Python Client for Pilosa

Python client for Pilosa high performance distributed bitmap index.

Change Log

• v0.5.0 (2017-08-03):

  • Supports importing data to Pilosa server.
  • Failover for connection errors.
  • More logging.
  • Introduced schemas. No need to re-define already existing indexes and frames.
  • make commands are supported on Windows.
  • • Breaking Change: Removed time_quantum query option.
  • Deprecation Index constructor. Use schema.index instead.
  • Deprecation client.create_index, client.create_frame, client.ensure_index, client.ensure_frame. Use schemas and client.sync_schema instead.

• v0.4.0 (2017-06-08):

  • Supports Pilosa Server v0.4.0.
  • This version has the updated documentation.
  • Some light refactoring which shouldn't affect any user code.
  • Updated the accepted values for index, frame names and labels to match with the Pilosa server.
  • Union queries accept 0 or more arguments. Intersect and Difference queries accept 1 or more arguments.
  • Added inverse TopN and inverse Range calls.
  • Inverse enabled status of frames is not checked on the client side.

• v0.3.2 (2017-05-03):

  • Fixes a bug with getting the version of the package.

• v0.3.1 (2017-05-01):

  • Initial version.
  • Supports Pilosa Server v0.3.1.

Requirements

• Python 2.6 and higher or Python 3.3 and higher

Install

Pilosa client is on PyPI. You can install the library using pip:

pip install pilosa

Usage

Quick overview

Assuming Pilosa server is running at localhost:10101 (the default):

import pilosa

# Create the default client
client = pilosa.Client()

# Retrieve the schema
schema = client.schema()

# Create an Index object
myindex = schema.index("myindex")

# Create a Frame object
myframe = myindex.frame("myframe")

# make sure the index and frame exists on the server
client.sync_schema(schema)

# Send a SetBit query. PilosaError is thrown if execution of the query fails.
client.query(myframe.setbit(5, 42))

# Send a Bitmap query. PilosaError is thrown if execution of the query fails.
response = client.query(myframe.bitmap(5))

# Get the result
result = response.result

# Act on the result
if result:
    bits = result.bitmap.bits
    print("Got bits: ", bits)

# You can batch queries to improve throughput
response = client.query(
    myindex.batch_query(
        myframe.bitmap(5),
        myframe.bitmap(10),
    )    
)
for result in response.results:
    # Act on the result
    print(result)

Data Model and Queries

Indexes and Frames

Index and frames are the main data models of Pilosa. You can check the Pilosa documentation for more detail about the data model.

schema.index method is used to create an index object. Note that this does not create an index on the server; the index object simply defines the schema.

schema = Schema()
repository = schema.index("repository")

Indexes support changing the column label and time quantum (resolution). You can pass these additional arguments to the Index constructor:

repository = schema.index("repository",
    column_label="repo_id", time_quantum=pilosa.TimeQuantum.YEAR_MONTH)

Frames are created with a call to index.frame method:

stargazer = repository.frame("stargazer")

Similar to index objects, you can pass custom options to the index.frame method:

stargazer = repository.frame("stargazer",
    row_label="stargazer_id", time_quantum=pilosa.TimeQuantum.YEAR_MONTH_DAY)

Queries

Once you have indexes and frame objects created, you can create queries for them. Some of the queries work on the columns; corresponding methods are attached to the index. Other queries work on rows, with related methods attached to frames.

For instance, Bitmap queries work on rows; use a frame object to create those queries:

bitmap_query = stargazer.bitmap(1, 100)  # corresponds to PQL: Bitmap(frame='stargazer', stargazer_id=1)

Union queries work on columns; use the index object to create them:

query = repository.union(bitmap_query1, bitmap_query2)

In order to increase througput, you may want to batch queries sent to the Pilosa server. The index.batch_query method is used for that purpose:

query = repository.batch_query(
    stargazer.bitmap(1, 100),
    repository.union(stargazer.bitmap(100, 200), stargazer.bitmap(5, 100))
)

The recommended way of creating query objects is, using dedicated methods attached to index and frame objects. But sometimes it would be desirable to send raw queries to Pilosa. You can use the index.raw_query method for that. Note that, query string is not validated before sending to the server:

query = repository.raw_query("Bitmap(frame='stargazer', stargazer_id=5)")

Please check Pilosa documentation for PQL details. Here is a list of methods corresponding to PQL calls:

Index:

• union(self, *bitmaps)
• intersect(self, *bitmaps)
• difference(self, *bitmaps)
• count(self, bitmap)
• set_column_attrs(self, column_id, attrs)

Frame:

• bitmap(self, row_id)
• inverse_bitmap(self, column_id)
• setbit(self, row_id, column_id, timestamp=None)
• clearbit(self, row_id, column_id)
• topn(self, n, bitmap=None, field="", *values)
• inverse_topn(self, n, bitmap=None, field="", *values)
• range(self, row_id, start, end)
• inverse_range(self, column_id, start, end)
• set_row_attrs(self, row_id, attrs)

Pilosa URI

A Pilosa URI has the ${SCHEME}://${HOST}:${PORT} format:

• Scheme: Protocol of the URI. Default: http.
• Host: Hostname or ipv4/ipv6 IP address. Default: localhost.
• Port: Port number. Default: 10101.

All parts of the URI are optional, but at least one of them must be specified. The following are equivalent:

• http://localhost:10101
• http://localhost
• http://:10101
• localhost:10101
• localhost
• :10101

A Pilosa URI is represented by the pilosa.URI class. Below are a few ways to create URI objects:

# create the default URI: http://localhost:10101
uri1 = pilosa.URI()

# create a URI from string address
uri2 = pilosa.URI.address("db1.pilosa.com:20202")

# create a URI with the given host and port
URI uri3 = pilosa.URI(host="db1.pilosa.com", port=20202);

Pilosa Client

In order to interact with a Pilosa server, an instance of pilosa.Client should be created. The client is thread-safe and uses a pool of connections to the server, so we recommend creating a single instance of the client and share it with other objects when necessary.

If the Pilosa server is running at the default address (http://localhost:10101) you can create the default client with default options using:

client = pilosa.Client()

To use a a custom server address, pass the address in the first argument:

client = pilosa.Client("http://db1.pilosa.com:15000")

If you are running a cluster of Pilosa servers, you can create a pilosa.Cluster object that keeps addresses of those servers:

cluster = pilosa.Cluster(
    pilosa.URI.address(":10101"),
    pilosa.URI.address(":10110"),
    pilosa.URI.address(":10111"),
);

# Create a client with the cluster
client = pilosa.Client(cluster)

It is possible to customize the behaviour of the underlying HTTP client by passing client options to the Client constructor:

client = pilosa.Client(cluster,
    connect_timeout=1000,  # if can't connect in  a second, close the connection
    socket_timeout=10000,  # if no response received in 10 seconds, close the connection
    pool_size_per_route=3,  # number of connections in the pool per host
    rety_count=5,  # number of retries before failing the request
)

Once you create a client, you can create indexes, frames and start sending queries.

Here is how you would create a index and frame:

# materialize repository index instance initialized before
client.create_index(repository)

# materialize stargazer frame instance initialized before
client.create_frame(stargazer)

If the index or frame exists on the server, you will receive a PilosaError. You can use ensure_index and ensure_frame methods to ignore existing indexes and frames.

You can send queries to a Pilosa server using the query method of client objects:

response = client.query(frame.bitmap(5))

query method accepts optional columns argument:

response = client.query(frame.bitmap(5),
    columns=True  # return column data in the response
)

Server Response

When a query is sent to a Pilosa server, the server either fulfills the query or sends an error message. In the case of an error, PilosaError is thrown, otherwise a QueryResponse object is returned.

A QueryResponse object may contain zero or more results of QueryResult type. You can access all results using the results property of QueryResponse (which returns a list of QueryResult objects) or you can use the result property (which returns either the first result or None if there are no results):

response = client.query(frame.bitmap(5))

# check that there's a result and act on it
result = response.result
if result:
    # act on the result
}

# iterate on all results
for result in response.results:
    # act on the result

Similarly, a QueryResponse object may include a number of column objects, if columns=True query option was used:

# check that there's a column object and act on it
column = response.column
if column:
    # act on the column

# iterate on all columns
for column in response.columns:
    # act on the column

QueryResult objects contain:

• bitmap property to retrieve a bitmap result,
• count_items property to retrieve column count per row ID entries returned from topn queries,
• count attribute to retrieve the number of rows per the given row ID returned from count queries.

bitmap = response.bitmap
bits = bitmap.bits
attributes = bitmap.attributes

count_items = response.count_items

count = response.count

Importing Data

If you have large amounts of data, it is more efficient to import it to Pilosa instead of several SetBit queries.

This library supports importing bits in the CSV (comma separated values) format:

ROW_ID,COLUMN_ID

Optionally, a timestamp with GMT time zone can be added:

ROW_ID,COLUMN_ID,TIMESTAMP

Note that, each line corresponds to a single bit and the lines end with a new line (\n or \r\n). The target index and frame must have been created before hand.

Here's some sample code:

import pilosa
from pilosa.imports import csv_bit_reader

try:
    # python 2.7 and 3
    from io import StringIO
except ImportError:
    # python 2.6 and 2.7
    from StringIO import StringIO

text = u"""
    1,10,683793200
    5,20,683793300
    3,41,683793385        
    10,10485760,683793385        
"""
reader = csv_bit_reader(StringIO(text))
index = pilosa.Index("sample-index")
frame = index.frame("sample-frame")
client = pilosa.Client()
client.ensure_index(index)
client.ensure_frame(frame)
client.import_frame(frame, reader)

Logging

This library uses Python's standard logging facility. The following example sets the logging level to DEBUG and attaches a console handler:

import logging
logger = logging.getLogger("pilosa")
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler())

Contribution

Please check our Contributor's Guidelines.

1. Sign the Developer Agreement so we can include your contibution in our codebase.
2. Fork this repo and add it as upstream: git remote add upstream git@github.com:pilosa/python-pilosa.git.
3. Make sure all tests pass (use make test-all) and be sure that the tests cover all statements in your code (we aim for 100% test coverage).
4. Commit your code to a feature branch and send a pull request to the master branch of our repo.

Running tests

You can run unit tests with:

make test

And both unit and integration tests with:

make test-all

Check the test coverage:

make cover

Generating protobuf classes

Protobuf classes are already checked in to source control, so this step is only needed when the upstream public.proto changes.

Before running the following step, make sure you have the Protobuf compiler installed:

make generate

License

Copyright 2017 Pilosa Corp.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:

1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
DAMAGE.