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Generate ES Indexes, load and extract data, based on JSON Table Schema descriptors.

Project description


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Generate and load ElasticSearch indexes based on Table Schema descriptors.


  • implements tableschema.Storage interface


Getting Started


The package use semantic versioning. It means that major versions could include breaking changes. It's highly recommended to specify package version range in your setup/requirements file e.g. package>=1.0,<2.0.

pip install tableschema-elasticsearch


Code examples in this readme requires Python 3.3+ interpreter. You could see even more example in examples directory.

import elasticsearch
import jsontableschema_es

INDEX_NAME = 'testing_index'

# Connect to Elasticsearch instance running on localhost

# List all indexes

# Create a new index
storage.create('test', [
         'fields': [
                 'name': 'num',
                 'type': 'number'

# Write data to index
l=list(storage.write(INDEX_NAME, 'numbers', ({'num':i} for i in range(1000)), ['num']))
print(l[:10], '...')

l=list(storage.write(INDEX_NAME, 'numbers', ({'num':i} for i in range(500,1500)), ['num']))
print(l[:10], '...')

# Read all data from index


The whole public API of this package is described here and follows semantic versioning rules. Everyting outside of this readme are private API and could be changed without any notification on any new version.


Package implements Tabular Storage interface (see full documentation on the link):


This driver provides an additional API:


  • es (object) - elasticsearch.Elastisearc instance. If not provided new one will be created.

In this driver elasticsearch is used as the db wrapper. We can get storage this way:

from elasticsearch import Elasticsearch
from jsontableschema_sql import Storage

engine = Elasticsearch()
storage = Storage(engine)

Then we could interact with storage ('buckets' are ElasticSearch indexes in this context):

storage.buckets # iterator over bucket names
storage.create('bucket', [(doc_type, descriptor)],
        # doc_type can be None in case mapping_types are not supported (ES version >= 7.0.0)
        # reindex will copy existing documents from an existing index with the same name (in case of a mapping conflict)
        # always_recreate will always recreate an index, even if it already exists. default is to update mappings only.
        # mapping_generator_cls allows customization of the generated mapping
storage.describe('bucket') # return descriptor, not implemented yet
storage.iter('bucket', doc_type=optional) # yield rows'bucket', doc_type=optional) # return rows
storage.write('bucket', doc_type, rows, primary_key,
        # primary_key is a list of field names which will be used to generate document ids

When creating indexes, we always create an index with a semi-random name and a matching alias that points to it. This allows us to decide whether to re-index documents whenever we're re-creating an index, or to discard the existing records.


When creating indexes, the tableschema types are converted to ES types and a mapping is generated for the index.

Some special properties in the schema provide extra information for generating the mapping:

  • array types need also to have the es:itemType property which specifies the inner data type of array items.
  • object types need also to have the es:schema property which provides a tableschema for the inner document contained in that object (or have es:enabled=false to disable indexing of that field).


  "fields": [
      "name": "my-number",
      "type": "number"
      "name": "my-array-of-dates",
      "type": "array",
      "es:itemType": "date"
      "name": "my-person-object",
      "type": "object",
      "es:schema": {
        "fields": [
          {"name": "name", "type": "string"},
          {"name": "surname", "type": "string"},
          {"name": "age", "type": "integer"},
          {"name": "date-of-birth", "type": "date", "format": "%Y-%m-%d"}
      "name": "my-library",
      "type": "array",
      "es:itemType": "object",
      "es:schema": {
        "fields": [
          {"name": "title", "type": "string"},
          {"name": "isbn", "type": "string"},
          {"name": "num-of-pages", "type": "integer"}
      "name": "my-user-provded-object",
      "type": "object",
      "es:enabled": false

Custom mappings

By providing a custom mapping generator class (via mapping_generator_cls), inheriting from the MappingGenerator class you should be able


The project follows the Open Knowledge International coding standards.

Recommended way to get started is to create and activate a project virtual environment. To install package and development dependencies into active environment:

$ make install

To run tests with linting and coverage:

$ make test

For linting pylama configured in pylama.ini is used. On this stage it's already installed into your environment and could be used separately with more fine-grained control as described in documentation -

For example to sort results by error type:

$ pylama --sort <path>

For testing tox configured in tox.ini is used. It's already installed into your environment and could be used separately with more fine-grained control as described in documentation -

For example to check subset of tests against Python 2 environment with increased verbosity. All positional arguments and options after -- will be passed to py.test:

tox -e py27 -- -v tests/<path>

Under the hood tox uses pytest configured in pytest.ini, coverage and mock packages. This packages are available only in tox envionments.


Here described only breaking and the most important changes. The full changelog and documentation for all released versions could be found in nicely formatted commit history.


  • Initial driver implementation

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