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The little business intelligence engine that could.

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A lightweight Flask-based microservice that submits queries to any SQLAlchemy-supported database, and returns results as JSON or CSV. By analogy with static site generators, Nerium reads its queries and serialization formats from local files, stored on the filesystem. The idea is that report analysts should be able to write queries in their preferred local editor, and upload or mount them where Nerium can use them.

Nerium provides a quick, simple, and easy way to develop JSON APIs for use in reporting and analytic applications. In keeping with SQLAlchemy usage, query parameters can be specified in key=value format, and (safely!) injected into your query in :key format.

Default JSON output represents data as an array of objects, one per result row, with database column names as keys. The default schema also provides top-level nodes for name, metadata, and params (details below). A compact JSON output format may also be requested, with separate column (array of column names) and data (array of row value arrays) nodes for compactness. Additional formats can be added by adding marshmallow schema definitions to format_files.

Nerium supports any backend that SQLAlchemy can, but since none of these are hard dependencies, drivers aren't included in Pipfile, and the Dockerfile only supports PostgreSQL. If you want Nerium to work with other databases, you can install Python connectors with pip, either in a virtualenv or by creating your own Dockerfile using FROM tymxqo/nerium. (To ease installation, options for nerium[mysql] and nerium[pg] are provided in


Using Docker

docker run -d --name=nerium \
--envfile=.env \
-v /local/path/to/query_files:/app/query_files
-v /local/path/to/format_files:/app/format_files \
-p 5000:5000 tymxqo/nerium

curl http://localhost:5000/v1/<query_name>?<params>

You might also want to use tymxqo/nerium as a base image for your own custom container, in order to add different database drivers, etc. Or you can build locally from the included Dockerfile. The base image includes psycopg2 PostgreSQL adapter, along with gunicorn WSGI server for a production-ready service.

Local install

pipenv install nerium[pg]

Or install latest source from Github:

git clone
cd nerium
pipenv install --dev

Then add a query_files (and, optionally, format_files) directory to your project, write your queries, and configure the app as described in the next section. The command FLASK_APP=nerium/ flask run starts a local development server running the app, listening on port 5000. For production use, you will want to add a proper WSGI server (we like gunicorn).


DATABASE_URL for query connections must be set in the environment (or in a local .env file). This is the simplest configuration option.

Script file paths

By default, Nerium looks for query and format schema files in query_files and format_files respectively, in the current working directory from which the service is launched. QUERY_PATH and FORMAT_PATH environment variables can optionally be set in order to use files from other locations on the filesystem, as desired.

Multiple Data Sources

If you want to query multiple databases from a single Nerium installation, any individual query file can define its own database_url as a key in YAML front matter (see below). This will override the $DATABASE_URL setting in the environment for that query only. If you have a large number of queries across several databases, keep in mind that running a separate Nerium instance for each database is always an option.


Query files

As indicated above, queries are simply text files placed in a local query_files directory, or another arbitrary filesystem location specified by QUERY_PATH in the environment. The base name of the file (stem in Python pathlib parlance) will determine the {query_name} portion of the matching API endpoint.

Query parameters

Use :<param> to specify bind parameters in your query text. Clients can then specify values for these bind parameters in their results request, passed either as JSON or query string arguments.


Query files can optionally include a YAML metadata block. The use of a special comment for metadata allows for the SQL file to be used as-is in other SQL clients. To add this metadata, create a multiline comment surrounded by \* ... */ markers, and include the label :meta at the top. Within this comment, surround the YAML document with standard triple-dashed lines, as in this example:

/* :meta
Author: Joelle van Dyne
Description: Returns all active usernames in the system
select username from user;

Metadata can generally be thought of as a way to pass arbitrary key-value pairs to a front-end client; in the default format, the metadata is simply returned in the results response. (The compact formatter drops the metadata.) Other possible use cases include whatever a reporting service and front-end developer want to coordinate on.

There are a couple of special-case metadata items:

  1. As noted above, it can be used to specify a database connection for the query, overriding the main DATABASE_URL in the environment
  2. If the metadata includes a params block, its contents are returned as the params object in the v1/reports/ discovery response.
  3. Similarly, metadata describing columns will populate that section of the /reports/ response.

In the absence of explicit metadata, Nerium attempts to find column specifications and named parameters by inspecting the query text itself. Although it is more manual, a metadata comment can provide greater detail in these sections — a report developer might specify the data type of a column or parameter, for example, in addition to its name.

Jinja templating

Nerium supports Jinja templating syntax in queries. The most typical use case would be for adding optional filter clauses to a query so that the same SELECT statement can be reused without having to be repeated in multiple files, for example:

select username
     , user_id
     , display_name
  from user
{% if group %}
 where = :group
{% endif %}

Jinja filters and other logic can be applied to inputs, as well.


The Jinja template is rendered in a SandboxedEnvironment, which should protect against server-side template injection and most SQL injection tactics. It should not be considered perfectly safe, however. Use this feature sparingly; stick with SQLAlchemy-style :key named parameters for bind value substitutions, and test your specific queries carefully. It should almost go without saying that database permission grants to the user Nerium connects as should be well-restricted, whether one is using Jinja syntax or not.

One known dangerous case is if your entire query file just does a Jinja variable expansion and nothing else: {{ my_whole_query }}. This will allow execution of arbitrary SQL and you should never make a template like this available.

Custom format files

For serialization formats besides the built-in default and compact, schema definitions can be added to your format_files directory, using the Python marshmallow library. Similarly to query files, the app will look for a format module name matching the {format} specified in the endpoint URL. The app expects a marshmallow.Schema subclass named ResultSchema. Available attributes passed to this schema are all found in the original query object. (See nerium/schema for examples of how this is done by built-in formats.)


Report listing endpoint


  • /v2/reports



Success Response

Code: 200

Content: {"reports": [<an array of report names>]}

Report description endpoints


  • /v2/reports/{string:query_name}



Success Response

{"columns":[<list of columns from report>],"error":false,"metadata":{<report: metadata object>},"name":"<query_name>","params":[<array of parameterized keys in query>],"type":"sql"}

Results endpoints


  • /v1/<string:query_name>?<query_params>
  • /v1/<string:query_name>/<string:format>?<query_params>
  • /v2/results/
  • /v2/results/<string:query_name>?<query_params>
  • /v2/results/<string:query_name>/<string:format>?<query_params>

[v1 endpoints are deprecated and will be removed eventually]

As shown above query_name and format may be accessed as part of the URL structure, or can be passed as parameters to the request.

Because we're retrieving report results here, the request is a GET in any case, but parameters may be sent in a JSON body or as querystring parameters. Note that query_name and format from URL base path will be preferred, even if a request to such a path happens to include either key in the request body (client apps should avoid doing this to avoid confusion).

query_name should match the name of a given query script file, minus the file extension. URL querystring parameters (or JSON keys other than query_string and format) are passed to the invoked data source query, matched to any parameter keys specified in the query file. If any parameters expected by the query are missing, an error will be returned. Extra/unrecognized parameters are silently ignored (this might seem surprising, but it's standard SQLAlchemy behavior for parameter substitution).

format path may be included as an optional formatter name, and defaults to 'default'. Other supported formatter options are described in Content section below.

Unknown values of format will silently fall back to default.



Success Response

Code: 200


'default': {"name": "<query_name>", "data": [{<column_name>:<row_value>, etc..., }, {etc...}, ], "metadata": {<key>: <value>, etc..., }, "params": {<array of name-value pairs submitted to query with request>}}
'compact': {"columns": [<list of column names>], "data": [<array of row value arrays>]} 'csv': <csv formatted string (with \r\n newline)>

Of course, it is possible that a database query might return no results. In this case, Nerium will respond with an empty JSON array [] regardless of specified format. This is not considered an error, and clients should be prepared to handle it appropriately.

Error Responses

Code: 400

Content: {"error": <exception.repr from Python>}

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