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A Python library for creating Elasticsearch partitioned indexes by date range

Project description

Elasticsearch Partition

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A Python library is written on Cython for creating Elasticsearch indexes by date range.

For time oriented data, such as logs, a common strategy is to partition data into indexes that hold data for a certain time range. For example, the index logstash-2018.01.01 holds data for events that happened on 2018-01-01, i.e. a time range of a day. You can of course choose bigger or smaller time ranges as well(year, month or day frequencies), depending on your needs. Using index templates, you can easily manage settings and mappings for any index created with a name starting with e.g. logstash-*.

Installation

Install the elasticsearch partition package with pip:

pip install elasticsearch-partition

How to Use

Basic usage

How to import and use partition module with since and until dates:

import datetime
from elasticsearch_partition import partition

partition('logs-*', datetime.date(2016, 11, 29), datetime.date(2018, 2, 4))
# ['logs-2016-11-29', 'logs-2016-11-30', 'logs-2016-12-*', 'logs-2017-*',
# 'logs-2018-01-*', 'logs-2018-02-01', 'logs-2018-02-02', 'logs-2018-02-03',
# 'logs-2018-02-04']

When you are using partition only with since date, until will be replaced on a current date.

partition('logs-*', since=datetime.date(2018, 7, 10))
# ['logs-2018-07-10', 'logs-2018-07-11', 'logs-2018-07-12', 'logs-2018-07-13',
# 'logs-2018-07-14', 'logs-2018-07-15', 'logs-2018-07-16', 'logs-2018-07-17']

Or when you are using partition only with until all dates from until to current date will be excluded.

partition('logs-*', until=datetime.date(2018, 7, 10))
# ['-logs-2018-07-10', '-logs-2018-07-11', '-logs-2018-07-12',
# '-logs-2018-07-13', '-logs-2018-07-14', '-logs-2018-07-15',
# '-logs-2018-07-16', '-logs-2018-07-17', 'logs-*']

Note: If until more then current date you will get an error.

How to customize partitioning

If you want to change some partition bahavior you can do it ease with RangePartition and formatters module, also you can use your custom date now functions.

from elasticsearch_partition import RangePartition
from elasticsearch_partition.partitioning import MONTH
from elasticsearch_partition.formatters import LittleEndianDateFormatter

# frequency - Index partitioning frequency
# formatter - Formatter instance
# escape - Special character which will be replaced on a date
# now_func - Get now date function
my_partition = RangePartition(
    frequency=MONTH,
    formatter=LittleEndianDateFormatter(sep='.'),
    escape='@',
    now_func=custom_date_now,
)

my_partition('logs-@', datetime.date(2016, 11, 29), datetime.date(2018, 2, 4))
# ['logs-11.2016', 'logs-12.2016', 'logs-*.2017', 'logs-01.2018', 'logs-02.2018']

How to create custom date formatter

All date formatters must be inherited from abstract DateFormatter class and implement fmt_year, fmt_month and fmt_day methods. Some method accept additional keyword parameter wildcard which used for creating formatted date with specified wildcard character. For example 2018-04 will be replced on 2018-04-*, 2018 on 2018-* etc.

class MyDateFormatter(DateFormatter):
    def fmt_year(self, year, wildcard):
        # Should be implemented

    def fmt_month(self, year, month, wildcard):
        # Should be implemented

    def fmt_day(self, year, month, day):
        # Should be implemented

partition = RangePartition(formatter=MyDateFormatter())

How to use with elasticsearch-py

This is useful for all Elasticsearch APIs that refer to an index parameter support execution across multiple indices.

from elasticsearch import Elasticsearch

es = Elasticsearch()
indexes = partition(
    'logs-*',
    datetime.date(2016, 11, 29),
    datetime.date(2018, 2, 4)
)
res = es.search(index=indexes, body={"query": {"match_all": {}}})

How to use with elasticsearch-dsl-py

This is useful for all Elasticsearch APIs that refer to an index parameter support execution across multiple indices and similar for simple Search and Persistance DSL.

from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search

client = Elasticsearch()

indexes = partition(
    'logs-*',
    datetime.date(2016, 11, 29),
    datetime.date(2018, 2, 4)
)
search = Search(using=client, index=indexes) \
    .filter("term", category="search") \
    .query("match", title="python") \
    .exclude("match", description="beta")

response = search.execute()

Changes

A full changelog is maintained in the CAHNGELOG file.

Contributing

elasticsearch-partition is an open source project and contributions are welcome! Check out the Issues page to see if your idea for a contribution has already been mentioned, and feel free to raise an issue or submit a pull request.

Project details


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