Trickster is an HTTP reverse proxy/cache for http applications and a dashboard query accelerator for time series databases.
Learn more below, and check out our roadmap to find out what else is in the works.
Trickster is hosted by the Cloud Native Computing Foundation (CNCF) as a sandbox level project. If you are a company that wants to help shape the evolution of technologies that are container-packaged, dynamically-scheduled and microservices-oriented, consider joining the CNCF.
Note: Trickster v1.1 is the production release, sourced from the v1.1.x branch. The
main branch sources Trickster 2.0, which is currently in beta.
HTTP Reverse Proxy Cache
Trickster is a fully-featured HTTP Reverse Proxy Cache for HTTP applications like static file servers and web API's.
Proxy Feature Highlights
- A unique and powerful Application Load Balancer for Time Series and generic HTTP endpoints
- Supports TLS and HTTP/2 for frontend termination and backend origination
- Offers several options for a caching layer, including in-memory, filesystem, Redis and bbolt
- Highly customizable, using simple yaml configuration settings, down to the HTTP Path
- Built-in Prometheus metrics and customizable Health Check Endpoints for end-to-end monitoring
- Negative Caching to prevent domino effect outages
- High-performance Collapsed Forwarding
- Best-in-class Byte Range Request caching and acceleration.
- Distributed Tracing via OpenTelemetry, supporting Jaeger and Zipkin
- Rules engine for custom request routing and rewriting
Time Series Database Accelerator
Trickster dramatically improves dashboard chart rendering times for end users by eliminating redundant computations on the TSDBs it fronts. In short, Trickster makes read-heavy Dashboard/TSDB environments, as well as those with highly-cardinalized datasets, significantly more performant and scalable.
Trickster works with virtually any Dashboard application that makes queries to any of these TSDB's:
See the Supported TSDB Providers document for full details
How Trickster Accelerates Time Series
1. Time Series Delta Proxy Cache
Most dashboards request from a time series database the entire time range of data they wish to present, every time a user's dashboard loads, as well as on every auto-refresh. Trickster's Delta Proxy inspects the time range of a client query to determine what data points are already cached, and requests from the tsdb only the data points still needed to service the client request. This results in dramatically faster chart load times for everyone, since the tsdb is queried only for tiny incremental changes on each dashboard load, rather than several hundred data points of duplicative data.
2. Step Boundary Normalization
When Trickster requests data from a tsdb, it adjusts the clients's requested time range slightly to ensure that all data points returned are aligned to normalized step boundaries. For example, if the step is 300s, all data points will fall on the clock 0's and 5's. This ensures that the data is highly cacheable, is conveyed visually to users in a more familiar way, and that all dashboard users see identical data on their screens.
3. Fast Forward
Trickster's Fast Forward feature ensures that even with step boundary normalization, real-time graphs still always show the most recent data, regardless of how far away the next step boundary is. For example, if your chart step is 300s, and the time is currently 1:21p, you would normally be waiting another four minutes for a new data point at 1:25p. Trickster will break the step interval for the most recent data point and always include it in the response to clients requesting real-time data.
Trying Out Trickster
Check out our end-to-end Docker Compose demo composition for a zero-configuration running environment.
Docker images are available on Docker Hub:
$ docker run --name trickster -d -v /path/to/trickster.yaml:/etc/trickster/trickster.yaml -p 0.0.0.0:8480:8480 trickstercache/trickster
See the 'deploy' Directory for more information about using or creating Trickster docker images.
See the 'deploy' Directory for Kube and deployment files and examples.
Building from source
To build Trickster from the source code yourself you need to have a working Go environment with version 1.17 or greater installed.
You can directly use the
go tool to download and install the
binary into your
$ go get github.com/trickstercache/trickster/cmd/trickster # this starts a prometheus accelerator proxy for the provided endpoint $ trickster -origin-url http://prometheus.example.com:9090 -provider prometheus
You can also clone the repository yourself and build using
$ mkdir -p $GOPATH/src/github.com/trickstercache $ cd $GOPATH/src/github.com/trickstercache $ git clone https://github.com/trickstercache/trickster.git $ cd trickster $ make build $ ./OPATH/trickster -origin-url http://prometheus.example.com:9090 -provider prometheus
The Makefile provides several targets, including:
- build: build the
- docker: build a docker container for the current
- clean: delete previously-built binaries and object files
- test: runs unit tests
- bench: runs benchmark tests
- rpm: builds a Trickster RPM
- Refer to the docs directory for additional info.
Refer to CONTRIBUTING.md
Who Is Using Trickster
As the Trickster community grows, we'd like to keep track of who is using it in their stack. We invite you to submit a PR with your company name and @githubhandle to be included on the list.
- Comcast [@jranson]
- Selfnet e.V. [@ThoreKr]
- swarmstack [@mh720]
- Hostinger [@ton31337]
- The Remote Company (MailerLite, MailerSend, MailerCheck, YCode) [@aorfanos]
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