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Summary
Together with our global community of contributors, GreptimeDB continues to evolve and flourish as a growing open-source project. We are grateful to each and every one of you.
Below are the highlights among recent commits:
Introduced Flow Engine to provide continuous aggregation capabilities.
Expressions can be used in Range Query, such as
RANGE (INTERVAL '2' day - INTERVAL '1' day).Support for using
EXPLAIN ANALYZEin distributed mode to analyze query execution costs.
Contributors
For the past two weeks, our community has been super active with a total of 86 PRs merged. 12 PRs from 8 individual contributors merged successfully, and lots are pending to be merged.
Congrats on becoming our most active contributors in the past 2 weeks:
👏 Welcome contributor @groobyming @ltratt @tizee join to the community as the new individual contributor, and congratulations on successfully merging their first PR, more PRs are waiting to be merged.
A big THANK YOU to all our members and contributors! It is people like you who are making GreptimeDB a great product. Let's build an even greater community together.
Highlights of Recent PRs
db#3823 Using expressions in Range Query
Now, the Range Query has new support for the following features:
Supporting Interval type calculations in the Range and Align parameters. For example, you can use
(INTERVAL '2' day - INTERVAL '1' day)to compute time intervals.Specifying the time origin for alignment using expressions. For instance,
(now() - INTERVAL '1' hour)can be used to align the query to one hour before the current time.
sql
SELECT
ts,
min(val) RANGE (INTERVAL '2' day - INTERVAL '1' day)
FROM
host
ALIGN (INTERVAL '2' day - INTERVAL '1' day)
TO (now() - INTERVAL '1' hour)
ORDER BY ts;These enhancements allow users to define time ranges and alignment points more flexibly, enabling more precise time-series data analysis.
Range Query is an extension syntax of GreptimeDB. For more information: https://docs.greptime.com/reference/sql/range
db#3908 Supporting EXPLAIN ANALYZE in a distributed mode
In distributed mode, GreptimeDB now supports EXPLAIN ANALYZE, which executes the corresponding SQL statement. By running EXPLAIN ANALYZE, users can gain detailed insights into the performance of distributed queries, including execution time and resource utilization metrics. This feature is crucial for optimizing and troubleshooting complex queries in distributed environments.
java
explain analyze SELECT count(*) FROM system_metrics;
+-------+------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| stage | node | plan |
+-------+------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 0 | 0 | MergeScanExec: peers=[4402341478400(1025, 0), ] metrics=[output_rows: 1, greptime_exec_read_cost: 0, ready_time: 6352264, finish_time: 7509279, first_consume_time: 7165836, ] |
| | | |
| 1 | 0 | AggregateExec: mode=Final, gby=[], aggr=[COUNT(greptime.public.system_metrics.ts)] metrics=[output_rows: 1, elapsed_compute: 108029, ] |
| | | CoalescePartitionsExec metrics=[output_rows: 32, elapsed_compute: 83055, ] |
| | | AggregateExec: mode=Partial, gby=[], aggr=[COUNT(greptime.public.system_metrics.ts)] metrics=[output_rows: 32, elapsed_compute: 334913, ] |
| | | RepartitionExec: partitioning=RoundRobinBatch(32), input_partitions=1 metrics=[repart_time: 1, fetch_time: 441565, send_time: 30325, ] |
| | | StreamScanAdapter { stream: "<SendableRecordBatchStream>" } metrics=[output_rows: 3, mem_used: 24, ] |
| | | |
| | | Total rows: 1 |
+-------+------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+db#3923 Migrated the dependency from orc-rs to datafusion-orc
Earlier, we donated orc-rs to the datafusion-contrib community and maintained it together with the community. Recently, the main repository's dependency on orc-rs has been migrated to datafusion-orc.
db#3932 Fuzz testing now includes validation of inserted data
In previous work, we added fuzz testing for data writes. In this PR, we further enhanced it by adding validation for the data that has been written to ensure it meets the expected criteria.
Good First Issue
db#3997 Conduct fuzz testing for GreptimeDB clusters using shared storage
In db#3967, we ran GreptimeDB clusters and fuzz tests in CI. To better simulate real-world usage, we want to run fuzz testing for GreptimeDB clusters using shared storage in CI.
Keywords: CI
Difficulty: Medium
db#3973 Add fuzz testing for the column type change feature
In db#3517, we supported altering the column data type. We need to add corresponding fuzz tests for this feature.
Keywords: Fuzz testing
Difficulty: Medium
db#3884 Remove unnecessary traits and wrapper types from the query crate
The manifest doesn't have any checksum for data validation. We need a way to do the checksum validation for region manifests. A possible way is to save the checksum as the part of manifest file name, for example, 000000000001-{checksum}.json.
Most implementations simply forward requests to Datafusion. Since we are highly coupled with Datafusion and have no plans to support another query engine, we can remove these types.
Keywords: Refactoring
Difficulty: Simple
About Greptime
We help industries that generate large amounts of time-series data, such as Connected Vehicles (CV), IoT, and Observability, to efficiently uncover the hidden value of data in real-time.
Visit the latest version to get started and get the most out of your data.
GreptimeDB, written in Rust, is a distributed, open-source time-series database designed for unlimited horizontal scaling, high performance, and integrated analytics. We provide GreptimeDB Enterprise for corporate users which supports more enterprise features and customized services. Contact us here for more information.
GreptimeCloud is a fully-managed cloud database-as-a-service (DBaaS) solution built on GreptimeDB. It efficiently supports applications in fields such as observability, IoT, and finance. The built-in observability solution, GreptimeAI, helps users comprehensively monitor the cost, performance, traffic, and security of LLM applications.
The Vehicle-Cloud Integrated TSDB is a finely tailored solution that aligns closely with the specific business scenarios of automotive companies, addressing the challenges posed by the exponential growth of vehicle data. The multimodal vehicle-side database, combined with the cloud-based GreptimeDB Enterprise, greatly reduces traffic, computing, and storage costs, and boosts data timeliness and business insight capabilities.
If anything above draws your attention, don't hesitate to star us on GitHub or join GreptimeDB Community on Slack. Also, you can go to our contribution page to find some interesting issues to start with.
