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JSON based document database

Project description


JSON Key-Value store in pure Python 3


JSONDB is a library for Python 3 that provides the ability to run a very simplified CouchDB-like document database, a.k.a. a Key-Value store. The features include:

  • Hard disk storage of documents

  • In-memory storage of indexes

  • Map and reduce functions specified in Python directly

  • Any number of views per database

  • Views can be accessed with or without reducing them

  • Thread-safe (with locks per database)


You can pip (python 3) install this Github repository or a tag, like this:

$pip install

This will also install blist which is used to get the views faster.


To create a new database (a table if you think in relation database terms):

>>> from lindh.jsondb import Database
>>> db = Database('/tmp/cars')
>>> db.clear() # for doctest purposes

This will create a folder /tmp/cars which will be used to store the documents (json files) and an ID counter.

To populated the database with some content you can use These documents will be given a unique id automatically. If you just want to retrieve them using indices, this is not a problem, but if you want control over the identifiers, you can do like this instead:

>>> db[0] = {'brand': 'Volvo', 'model': 'S40', 'wheels': 6}
>>> db[1] = {'brand': 'Mercedes', 'model': 'C', 'wheels': 8}
>>> db[2] = {'brand': 'Volvo', 'model': 'V70', 'wheels': 4}
>>> db[3] = {'brand': 'Honda', 'model': 'CB500F', 'wheels': 2}

This enables you to retrieve them back in the expected pythonic way.

The documents are stored synchronously, so your app may be restarted without data loss.

Let’s look at an interactive session to find out what the document looks like when it comes back:

>>> db[0] == {'wheels': 6, '_id': 0, '_rev': 0, 'brand': 'Volvo', 'model': 'S40'}

As you can see, the structure closely mimic that of CouchDB, with the _id and _rev fields. The _rev field is important to keep intact as updated requires it to be the latest (otherwise a lindh.jsondb.Conflict is raised). To update, it’s quite easy to use save (but index-based setting also works):

>>>{'wheels': 6, '_id': 0, '_rev': 0, 'brand': 'Volvo', 'model': 'S40', 'color': 'white'}) == \
... {'wheels': 6, '_id': 0, '_rev': 1, 'brand': 'Volvo', 'model': 'S40', 'color': 'white'}

The _rev should change here, usually pop one number up (whereas CouchDB would return random hashes for each revision).

To delete a document you can simple use del db[key] or db.delete(key).


What fun is a Key-Value store with no indexing? Not much!

>>> db.define('by_wheels', lambda o: (o['wheels'], ' '.join([o['brand'], o['model']])))
>>> list(db.view('by_wheels'))[0] == \
... {'id': 3, 'key': 2, 'value': 'Honda CB500F'}

So we defined a view called by_wheels where the number of wheels is used as key and a concatenation of brand and model is used as value. The view is always sorted so I know that the motorcycle will come out first. The rest of the order is somewhat arbitrary since a binary search tree is used to hold the index in memory.

Note that the index is available as soon as it is created. This is because the operation of defining an index is asynchronous. It does not matter if the view is defined before or after the documents are created, as the documents will be placed in the index ad hoc. They will also be deleted that way. This means, for performance:

  • Adding a document is O(log n)

  • Finding a document is O(log n)

  • Deleting a document is O(log n)

So this scales quite well as long as the index fits in memory (the actual documents do not need to fit in memory, however). By the nature of being a binary search tree, it is constantly sorted by key.

Now, this takes us to the sorting. To further mimic CouchDB, keys need to be sortable beyond the core functionality of python. Anything needs to be comparable with anything basically. Also, we need something to be smaller and bigger than everything else, respectively. These are None and any.

Lets revisit the by_wheels view, and take everything with equal to or more than 6 wheels (I know this is not accurate data).

>>> list(db.view('by_wheels', startkey=6, endkey=any)) == \
... [{'id': 0, 'key': 6, 'value': 'Volvo S40'},{'id': 1, 'key': 8, 'value': 'Mercedes C'}]

The reason to use list() here is because I’m always given a generator back.

More on Views

A number of keyword arguments can be passed to the view(...) method:

  • key specifies a single key (which can give 0 to many values)

  • startkey specifies an inclusive starting point. Can be a tuple.

  • endkey specifies and inclusive ending point. Can be a tuple.

  • include_docs, if True, the document that rendered this index post is included under doc.

  • group, if True and a reduce function is specified as a third argument to the define method, the result will be the reduced data rather than the mapped.

  • no_reduce, if there is a reduce function, but you don’t want to use it this time, set this to True and leave group as False.

  • skip, an integer offset (defaults to 0)

  • limit, an integer page size (set to None for no limit)

For more information about reduce functions please see the CouchDB documentation. The big differences are:

  • Group levels are not supported. Grouping is always done on the deepest level (meaning all elements in a tuple key).

  • Re-reduce is never done. But. The reduce function nevertheless expects f(keys, values, rereduce). This potentially leads to scaling issues but I have not run into them yet.

Further Reading

  • The lib is developed mainly for the Images6 project, found at This means it’s full of usage examples. Look into images6/ for instance to see how the views are set up.

  • Also the lib works quite well together with its sister, lindh-jsonobject which is a Django-inspired serialization/deserialization lib for complex python objects and json. It can be found here:


lindh.jsondb is written and maintained by Johan Egneblad <>.

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