actually quite stark: While InfluxDB might be faster by a few milliseconds or tens of milliseconds for some of the single-metric rollups, that difference is mostly indistinguishable to human-facing applications.
For complex queries that go beyond rollups or thresholds, there really is no comparison: TimescaleDB vastly outperforms InfluxDB here (in some cases over thousands of times faster).
Yet for these more complex queries, TimescaleDB provides real-time responses (e.g., 10–100s milliseconds), while InfluxDB sees significant human-observable delays (tens of seconds).
It’s worth noting that several other complex queries, such as joins, window functions, geospatial queries, etc., weren't tested because InfluxDB didn't support them.
Notice that Timescale exhibits 340-7100% of the performance of Influx on these complex queries, many of which are common to historical analysis and monitoring.
Read latency performance summary
We had several operational issues benchmarking InfluxDB as our datasets grew, even with the Influx Time-series Index (TSI) enabled. In particular, as we experimented with higher cardinality data sets (100K+ tags), we ran into trouble with both inserts and queries on InfluxDB (but not on TimescaleDB).
While we were able to insert batches of 10k into InfluxDB at lower cardinalities, once we got to 100k devices, we would experience timeouts and errors with batch sizes that large. The most common errors were write errors caused by exceeding the maximum cache memory size, timeouts, and fatal out-of-memory errors, which all occurred during runtime.
Solving these errors required a combination of increasing the maximum cache size from the default 1 GB to between 4 GB and 64 GB as we went from 100,000 to 10,000,000 devices, as well as decreasing the batch size from 10,000 to between 5,000 and 1,000 and using client-side code to deal with the backpressure incurred at higher cardinalities. We had to force our client code to sleep for up to 30 seconds after requests received errors writing the batches.
In contrast, with TimescaleDB, we were able to write large batches at higher cardinality without issue and with no additional configuration.
Moreover, starting at 100K cardinality, we also experienced problems with some of our read queries on InfluxDB. Our InfluxDB HTTP connection would error out with a cryptic ‘End of File’ message. When we investigated the InfluxDB server, we found out that InfluxDB had consumed all available memory to run the query and subsequently crashed with an out-of-memory error.
Since PostgreSQL helpfully allows us to limit system memory usage with settings like shared_buffers and work_mem, this generally was not an issue for TimescaleDB, even at higher cardinalities.
High-cardinality datasets are a significant weakness for InfluxDB.
High-cardinality datasets are a significant weakness for InfluxDB. This is because of how the InfluxDB developers have architected their system, starting with their Time-series Index (TSI).
The InfluxDB TSI is a home-grown log-structured merge tree-based system comprised of various data structures, including hashmaps and bitsets. This includes an in-memory log (“LogFile”) that gets periodically flushed to disk when it exceeds a threshold (5 MB) and compacted to an on-disk memory-mapped index (“IndexFile”); a file (“SeriesFile”) that contains a set of all series keys across the entire database. (Described here in the InfluxDB documentation.)
The performance of the TSI is limited by the complex interactions of all of these data structures.
The design decisions behind the TSI also lead to several other limitations with performance implications:
SELECT * FROM sensor_data WHERE mem_free = 0), one could not do better than a full linear scan (i.e., O(n) time) to identify the relevant data points.SELECT * FROM sensor_data WHERE temperature > 90), one would again have to fully scan the entire dataset.In contrast, TimescaleDB is a relational database that relies on a proven data structure for indexing data: the B-tree. This decision leads to its ability to scale to high cardinalities.
First, TimescaleDB partitions your data by time, with one B-tree mapping time segments to the appropriate partition (“chunk”). All of this partitioning happens behind the scenes and is hidden from the user, who is able to access a virtual table (“hypertable”) that spans all of their data across all partitions.
Next, TimescaleDB allows for the creation of multiple indexes across your dataset (e.g., for equipment_id, sensor_id, firmware_version, site_id). These indexes are then created on every chunk, by default, in the form of a B-tree. (One can also create indexes using any of the built-in PostgreSQL index types: Hash, GiST, SP-GiST, GIN, and BRIN.)
This approach has a few benefits for high-cardinality datasets:
<, <=, =, >=, >, BETWEEN, IN, IS NULL, IS NOT NULL. Our example queries from above ( SELECT * FROM sensor_data WHERE mem_free = 0 and SELECT * FROM sensor_data WHERE temperature > 90’) will run in logarithmic, or O(log n), time.The database can only do so much, so when one typically turns to the broader third-party ecosystem for additional capabilities, the size and scope of the ecosystem make a large difference.
TimescaleDB’s approach of embracing SQL pays large dividends, as it allows TimescaleDB to speak with any tool that speaks SQL. In contrast, InfluxDB's non-SQL strategy isolates the database and limits its developers' use of InfluxDB.
Having a broad ecosystem makes deployment easier. For example, if one is already using Tableau to visualize data or Apache Spark for data processing, TimescaleDB can plug right into the existing infrastructure due to its compatible connectors.
Here is a non-exhaustive list of first-party (e.g., the components of the InfluxData TICK stack) and third-party tools that connect with either database to show the relative difference in the two database ecosystems.
For the open-source projects below, to reflect their popularity, we included the number of GitHub stars they had as of publication in parentheses, e.g., Apache Kafka (9k+). For many of the unofficial projects for InfluxDB, for example, the unofficial supporting project was often very early (very few stars) or inactive (no updates in months or years).
Even if a database satisfies all the above needs, it still needs to work, and someone needs to operate it.
Based on our experience, operational management requirements typically boil down to these categories: high availability, resource consumption (memory, disk, CPU), general tooling.
No matter how reliable the database, at some point, your node will go down due to hardware errors, disk errors, or some other unrecoverable issue.
Thus, the high availability of your database goes from a value-added feature to a requirement for production deployments. At that point, you will want to ensure you have a stand-by available for failover with no loss of data.
While InfluxDB's high availability is only offered by its paid enterprise version, TimescaleDB supports high availability for free in both its open-source and Community editions, via PostgreSQL streaming replication (as explained in this tutorial). This is yet another benefit that Timescale inherits as a result of the rock-solid foundation of PostgreSQL.
Disk usage
InfluxDB offers native compression using a variety of techniques: Snappy for strings, delta encoding, scaling, and simple-8b with run length encoding for timestamps, Gorilla delta encoding for floats, bits for booleans, and delta encoding for integers.
TimescaleDB offers native compression using a novel hybrid row/columnar storage approach, using Gorilla compression for floats, delta-of-delta, and simple-8b with run-length encoding for timestamps and integer-like types, whole-row dictionary compression for a few repeating values, with LZ compression on top and lastly LZ-based array compression for all other types. (See here for more on how TimescaleDB’s native compression works.)
Using a combination of datasets for the insert and query benchmarks above, and compressing all chunks for TimescaleDB, here’s how the two databases fared at varying cardinalities:
Note: Numbers do not include WAL size, as that is configurable by the user.
Thanks to its column-oriented structure, InfluxDB is able to achieve better compression ratios overall. However, the gap is on the order of megabytes on the low end and narrows at higher cardinalities. Considering the low memory usage of TimescaleDB compared to InfluxDB, and the fact that memory is often an order of magnitude more expensive than disk, we have found that this gap in storage not to be an issue.
CPU usage
According to an external comparison by DNSFilter, using TimescaleDB resulted in 10x better resource utilization (even with 30 % higher requests) when compared to InfluxDB:
When operating TimescaleDB, one inherits all of the battle-tested tools that exist in the PostgreSQL ecosystem: pg_dump and pg_restore for backup/restore, HA/failover tools like Patroni, load balancing tools for clustering reads like Pgpool, etc. Since TimescaleDB looks and feels like PostgreSQL, there are minimal operational learning curves. TimescaleDB “just works,” as one would expect from PostgreSQL.
For operating InfluxDB, one is limited to the tools that the Influx team has built: backup, restore, internal monitoring, etc.
Finally, when investing in an open-source technology primarily developed by a company, you are implicitly also investing in that company’s ability to serve you, whether you’re a paying customer or not. With that in mind, let’s note the differences between Timescale and InfluxData, the companies behind TimescaleDB and InfluxDB.
Timescale continues to invest in the community with its free self-managed versions and actively develops its high-performance cloud offering for time series and analytics.
In contrast, while Influx does have an open-source offering alongside its licensed InfluxDB Enterprise, its more advanced features, like clustering, remain gated behind an enterprise license. The company also appears to be de-prioritizing its open-source product and instead focusing on its SaaS offering, Influx Cloud.
Moreover, for technical products, support and resources often come not just from the company building the technology but also from the community of developers who use it. InfluxData is building its community from scratch, while Timescale is able to inherit and build on PostgreSQL’s community.
This means that InfluxData community support is a walled garden: inherently more closed, less varied, and smaller compared to the open diversity of expertise present in the vibrant 25+-year-old PostgreSQL ecosystem.
Furthermore, because TimescaleDB operates just like PostgreSQL, much of the knowledge base relevant to PostgreSQL is also relevant to TimescaleDB. So if you are new to TimescaleDB (or SQL or PostgreSQL), there are many resources available to help get you started. Alternatively, if you are already a SQL or PostgreSQL expert, you will already know how to use the majority of TimescaleDB (save for a small learning curve of optimizations built specifically for time-series data, like SQL functions for complex analysis).
No one wants to invest in technology only to have it limit their growth or scale in the future, let alone invest in something that's the wrong fit today.
Before making a decision, take a step back and analyze your stack, your team's skills, and your needs (now and in the future). It could be the difference between infrastructure that evolves and grows with you and one that crumbles to the ground and forces you to start all over.
In this post, we performed a detailed comparison of TimescaleDB and InfluxDB. We don’t claim to be InfluxDB experts, so we’re open to any suggestions on how to improve this comparison. In general, we aim to be as transparent as possible about our data models, methodologies, and analysis, and we welcome feedback. We also encourage readers to raise any concerns about the information we’ve presented in order to help us with benchmarking in the future.
We recognize that TimescaleDB isn’t the only time-series solution, and there are situations where it might not be the best answer. And we strive to be upfront in admitting where an alternate solution may be preferable. But we’re always interested in holistically evaluating our solution against that of others, and we’ll continue to share our insights with the greater community.
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If you want to keep reading about TimescaleDB, these articles will help you learn more: