Nerium 0.3

The little business intelligence engine that could

Nerium

A simple aiohttp microservice that submits queries to a database and returns machine-readable serialized results (typically JSON). By analogy with static site generators, Nerium reads its queries from local files, stored in a (configurable) directory on the filesystem. The idea is that report analysts should be able to author queries in their preferred local editor, and upload or mount them where Nerium can use them.

OAO uses Nerium to easily and quickly provide JSON APIs with report results from our PostgreSQL data warehouse.

Nerium features an extendable architecture, allowing support for multiple query types and output formats.

Currently supports SQL queries using the excellent Records library. In keeping with Records usage, query parameters can be specified in key=value format, and (safely!) injected into your query in :key format.

In theory, other query types can be added (under nerium/resultset) for non-SQL query languages. This is a promising area for future development.

Default JSON output is an array of objects, one per result row, with database column names as keys. 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 (not necessarily JSON) can be added with new plugin modules under nerium/formatter.

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 oaodev/nerium. (To ease installation, options for nerium[mysql] and nerium[pg] are provided in setup.py)

Nerium is inspired in roughly equal measure by SQueaLy and Pelican. It hopes to be something like Superset when it grows up.

Install/Run

Using Docker

$ docker run -d --name=nerium \
--envfile=.env \
-v /local/path/to/query_files:/app/query_files \
-p 8080:8080 oaodev/nerium

$ curl http://localhost:8081/v1/<query_name>?<params>

Local install

pipenv install nerium[pg]

Then add a query_files directory to your project, write your queries, and configure the app as described in the next section. The command nerium starts a local aiohttp server running the app, listening on port 8080.

Configuration

DATABASE_URL and optional QUERY_PATH (directory where query files reside, defaults to query_files in the working directory) may be set in the environment, or in a local .env file. This is the simplest configuration option.

In order to query multiple databases with a single instance of Nerium, create a subdirectory for each database under the $QUERY_PATH, place the related files under their respective directory, and include a separate db.yaml file per subdirectory, which may define a database or database_url key. (The method of naming the subdirectories to match database names still works for now, but should be considered deprecated.)

Separate database connections can also be specified directly in individual query files, by defining a database or database_url key in the YAML front matter (see below).

Query files and front matter

As indicated above, queries are simply text files placed in local query_files directory, or an other arbitrary file system location specified by QUERY_PATH in the environment.

Query files can optionally include a YAML front matter block. The front matter goes at the top of the file, set off by triple-dashed lines, as in this example:

---
Author: Joelle van Dyne
Description: Returns all active usernames in the system
---

At present, the Nerium service doesn't do much with the front matter. As noted, it can be used to specify a database connection for the query. For other keys, the default response format simply passes the keys and values along in a metadata object. (All other current formatters simply ignore the metadata.) This mechanism can theoretically be used to pass relevant information about the query along to any clients of the service: for example, the data types of the columns in the results or what have you. Possibilities include whatever a reporting service and front end developer want to coordinate on. Front matter could also be used in more detailed ways in formatters yet to be devised.

Plugin Architecture

As of v0.3, Nerium no longer includes ResultSet and ResultFormatter abstract base classes, nor a contrib package, as the project has been refactored to use a more functional style. Additional (non-SQL) query types and formats can still be added, by putting new modules under nerium/resultset and nerium/formatter, respectively.

resultset

A resultset module is expected to have a result method that takes a query object and optional keyword argument (kwargs) dictionary, connects to a data source, and returns tabular results as a serializable Python structure (most typically a list of dictionaries). A Nerium query object is a munchified dictionary, with elements found in get_query(). Query files to be passed to this module should be named with a file extension that matches the module name (for example, foo.sql will be handed to the resultset/sql.py module).

formatter

A formatter module requires a format_results method that takes a result object, rearranges or supplements the original structure as desired, and returns the new structure for serialization to the web service output. Formatter objects are invoked at runtime by passing a ne_format parameter to the Nerium URL, which should match the name of the formatter module (see API below).

API

URLs

/v1/<string:query_name>/?[ne_format=<formatter>]&<query_params>

query_name should match the name of a given query script file, minus the file extension. Params are as specified in the queries themselves.

ne_format may be passed as in the query string as an optional formatter name, and defaults to 'default'. Other supported formatter options are described in Content section below.

Unknown values passed to query_extension or format will silently fall back to defaults.

Method

GET

Success Response

Code: 200

Content:
'default': {"query_name": "<query_name>", "data": [{<column_name>:<row_value>, etc..., }, {etc...}, ], "metadata": {<key>: <value>, etc..., }}
'compact': {"columns": [<list of column names>], "data": [<array of row value arrays>]}
'affix': {"error": false, "response": {<'default' array of result objects>}, "metadata":{"executed": <timestamp>, "params": {<array of name-value pairs submitted to query with request>}}} 'csv': <csv formatted string (w \r\n newline)>
'sum': {"error": false, "response": {"summary": <array of row dicts having grouping > 0>, "result": <array of row dicts having grouping = 0>}, "metadata":{"executed": <timestamp>, "params": {<array of name-value pairs submitted to query with request>}}}

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>}

Sketchy Roadmap/TODOs

(in no particular order)

• More detailed documentation, especially about usage
• Parameter discovery endpoint
• Report listing endpoint
• Plugin architecture
• Dynamic filtering without jinja-sql
• Improve/mature plugin architecture
  • Separate base classes to a library
  • Implementation subclasses in contrib package
  • Refactor plugin approach to use modules with an interface standard, instead of abstract class inheritance
• Configurable/flexible JSON output formatters (AffixFormatter could do with less hard-coding) [WONTFIX]
• Static output file generator (and other caching)
• Swagger docs
• Health check/default query endpoint (Own git commit hash report(?))
• Convert app.py to Responder