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
- IMPORTANT - Read vs. Write
- Benchmarks
- Requirements
- Installation
- Usage
- Known Issues
- Background
- Testing
- CI/CD
- Contributing
- GitHub Stars Trend
In [1]: import pandas as pd
...: import numpy as np
...:
...: from bcpandas import SqlCreds, to_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 = pd.read_sql_table(table_name='my_test_table', con=creds.engine)
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 3014770The big speedup benefit of bcpandas is in the to_sql function, as the benchmarks below show.
However, the bcpandas read_sql function actually performs slower than the pandas equivalent.
Therefore, the bcpandas read_sql function was deprecated in v5.0 and has now been removed in
v6.0+. To read data from SQL to pandas, use the native pandas method pd.read_sql_table or
pd.read_sql_query.
See figures below. All code is in the /benchmarks directory. To run the benchmarks, from the root
directory of this repository, run python benchmarks/benchmark.py main --help 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
I didn't bother including the pandas non-
multiinsertversion 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
chunksizeparameter 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 regularINSERTstatement to support huge amounts of data.
As you can see, pandas native clearly wins here
Any version of Microsoft SQL Server. Can be installed on-prem, in the cloud, on a VM, or one of the Azure versions.
- BCP Utility
- Microsoft ODBC Driver 11, 13, 13.1, or 17 for SQL Server. (Microsoft Docs) See the pyodbc docs for more on different driver versions.
- Python >= 3.8
pandas>= 1.5sqlalchemy>= 1.4pyodbcas the supported DBAPI
| Source | Command |
|---|---|
| PyPI | pip install bcpandas |
| Conda | conda install -c conda-forge bcpandas |
- Create creds (see next section)
- Replace any
df.to_sql(...)in your code withbcpandas.to_sql(df, ...)
That's it!
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
SqlCredsobject with just the minimum attributes needed (server, database, username, password), and bcpandas will create a fullEngineobject from this. It will usepyodbc,sqlalchemy, and the Microsoft ODBC Driver for SQL Server, and will store it in the.engineattribute.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
Engineobject to the bcpandasSqlCredsobject, 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])
| Feature | Pandas native | BCPandas |
|---|---|---|
| Super speed | ❌ | ✅ |
| Good for simple data types like numbers and dates | ❌ | ✅ |
| Handle messy string data | ✅ | ❌ |
built with the help of https://www.tablesgenerator.com/markdown_tables# and https://gist.github.com/rxaviers/7360908
Here are some caveats and limitations of bcpandas.
- Bcpandas has been tested with all ASCII characters 32-127. Unicode characters beyond that range have not been tested.
- An empty string (
"") in the dataframe becomesNULLin the SQL database instead of remaining an empty string. - 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.
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.
If you encounter the error:
bcpandas.constants.BCPandasValueError: Data contains all of the possible quote characters ('"', "'", '`', '~'),
cannot use BCP to import it. Replace one of the possible quote characters in
your data, or use another method besides bcpandas.
And want to still use BCPandas, you will need to pick a quote character and remove all instances of it from the dataframe. Note
that you are modifying your data and take care that replacing e.g., the ~ character will not have undesired consequences.
In this case we are looking to remove ~, replacing it with -. Hunt for its presence in a column:
my_df['some_text_column'].str.contains('~').sum()
If that returns a value greater than zero, you can perform replacement in that column like this:
my_df['some_text_column'] = my_df['some_text_column'].str.replace('~','-')
Then use the first command to confirm that the number of occurences is now 0.
Very similar to above, but with the error message:
bcpandas.constants.BCPandasValueError: Data contains all of the possible delimiter characters (',', '|', '\t'),
cannot use BCP to import it. Replace one of the possible delimiter characters in
your data, or use another method besides bcpandas.
Approach this as is described above for quote characters. If you target the | character for replacement, note that it
must be escaped in a regular expression with a backslash. So the relevant commands would be (here the pipe | is replaced with a front slash):
my_df['some_text_column'] = my_df['some_text_column'].str.replace('\|','/')
my_df['some_text_column'].str.contains('\|').sum()
If you get this error message when writing to the database:
Error = [Microsoft][ODBC Driver 17 for SQL Server]Incorrect host-column number found in BCP format-file
Try replacing any space characters in your column names, with a command like my_df.columns = my_df.columns.str.replace(' ','_') (source).
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!
Much credit is due to
bcpyfor 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
bcpyand the above issues by making the design choices described earlier.
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.
- Docker Desktop installed, either of the Linux or Windows runtimes, doesn't matter
- pytest
- hypothesis
- pytest-cov (coverage.py)
We take testing very seriously here. In order to rely on a library like this in production, it MUST be ruthlessly tested, which thankfully it is. Here is a partial list of what has been tested so far. Pull Requests welcome!
- Data types: All ASCII characters 32-127 (using the Hypothesis library, see below). Unicode characters beyond that range have not been tested.
numpy.NaN,Nonenumpy.inf(fails, as expected)- Empty dataframe (nothing happens, database not modified)
- Duplicate column names (raises error)
- Database columns that are missing from the dataframe, are out of order, or both (passes)
- Extra dataframe columns that aren't in database, when
if_exists="append"specified (fails)
- Testing uses
pytest. - To test for all possible data types, we use the
hypothesislibrary, instead of trying to come up with every single case on our own. pytest-cov(which usescoverage.pyunder the hood) is used to measure code coverage. This is then uploaded to codecov.io as part of the CI/CD process (see below).- In order to spin up a local SQL Server during testing, we use Docker. Specifically, we run one of
the images that Microsoft provides that already have SQL Server fully installed, all we have to
do is use the image to run a container. Here are the links to the
Linux versions and the Windows versions -
Express and
Developer.
- When running the tests, we can specify a specific Docker image to use, by invoking the custom
command line option called
--mssql-docker-image. For example:pytest bcpandas/tests --mssql-docker-image mcr.microsoft.com/mssql/server:2019-latest
- When running the tests, we can specify a specific Docker image to use, by invoking the custom
command line option called
- Instead of using the
subprocesslibrary to control Docker manually, we use the elegantdocker-pylibrary which works very nicely. ADockerDBPython class is defined inbcpandas/tests/utils.pyand it wraps up all the Docker commands and functionality needed to use SQL Server into one class. This class is used inconftest.pyin the core bcpandas tests, and in thebenchmarks/directory for both the benchmarks code as well as the legacy tests forread_sql.
Github Actions is used for CI/CD, although it is still somewhat a work in progress.
Please, all contributions are very welcome!
I will attempt to use the pandas docstring style as detailed
here.
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
![pre-commit-ci[bot] avatar](https://avatars.githubusercontent.com/in/68672?v=4 "pre-commit-ci[bot]")
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent