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SuperDB

SuperDB offers a new approach that makes it easier to manipulate and manage your data. With its 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 SuperDB is easy: just install the command-line tool super and run through the tutorial.

Compared to putting JSON data in a relational column, the super-structured data model makes it really easy to mash up JSON with your relational tables. The super command is a little like DuckDB and a little like jq but super-structured data ties the two patterns together with strong typing of dynamic values.

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

While super and its accompanying data formats are production quality, the project's SuperDB data lake is a bit earlier in development.

Terminology​

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

Digging Deeper​

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

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

Design Philosophy​

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

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

super db query "from pool | ...remote query..." | super "...local query..." -

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

A SuperDB data 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 super db serve at the copy of the lake.

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

Bite-sized components are unified by the super-structured data, usually in the SUPZ format:

  • All lake meta-data is available via meta-queries.
  • All lake operations available through the service API are also available directly via the super db command.
  • 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.