In addition to Python and Node.js, GlareDB binaries are distributed through GitHub.

There are two ways to use GlareDB:

  • locally, with optional disk persistence. In this scenario, queries are executed entirely in-process, and data exists in-memory or on-disk.

  • hybrid, with GlareDB Cloud. In this scenario, queries are partitioned and optimized to run both in-process and using cloud compute. Data can be accessed and stored in cloud. When using GlareDB Cloud, you can access your data in all of your applications.


The GlareDB binary can be installed through a simple script in the current directory:

curl | sh

Alternatively, you can download the appropriate binary from GitHub.

The installation consists of just a single artifact, a binary called glaredb. Application data is stored in ~/.glaredb. Temporary files may be created in standard locations like /tmp (for more information, see here).


The CLI can be used in scripts or interactively. It is very convenient for small-scale ETL operations, local exploratory analysis and as a shell for GlareDB Cloud.

Each of these is covered below. To see the full list of options, run:

glaredb --help

Local Exploratory Analysis

To start an interactive session, run glaredb. From here, SQL statements are executed within a REPL, complete with syntax highlighting and support.

$ glaredb
Type \help for help.

Data sources (local and remote) can be analyzed and joined with ease:

>  SELECT * FROM './report.csv' r
::: JOIN read_excel('./my_workbook.xlsx') e
::: ON =
::: WHERE r.output > 5;

GlareDB Cloud shell

After signing up for GlareDB Cloud, you can connect to your deployment from the CLI either:

  • by passing --cloud-url (glaredb --cloud-url glaredb://user:pass@host:port/database)
  • or, if already in an interactive session, using \open:
$ glaredb
Type \help for help.
> \open glaredb://user:pass@host:port/database

Once a connection is opened to a cloud instance, all of its tables and data can be accessed while also being able to access local files. Furthermore, queries are optimized and partitioned to run using both local compute and cloud compute.

Small-Scale ETL

COPY TO is a powerful feature that bridges the above use cases together. It can be used to:

  • Pull data from cloud, or other remote sources
  • Interchange between data formats
  • Push data from local to cloud or other remote sources

As an example, imagine you have a table in GlareDB Cloud called my_cloud_table, and you want to perform some exploration on a subset of rows in the table. The following query creates a local file explore.csv with the desired rows.

    SELECT * FROM my_cloud_table
    WHERE column_a > 10
    LIMIT 1000;
TO './explore.csv';

After analyzing and modifying the data, COPY TO can be used to save results and even interchange the format (in this case, from CSV to Parquet):

COPY (SELECT * FROM './explore.csv') TO s3 FORMAT parquet OPTIONS (
  access_key_id = '<aws_access_key_id>',
  bucket = '<bucket>',
  location = 'explore.parquet',
  region = '<aws_region>',
  secret_access_key = '<aws_secret_access_key>'

Refer to the documentation for COPY TO for further specification and examples.