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<img src="./docs/images/logos/trickster-logo.svg" width=90 /> <img src="./docs/images/logos/trickster-text.svg" width=420 />

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 OTLP 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.

Compatibility

Trickster works with virtually any Dashboard application that makes queries to any of these TSDB's:

<img src="./docs/images/external/prom_logo_60.png" width=16 /> Prometheus

<img src="./docs/images/external/clickhouse_logo.png" width=16 /> ClickHouse

<img src="./docs/images/external/influx_logo_60.png" width=16 /> InfluxDB

<img src="./docs/images/external/irondb_logo_60.png" width=16 /> Circonus IRONdb

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.

Installing

Docker

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.

Kubernetes

See the 'deploy' Directory for Kube and deployment files and examples.

Helm

Trickster Helm Charts are located at https://helm.tricksterproxy.io for installation, and maintained at https://github.com/trickstercache/helm-charts. We welcome chart contributions.

Building from source

To build Trickster from the source code yourself you need to have a working Go environment with version 1.23 or greater installed.

You can directly use the go tool to download and install the trickster binary into your GOPATH:

    $ 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 make:

    $ 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
    $ ./bin/trickster -origin-url http://prometheus.example.com:9090 -provider prometheus

The Makefile provides several targets, including:

build: build the trickster binary
docker: build a docker container for the current HEAD
clean: delete previously-built binaries and object files
test: runs unit tests
bench: runs benchmark tests
rpm: builds a Trickster RPM

More information

Refer to the docs directory for additional info.

Contributing

Refer to CONTRIBUTING.md

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