High-level wrapper around BCP for high performance data transfers between pandas and SQL Server. No knowledge of BCP required!!
Project description
bcpandas
High-level wrapper around BCP for high performance data transfers between pandas and SQL Server. No knowledge of BCP required!! (pronounced BEE-CEE-Pandas)
Quickstart
In [1]: import pandas as pd
...: import numpy as np
...:
...: from bcpandas import SqlCreds, to_sql, read_sql
In [2]: creds = SqlCreds(
...: 'my_server',
...: 'my_db',
...: 'my_username',
...: 'my_password'
...: )
In [3]: df = pd.DataFrame(
...: data=np.ndarray(shape=(10, 6), dtype=int),
...: columns=[f"col_{x}" for x in range(6)]
...: )
In [4]: df
Out[4]:
col_0 col_1 col_2 col_3 col_4 col_5
0 4128860 6029375 3801155 5570652 6619251 7536754
1 4849756 7536751 4456552 7143529 7471201 7012467
2 6029433 6881357 6881390 7274595 6553710 3342433
3 6619228 7733358 6029427 6488162 6357104 6553710
4 7536737 7077980 6422633 7536732 7602281 2949221
5 6357104 7012451 6750305 7536741 7340124 7274610
6 7340141 6226036 7274612 7077999 6881387 6029428
7 6619243 6226041 6881378 6553710 7209065 6029415
8 6881378 6553710 7209065 7536743 7274588 6619248
9 6226030 7209065 6619231 6881380 7274612 3014770
In [5]: to_sql(df, 'my_test_table', creds, index=False, if_exists='replace')
In [6]: df2 = read_sql('my_test_table', creds)
In [7]: df2
Out[7]:
col_0 col_1 col_2 col_3 col_4 col_5
0 4128860 6029375 3801155 5570652 6619251 7536754
1 4849756 7536751 4456552 7143529 7471201 7012467
2 6029433 6881357 6881390 7274595 6553710 3342433
3 6619228 7733358 6029427 6488162 6357104 6553710
4 7536737 7077980 6422633 7536732 7602281 2949221
5 6357104 7012451 6750305 7536741 7340124 7274610
6 7340141 6226036 7274612 7077999 6881387 6029428
7 6619243 6226041 6881378 6553710 7209065 6029415
8 6881378 6553710 7209065 7536743 7274588 6619248
9 6226030 7209065 6619231 6881380 7274612 3014770
IMPORTANT - Read vs. Write
The big speedup benefit of bcpandas is in the to_sql
function, as the benchmarks below show. However, the read_sql
function actually performs slower than the pandas equivalent. So don't use it. Use bcpandas for the to_sql
function only and to use native pandas in read_sql
.
Also, read_sql
is not fully tested for this reason, as it became apparant that it is not worth the effort to fix all of the edge cases.
Q: So why do we even have a read_sql
function?
A: To complete the API, and in order to discover that there is no speedup for it in bcpandas. Now that this is determined, it will be removed in a future release.
Benchmarks
See figures below. All code is in the /benchmarks
directory. To run the benchmarks, run python benchmark.py main
and fill in the command line options that are presented.
Running this will output
- PNG image of the graph
- JSON file of the benchmark data
- JSON file with the environment details of the machine that was used to generate it
to_sql
I didn't bother including the pandas non-
multiinsert
version here because it just takes way too long
Why not just use the new pandas method='multi'
?
- Because it is still much slower
- Because you are forced to set the
chunksize
parameter to a very small number for it to work - generally a bit less then2100/<number of columns>
. This is because SQL Server can only accept up to 2100 parameters in a query. See here and here for more discussion on this, and the recommendation to use a bulk insert tool such as BCP. It seems that SQL Server simply didn't design the regularINSERT
statement to support huge amounts of data.
read_sql
As you can see, pandas native clearly wins here
Requirements
Database
Any version of Microsoft SQL Server. Can be installed on-prem, in the cloud, on a VM, or one of the Azure versions.
Python User
- BCP Utility
- Microsoft ODBC Driver 11, 13, 13.1, or 17 for SQL Server. See the pyodbc docs for details.
- Python >= 3.6
pandas
>= 0.19sqlalchemy
>= 1.1.4pyodbc
as the supported DBAPI
Installation
Source | Command |
---|---|
PyPI | pip install bcpandas |
Conda | conda install -c conda-forge bcpandas |
Usage
- Create creds (see next section)
- Replace any
df.to_sql(...)
in your code withbcpandas.to_sql(df, ...)
That's it!
Credential/Connection object
Bcpandas requires a bcpandas.SqlCreds
object in order to use it, and also a sqlalchemy.Engine
. The user has 2 options when constructing it.
-
Create the bcpandas
SqlCreds
object with just the minimum attributes needed (server, database, username, password), and bcpandas will create a fullEngine
object from this. It will usepyodbc
,sqlalchemy
, and the Microsoft ODBC Driver for SQL Server, and will store it in the.engine
attribute.In [1]: from bcpandas import SqlCreds In [2]: creds = SqlCreds('my_server', 'my_db', 'my_username', 'my_password') In [3]: creds.engine Out[3]: Engine(mssql+pyodbc:///?odbc_connect=Driver={ODBC Driver 17 for SQL Server};Server=tcp:my_server,1433;Database=my_db;UID=my_username;PWD=my_password)
-
Pass a full
Engine
object to the bcpandasSqlCreds
object, and bcpandas will attempt to parse out the server, database, username, and password to pass to the command line utilities. If a DSN is used, this will fail.(continuing example above)
In [4]: creds2 = SqlCreds.from_engine(creds.engine) In [5]: creds2.engine Out[5]: Engine(mssql+pyodbc:///?odbc_connect=Driver={ODBC Driver 17 for SQL Server};Server=tcp:my_server,1433;Database=my_db;UID=my_username;PWD=my_password) In [6]: creds2 Out[6]: SqlCreds(server='my_server', database='my_db', username='my_username', with_krb_auth=False, engine=Engine(mssql+pyodbc:///?odbc_connect=Driver={ODBC Driver 17 for SQL Server};Server=tcp:my_server,1433;Database=my_db;UID=my_username;PWD=my_password), password=[REDACTED])
Recommended Usage
Feature | Pandas native | BCPandas |
---|---|---|
Super speed | :x: | :white_check_mark: |
Good for simple data types like numbers and dates | :x: | :white_check_mark: |
Handle messy string data | :white_check_mark: | :x: |
built with the help of https://www.tablesgenerator.com/markdown_tables# and https://gist.github.com/rxaviers/7360908
Known Issues
Here are some caveats and limitations of bcpandas.
- In the
to_sql
function:- Bcpandas has been tested with all ASCII characters 32-127. Unicode characters beyond that range have not been tested.
- For now, an empty string (
""
) in the dataframe becomesNULL
in the SQL database instead of remaining an empty string. If there is a NaN/Null in the last column of the dataframe it will throw an error. This is due to a BCP issue. See my issue with Microsoft about this here.This doesn't seem to be a problem based on the tests.- Because bcpandas first outputs to CSV, it needs to use several specific characters to create the CSV, including a delimiter and a quote character. Bcpandas attempts to use characters that are not present in the dataframe for this, going through the possilbe delimiters and quote characters specified in
constants.py
. If all possible characters are present in the dataframe and bcpandas cannot find both a delimiter and quote character to use, it will throw an error.- The BCP utility does not ignore delimiter characters when surrounded by quotes, unlike CSVs - see here in the Microsoft docs.
Background
Writing data from pandas DataFrames to a SQL database is very slow using the built-in to_sql
method, even with the newly introduced execute_many
option. For Microsoft SQL Server, a far far faster method is to use the BCP utility provided by Microsoft. This utility is a command line tool that transfers data to/from the database and flat text files.
This package is a wrapper for seamlessly using the bcp utility from Python using a pandas DataFrame. Despite the IO hits, the fastest option by far is saving the data to a CSV file in the file system and using the bcp utility to transfer the CSV file to SQL Server. Best of all, you don't need to know anything about using BCP at all!
Existing Solutions
Much credit is due to
bcpy
for the original idea and for some of the code that was adopted and changed.
bcpy
bcpy has several flaws:
- No support for reading from SQL, only writing to SQL
- A convoluted, overly class-based internal design
- Scope a bit too broad - deals with pandas as well as flat files
This repository aims to fix and improve on
bcpy
and the above issues by making the design choices described earlier.
Design and Scope
The only scope of bcpandas
is to read and write between a pandas DataFrame and a Microsoft SQL Server database. That's it. We do not concern ourselves with reading existing flat files to/from SQL - that introduces way to much complexity in trying to parse and decode the various parts of the file, like delimiters, quote characters, and line endings. Instead, to read/write an exiting flat file, just import it via pandas into a DataFrame, and then use bcpandas
.
The big benefit of this is that we get to precicely control all the finicky parts of the text file when we write/read it to a local file and then in the BCP utility. This lets us set library-wide defaults (maybe configurable in the future) and work with those.
For now, we are using the non-XML BCP format file type. In the future, XML format files may be added.
Testing
Testing uses pytest
. A local SQL Server is spun up using Docker.
Contributing
Please, all contributions are very welcome!
I will attempt to use the pandas
docstring style as detailed here.
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