Sold by: quasardb
Deployed on AWS
AWS Free Tier
High performance time series database
4.3
Overview
QuasarDB is a high performance, distributed, transactional, time series database. It can ingest data at very high speed, while giving you immediate access through a powerful, SQL-like, query language. QuasarDB was designed to withstand the most extreme use case that can be found in financial markets, aeronautics, and heavy industry.
Highlights
- Speed, power, convenience, safety
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Debian 13
Deployed on AWS
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This product is available free of charge. Free subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
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None
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Protocol version 48 [api] Add support for get metadata by ID [api] Batch operation to detach tags [api] Batch writers can now influence the server-side caching policy [api] Explicit support for timezone configuration in the API [api] New "lazy" batch table creation mode: create missing tables on insertion [api] New API to validate queries and get the expected schema of the result [api] New error code for asynchronous pipelines being full giving better feedback to users [api] New high-performance bulk reader for streaming large amounts of raw data from a Quasar server [api] New high-performance random bulk reader API for loading raw data chunks that fit in RAM from a Quasar server [api] New qdb_set_tags_fast API [api] Properly truncate every bucket when doing INSERT TRUNCATE [compatibility] Minimum glibc version is now 2.26 (was 2.17) [general] Applications will no longer leave stale temporary files [kernel] Aggregated tables internal states can be persisted for extra reliability [kernel] Automatically cancel background trimming and compaction when shutting down the server [kernel] Better scan-resistant caching heuristic based on LRU-2 [kernel] Enriched query logging [kernel] Fix a bug in the micro-index that could result in severely degraded performance [kernel] Fix a deduplication issue with asynchronous inserts [kernel] Fix a potential server-side crash with GROUP BY + WHERE after an ALTER [kernel] Fix an issue where Quasar could not restart after a system failure [kernel] Fix crash on deduplicated insertion on a column that has been added after table creation [kernel] Greatly improve the performance of first(x) and last(x) [kernel] Greatly improve user management enabling remote listing and modifications [kernel] Improve cache metrics [kernel] Improve memory usage for string columns [kernel] Improve performance of PARTITION BY in AGGREGATED TABLE [kernel] Improve support for PARTITION BY in AGGREGATED TABLE [kernel] More asynchronous pipeline statistics for better monitoring and planning [kernel] More write statistics for better monitoring and planning [kernel] Server side option to disable micro-indexes for testing purposes [kernel] Support for infinite windows in aggregated tables [kernel] The aggregation engine is more resistant to corrupted or altered data [kernel] UPDATE and DELETE storage modifications are now much more resistant to hardware and system failures [logging] Support for JSON-format log output [logging] Support for user-provided properties in logging [odbc] Full support for SAS Viya caslib [odbc] Greatly improved SAS Viya support [orderbook] Fix rare "empty result" bug [orderbook] General performance improvements [orderbook] Support for symbol tables in ORDERBOOK functions [persistence] Added more persistence statistics [persistence] Configurable paranoid S3 persistence mode: file upload verification S3-side checksum local checksum [persistence] Ensure files are properly truncated on disk to avoid excessive disk usage [persistence] Extended local validation before startup [persistence] Several configuration settings are now set per column family and default values have been updated [persistence] Updated compaction default settings for more throughput [protocol] Changed on-the-wire encryption from AES 256 GCM to AEGIS 256 [protocol] New on-the-wire data compression for improved network usage [query] Add fallback parameter to LEAD and LAG [query] Add support for BETWEEN in WHERE clauses [query] Add support for CREATE TABLE AS SELECT [query] Add support for INSERT INTO SELECT [query] Add support to specify time alignment of GROUP BY queries [query] Allow aliases for selected tables [query] Constant variables are now case insensitive [query] count($timestamp) fix to work with restrict to [query] Disallow negative steps in ASOF RANGE [query] Enhanced REPAIR capabilities [query] Ensure last(x) returns the last row of duplicated timestamps. [query] Every API will now validate that strings are valid UTF-8 sequences [query] Extended PIVOT support [query] Fix a bug in TWAP when used with GROUP BY that would return an internal timestamp list [query] Fix bug that made WHERE IN interpret an INT64 as a timestamp [query] Fix error with ASOF JOIN RANGE when aggregating on a string column [query] Fix performance issue with ASOF RANGE scanning an entire table [query] HAVING is now properly evaluated before OVER [query] Insertions with invalid timestamps will now be rejected [query] Massive performance improvement for DISTINCT COUNT [query] New multithreaded model for the SELECT engine with drastic performance improvements across the board [query] Query engine is now timezone aware [query] SHOW TABLE correctly displays the TTL of a table [query] Support for adding tags in a table at creation [query] Support for approximate median [query] Support for DELETE without an explicit RANGE [query] Support for exact median [query] Support for explicit timezone value in queries with new AT TIME ZONE construct [query] Support for IF/ELSE [query] Support for LIKE [query] Support for PostgreSQL-style timestamp casts [query] Support for quantile (Histogram Q-Digest and T-Digest) [query] Support high-performance CSV/TSV files loading with IMPORT command [query] Support last(x).$timestamp in aggregated tables [query] Time grouping now starts from epoch [shell] Display license information on start [shell] Support for manual trimming [shell] Support for multi-line queries
Additional details
Usage instructions
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QuasarDB runs as a systemd service, you can inspect its status as follows:
systemctl status qdbd.service
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An exported of QuasarDB metrics to Amazon Cloudwatch has been preinstalled and configured as a systemd service. You can inspect its status as follows:
systemctl status qdb-cloudwatch.timer systemctl status qdb-cloudwatch.service
In order for the exporter to work, it requires the relevant IAM permissions assigned to this EC2's instance role.
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To connect to your QuasarDB instance, you can use qdbsh:
qdbsh qdb://127.0.0.1:2836
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For security purposes, QuasarDB only listens to localhost by default.
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You can configure and customize your QuasarDB installation by editing the configuration file at /etc/qdb/qdbd.conf.
For more information about QuasarDB, please consult the documentation available online at https://doc.quasar.ai/
For support, please contact us at support@quasar.ai .
Resources
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Support
Vendor support
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Customer reviews
Joel F.
A clear breakthrough in timeseries database
Reviewed on Apr 01, 2019
Review provided by G2
What do you like best about the product?
QuasarDB is a clear innovation. QuasarDB enables you to store massive amount of data without sacrificing on query speed.
The level of performances that one can leverage is clearly at least one order of magnitude over a lot of competitors. From
the large selection of platforms and languages to high-performance compression features, it really simplifies
the management of large scale databases.
A must-have if performances on your whole stack is a required.
The level of performances that one can leverage is clearly at least one order of magnitude over a lot of competitors. From
the large selection of platforms and languages to high-performance compression features, it really simplifies
the management of large scale databases.
A must-have if performances on your whole stack is a required.
What do you dislike about the product?
No major roadblocks even if in some places the query error messages are sometimes unclear
What problems is the product solving and how is that benefiting you?
- HPC research on large datasets
- Storage of HPC datasets
- Storage of HPC datasets
Financial Services
a great analytics platform when high performance needed.
Reviewed on Mar 18, 2019
Review provided by G2
What do you like best about the product?
The software development team, really go deep on optimization and software quality, exploiting the best of Intel CPU. Very very few software platform are going that way, which explain the performance of the platform.
What do you dislike about the product?
no major roadblock, user interface cam always be improved.
What problems is the product solving and how is that benefiting you?
Nice platform for Financial market place process/data. Anything requesting near real time analytics.
Recommendations to others considering the product:
nice player, when needing near real time analytics.
Information Technology and Services
High performance timeseries database that is great with numbers
Reviewed on Mar 16, 2019
Review provided by G2
What do you like best about the product?
The performance of numerical calculations is superb -- when offloading calculations / aggregates to the database, it is able to achieve stellar performance.
What do you dislike about the product?
It takes some time to learn how to use it. The UI is very unforgiving sometimes, although it does seem to get better with every release.
What problems is the product solving and how is that benefiting you?
We moved from InfluxDB, and especially exploring our dataset has been much easier: the speed of the engine make it easy to interactively test and iterate on your queries.
Recommendations to others considering the product:
Make sure your data is mostly numerical, this is where you will be seeing the most benefits of using QuasarDB.