Python client for Pilosa high performance distributed bitmap index.
-
Next:
- Added TLS support. In order to activate it, prefix the server address with
https://
.
- Added TLS support. In order to activate it, prefix the server address with
-
v0.7.0 (2017-10-04):
- Added support for creating range encoded frames.
- Added
Xor
call. - Added support for excluding bits or attributes from bitmap calls. In order to exclude bits, call
setExcludeBits(true)
in yourQueryOptions.Builder
. In order to exclude attributes, callsetExcludeAttributes(true)
. - Added range field operations.
- Customizable CSV timestamp format (Contributed by @lachlanorr).
- Deprecation Row and column labels are deprecated, and will be removed in a future release of this library. Do not use
column_label
field when creatingIndex
objects and do not userow_label
field when creatingFrame
objects for new code. See: FeatureBaseDB/featurebase#752 for more info.
-
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.
- Breaking Change: Removed
- Deprecation
Index
constructor. Useschema.index
instead. - Deprecation
client.create_index
,client.create_frame
,client.ensure_index
,client.ensure_frame
. Use schemas andclient.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
andDifference
queries accept 1 or more arguments.- Added
inverse TopN
andinverse 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.
- Python 2.6 and higher or Python 3.3 and higher
Pilosa client is on PyPI. You can install the library using pip
:
pip install pilosa
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)
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 time quantum (resolution). You can pass these additional arguments to the Index
constructor:
repository = schema.index("repository", 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",
inverse_enabled=True, time_quantum=pilosa.TimeQuantum.YEAR_MONTH_DAY)
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', row=1)
Union
queries work on columns; use the index object to create them:
query = repository.union(bitmap_query1, bitmap_query2)
In order to increase throughput, 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', row=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)
xor(self, *bitmaps)
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)
set_field_value(self, column_id, field, value)
sum(self, bitmap, field)
A Pilosa URI has the ${SCHEME}://${HOST}:${PORT}
format:
- Scheme: Protocol of the URI, one of
http
orhttps
. 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)
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
pool_size_total=50, # total number of connections in the pool
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
)
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 fromtopn
queries,count
attribute to retrieve the number of rows per the given row ID returned fromcount
queries.
bitmap = response.bitmap
bits = bitmap.bits
attributes = bitmap.attributes
count_items = response.count_items
count = response.count
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)
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())
Please check our Contributor's Guidelines.
- Fork this repo and add it as upstream:
git remote add upstream git@github.com:pilosa/python-pilosa.git
. - 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). - Commit your code to a feature branch and send a pull request to the
master
branch of our repo.
You can run unit tests with:
make test
And both unit and integration tests with:
make test-all
Check the test coverage:
make cover
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
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.