Skip to main content
Edit this page

How to query a remote ClickHouse server

In this guide, we're going to learn how to query a remote ClickHouse server from chDB.

Setup​

Let's first create a virtual environment:

python -m venv .venv
source .venv/bin/activate

And now we'll install chDB. Make sure you have version 2.0.2 or higher:

pip install "chdb>=2.0.2"

And now we're going to install pandas, and ipython:

pip install pandas ipython

We're going to use ipython to run the commands in the rest of the guide, which you can launch by running:

ipython

You can also use the code in a Python script or in your favorite notebook.

An intro to ClickPy​

The remote ClickHouse server that we're going to query is ClickPy. ClickPy keeps track of all the downloads of PyPi packages and lets you explore the stats of packages via a UI. The underlying database is available to query using the play user.

You can learn more about ClickPy in its GitHub repository.

Querying the ClickPy ClickHouse service​

Let's import chDB:

import chdb

We're going to query ClickPy using the remoteSecure function. This function takes in a host name, table name, and username at a minimum.

We can write the following query to return the number of downloads per day of the openai package as a Pandas DataFrame:

query = """
SELECT
    toStartOfDay(date)::Date32 AS x,
    sum(count) AS y
FROM remoteSecure(
  'clickpy-clickhouse.clickhouse.com', 
  'pypi.pypi_downloads_per_day', 
  'play'
)
WHERE project = 'openai'
GROUP BY x
ORDER BY x ASC
"""

openai_df = chdb.query(query, "DataFrame")
openai_df.sort_values(by=["x"], ascending=False).head(n=10)
               x        y
2392  2024-10-02  1793502
2391  2024-10-01  1924901
2390  2024-09-30  1749045
2389  2024-09-29  1177131
2388  2024-09-28  1157323
2387  2024-09-27  1688094
2386  2024-09-26  1862712
2385  2024-09-25  2032923
2384  2024-09-24  1901965
2383  2024-09-23  1777554

Now let's do the same to return the downloads for scikit-learn:

query = """
SELECT
    toStartOfDay(date)::Date32 AS x,
    sum(count) AS y
FROM remoteSecure(
  'clickpy-clickhouse.clickhouse.com', 
  'pypi.pypi_downloads_per_day', 
  'play'
)
WHERE project = 'scikit-learn'
GROUP BY x
ORDER BY x ASC
"""

sklearn_df = chdb.query(query, "DataFrame")
sklearn_df.sort_values(by=["x"], ascending=False).head(n=10)
               x        y
2392  2024-10-02  1793502
2391  2024-10-01  1924901
2390  2024-09-30  1749045
2389  2024-09-29  1177131
2388  2024-09-28  1157323
2387  2024-09-27  1688094
2386  2024-09-26  1862712
2385  2024-09-25  2032923
2384  2024-09-24  1901965
2383  2024-09-23  1777554

Merging Pandas DataFrames​

We now have two DataFrames, which we can merge together based on date (which is the x column) like this:

df = openai_df.merge(
  sklearn_df, 
  on="x", 
  suffixes=("_openai", "_sklearn")
)
df.head(n=5)
            x  y_openai  y_sklearn
0  2018-02-26        83      33971
1  2018-02-27        31      25211
2  2018-02-28         8      26023
3  2018-03-01         8      20912
4  2018-03-02         5      23842

We can then compute the ratio of Open AI downloads to scikit-learn downloads like this:

df['ratio'] = df['y_openai'] / df['y_sklearn']
df.head(n=5)
            x  y_openai  y_sklearn     ratio
0  2018-02-26        83      33971  0.002443
1  2018-02-27        31      25211  0.001230
2  2018-02-28         8      26023  0.000307
3  2018-03-01         8      20912  0.000383
4  2018-03-02         5      23842  0.000210

Querying Pandas DataFrames​

Next, let's say we want to find the dates with the best and worst ratios. We can go back to chDB and compute those values:

chdb.query("""
SELECT max(ratio) AS bestRatio,
       argMax(x, ratio) AS bestDate,
       min(ratio) AS worstRatio,
       argMin(x, ratio) AS worstDate
FROM Python(df)
""", "DataFrame")
   bestRatio    bestDate  worstRatio   worstDate
0   0.693855  2024-09-19    0.000003  2020-02-09

If you want to learn more about querying Pandas DataFrames, see the Pandas DataFrames developer guide.