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Version: v1.1.0

The Zed Project

Zed offers a new approach to data that makes it easier to manipulate and manage your data.

With Zed's new super-structured data model, messy JSON data can easily be given the fully-typed precision of relational tables without giving up JSON's uncanny ability to represent eclectic data.

Getting Started

Trying out Zed is easy: just install the command-line tool zq and run through the zq tutorial.

zq is a lot like jq but is built from the ground up as a search and analytics engine based on the Zed data model. Since Zed data is a proper superset of JSON, zq also works natively with JSON.

While zq and the Zed data formats are production quality, the Zed project's Zed data lake is a bit earlier in development.

For a non-technical user, Zed is as easy to use as web search while for a technical user, Zed exposes its technical underpinnings in a gradual slope, providing as much detail as desired, packaged up in the easy-to-understand ZSON data format and Zed language.


"Zed" is an umbrella term that describes a number of different elements of the system:

  • The Zed data model is the abstract definition of the data types and semantics that underlie the Zed formats.
  • The Zed formats are a family of sequential (ZNG), columnar (ZST), and human-readable (ZSON) formats that all adhere to the same abstract Zed data model.
  • A Zed lake is a collection of optionally-indexed Zed data stored across one or more data pools with ACID commit semantics and accessed via a Git-like API.
  • The Zed language is the system's dataflow language for performing queries, searches, analytics, transformations, or any of the above combined together.
  • A Zed query is a Zed script that performs search and/or analytics.
  • A Zed shaper is a Zed script that performs data transformation to shape the input data into the desired set of organizing Zed data types called "shapes", which are traditionally called schemas in relational systems but are much more flexible in the Zed system.

Digging Deeper

The Zed language documentation is the best way to learn about zq in depth. All of its examples use zq commands run on the command line. Run zq -h for a list of command options and online help.

The Zed Lake documentation is the best way to learn about zed. All of its examples use zed commands run on the command line. Run zed -h or -h with any subcommand for a list of command options and online help. The same language query that works for zq operating on local files or streams also works for zed query operating on a lake.

Design Philosophy

The design philosophy for Zed is based on composable building blocks built from self-describing data structures. Everything in a Zed lake is built from Zed data and each system component can be run and tested in isolation.

Since Zed data is self-describing, this approach makes stream composition very easy. Data from a Zed query can trivially be piped to a local instance of zq by feeding the resulting Zed stream to stdin of zq, for example,

zed query "from pool | ...remote query..." | zq "...local query..." -

There is no need to configure the Zed entities with schema information like protobuf configs or connections to schema registries.

A Zed lake is completely self-contained, requiring no auxiliary databases (like the Hive metastore) or other third-party services to interpret the lake data. Once copied, a new service can be instantiated by pointing a zed serve at the copy of the lake.

Functionality like indexing, data compaction, and retention are all API-driven.

Bite-sized components are unified by the Zed data, usually in the ZNG format:

  • All lake meta-data is available via meta-queries.
  • All like operations available through the service API are also available directly via the zed command.
  • Search indexes and aggregate partials are all just ZNG files and you can learn about the Zed lake by simply running zq on the various ZNG files in a cloud store.
  • Lake management is agent-driven through the API. For example, instead of complex policies like data compaction being implemented in the core with some fixed set of algorithms and policies, an agent can simply hit the API to obtain the meta-data of the objects in the lake, analyze the objects (e.g., looking for too much key space overlap) and issue API commands to merge overlapping objects and delete the old fragmented objects, all with the transactional consistency of the commit log.
  • Components are easily tested and debugged in isolation.