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# Introduction
There are quite a few instruments for processing datasets at this time. All of them declare — after all they do — that they’re the most effective and the fitting selection for you. However are they? There are two predominant necessities these instruments ought to fulfill: they need to simply carry out on a regular basis information evaluation operations and accomplish that rapidly, even beneath the strain of huge datasets.
To find out the most effective instrument amongst DuckDB, SQLite, and Pandas, we examined them beneath these circumstances.
First, we gave them solely on a regular basis analytical duties: summing values, grouping by classes, filtering with circumstances, and multi-field aggregations. This mirrored how analysts really work with actual datasets, in comparison with eventualities designed to showcase the most effective traits of a instrument.
Second, we carried out these operations on a Kaggle dataset with over 1 million rows. It’s a practical tipping level — sufficiently small to run on a single machine, but massive sufficient that reminiscence strain and question velocity begin to reveal clear variations between instruments.
Let’s see how these exams went.
# The Dataset We Used
// Dataset Overview
We used the Financial institution dataset from Kaggle. This dataset incorporates over 1 million rows, comprising 5 columns:
| Column Title | Description |
|---|---|
| Date | The date the transaction occurred |
| Area | The enterprise class or sort (RETAIL, RESTAURANT) |
| Location | Geographic area (Goa, Mathura) |
| Worth | Transaction worth |
| Transaction_count | The full variety of transactions on that day |
This dataset is generated utilizing Python. Whereas it could not absolutely resemble real-life information, its dimension and construction are ample to check and examine the efficiency variations between the instruments.
// Peeking Into the Knowledge with Pandas
We used Pandas to load the dataset right into a Jupyter pocket book and look at its basic construction, dimensions, and null values. Right here is the code.
import pandas as pd
df = pd.read_excel('bankdataset.xlsx')
print("Dataset form:", df.form)
df.head()
Right here is the output.
If you need a fast reference to frequent operations when exploring datasets, try this useful Pandas Cheat Sheet.
Earlier than benchmarking, let’s see find out how to arrange the atmosphere.
# Setting Up a Truthful Testing Surroundings
All three instruments — DuckDB, SQLite, and Pandas — have been arrange and run in the identical Jupyter Pocket book atmosphere to make sure the take a look at was truthful. This ensured that the circumstances throughout runtime and the usage of reminiscence remained fixed all through.
First, we put in and loaded the required packages.
Listed here are the instruments we would have liked:
- pandas: for traditional
DataFrameoperations - duckdb: for SQL execution on a
DataFrame - sqlite3: for managing an embedded SQL database
- time: for capturing execution time
- memory_profiler: to measure reminiscence allocation
# Set up if any of them aren't in your atmosphere
!pip set up duckdb --quiet
import pandas as pd
import duckdb
import sqlite3
import time
from memory_profiler import memory_usage
Now let’s put together the information in a format that may be shared throughout all three instruments.
// Loading Knowledge into Pandas
We’ll use Pandas to load the dataset as soon as, after which we’ll share or register it for DuckDB and SQLite.
df = pd.read_excel('bankdataset.xlsx')
df.head()
Right here is the output to validate.
// Registering Knowledge with DuckDB
DuckDB permits you to instantly entry Pandas DataFrames. You do not have to transform something—simply register and question. Right here is the code.
# Register DataFrame as a DuckDB desk
duckdb.register("bank_data", df)
# Question by way of DuckDB
duckdb.question("SELECT * FROM bank_data LIMIT 5").to_df()
Right here is the output.
// Making ready Knowledge for SQLite
Since SQLite does not learn Excel information instantly, we began by including the Pandas DataFrame to an in-memory database. After that, we used a easy question to look at the information format.
conn_sqlite = sqlite3.join(":reminiscence:")
df.to_sql("bank_data", conn_sqlite, index=False, if_exists="substitute")
pd.read_sql_query("SELECT * FROM bank_data LIMIT 5", conn_sqlite)
Right here is the output.
# How We Benchmarked the Instruments
We used the identical 4 queries on DuckDB, SQLite, and Pandas to match their efficiency. Every question was designed to deal with a standard analytical process that mirrors how information evaluation is utilized in the actual world.
// Guaranteeing Constant Setup
The in-memory dataset was utilized by all three instruments.
- Pandas queried the
DataFrameinstantly - DuckDB executed SQL queries instantly in opposition to the
DataFrame - SQLite saved a replica of the
DataFramein an in-memory database and ran SQL queries on it
This technique ensured that each one three instruments used the identical information and operated with the identical system settings.
// Measuring Execution Time
To trace question length, Python’s time module wrapped every question in a easy begin/finish timer. Solely the question execution time was recorded; data-loading and preparation steps have been excluded.
// Monitoring Reminiscence Utilization
Together with processing time, reminiscence utilization signifies how effectively every engine performs with massive datasets.
If desired, reminiscence utilization will be sampled instantly earlier than and after every question to estimate incremental RAM consumption.
// The Benchmark Queries
We examined every engine on the identical 4 on a regular basis analytical duties:
- Whole transaction worth: summing a numeric column
- Group by area: aggregating transaction counts per class
- Filter by location: filtering rows by a situation earlier than aggregation
- Group by area & location: multi-field aggregation with averages
# Benchmark Outcomes
// Question 1: Whole Transaction Worth
Right here we measure how Pandas, DuckDB, and SQLite carry out when summing the Worth column throughout the dataset.
// Pandas Efficiency
We calculate the overall transaction worth utilizing .sum() on the Worth column. Right here is the code.
pandas_results = []
def pandas_q1():
return df['Value'].sum()
mem_before = memory_usage(-1)[0]
begin = time.time()
pandas_q1()
finish = time.time()
mem_after = memory_usage(-1)[0]
pandas_results.append({
"engine": "Pandas",
"question": "Whole transaction worth",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
pandas_results
Right here is the output.
// DuckDB Efficiency
We calculate the overall transaction worth utilizing a full-column aggregation. Right here is the code.
duckdb_results = []
def duckdb_q1():
return duckdb.question("SELECT SUM(worth) FROM bank_data").to_df()
mem_before = memory_usage(-1)[0]
begin = time.time()
duckdb_q1()
finish = time.time()
mem_after = memory_usage(-1)[0]
duckdb_results.append({
"engine": "DuckDB",
"question": "Whole transaction worth",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
duckdb_results
Right here is the output.
// SQLite Efficiency
We calculate the overall transaction worth by summing the worth column. Right here is the code.
sqlite_results = []
def sqlite_q1():
return pd.read_sql_query("SELECT SUM(worth) FROM bank_data", conn_sqlite)
mem_before = memory_usage(-1)[0]
begin = time.time()
sqlite_q1()
finish = time.time()
mem_after = memory_usage(-1)[0]
sqlite_results.append({
"engine": "SQLite",
"question": "Whole transaction worth",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
sqlite_results
Right here is the output.
// Total Efficiency Evaluation
Now let’s examine execution time and reminiscence utilization. Right here is the code.
import matplotlib.pyplot as plt
all_q1 = pd.DataFrame(pandas_results + duckdb_results + sqlite_results)
fig, axes = plt.subplots(1, 2, figsize=(10,4))
all_q1.plot(x="engine", y="time", sort="barh", ax=axes[0], legend=False, title="Execution Time (s)")
all_q1.plot(x="engine", y="reminiscence", sort="barh", coloration="salmon", ax=axes[1], legend=False, title="Reminiscence Utilization (MB)")
plt.tight_layout()
plt.present()
Right here is the output.
Pandas is by far the quickest and most memory-efficient right here, finishing nearly immediately with minimal RAM utilization. DuckDB is barely slower and makes use of extra reminiscence however stays environment friendly, whereas SQLite is each the slowest and the heaviest by way of reminiscence consumption.
// Question 2: Group by Area
Right here we measure how Pandas, DuckDB, and SQLite carry out when grouping transactions by Area and summing their counts.
// Pandas Efficiency
We calculate the overall transaction depend per area utilizing .groupby() on the Area column.
def pandas_q2():
return df.groupby('Area')['Transaction_count'].sum()
mem_before = memory_usage(-1)[0]
begin = time.time()
pandas_q2()
finish = time.time()
mem_after = memory_usage(-1)[0]
pandas_results.append({
"engine": "Pandas",
"question": "Group by area",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in pandas_results if p["query"] == "Group by area"]
Right here is the output.
// DuckDB Efficiency
We calculate the overall transaction depend per area utilizing a SQL GROUP BY on the area column.
def duckdb_q2():
return duckdb.question("""
SELECT area, SUM(transaction_count)
FROM bank_data
GROUP BY area
""").to_df()
mem_before = memory_usage(-1)[0]
begin = time.time()
duckdb_q2()
finish = time.time()
mem_after = memory_usage(-1)[0]
duckdb_results.append({
"engine": "DuckDB",
"question": "Group by area",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in duckdb_results if p["query"] == "Group by area"]
Right here is the output.
// SQLite Efficiency
We calculate the overall transaction depend per area utilizing SQL GROUP BY on the in-memory desk.
def sqlite_q2():
return pd.read_sql_query("""
SELECT area, SUM(transaction_count) AS total_txn
FROM bank_data
GROUP BY area
""", conn_sqlite)
mem_before = memory_usage(-1)[0]
begin = time.time()
sqlite_q2()
finish = time.time()
mem_after = memory_usage(-1)[0]
sqlite_results.append({
"engine": "SQLite",
"question": "Group by area",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in sqlite_results if p["query"] == "Group by area"]
Right here is the output.
// Total Efficiency Evaluation
Now let’s examine execution time and reminiscence utilization. Right here is the code.
import pandas as pd
import matplotlib.pyplot as plt
groupby_results = [r for r in (pandas_results + duckdb_results + sqlite_results)
if "Group by" in r["query"]]
df_groupby = pd.DataFrame(groupby_results)
fig, axes = plt.subplots(1, 2, figsize=(10,4))
df_groupby.plot(x="engine", y="time", sort="barh", ax=axes[0], legend=False, title="Execution Time (s)")
df_groupby.plot(x="engine", y="reminiscence", sort="barh", coloration="salmon", ax=axes[1], legend=False, title="Reminiscence Utilization (MB)")
plt.tight_layout()
plt.present()
Right here is the output.
DuckDB is quickest, Pandas trades a bit extra time for decrease reminiscence, whereas SQLite is each slowest and most memory-hungry.
// Question 3: Filter by Location (Goa)
Right here we measure how Pandas, DuckDB, and SQLite carry out when filtering the dataset for Location = 'Goa' and summing the transaction values.
// Pandas Efficiency
We filter rows for Location == 'Goa' and sum their values. Right here is the code.
def pandas_q3():
return df[df['Location'] == 'Goa']['Value'].sum()
mem_before = memory_usage(-1)[0]
begin = time.time()
pandas_q3()
finish = time.time()
mem_after = memory_usage(-1)[0]
pandas_results.append({
"engine": "Pandas",
"question": "Filter by location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in pandas_results if p["query"] == "Filter by location"]
Right here is the output.
// DuckDB Efficiency
We filter transactions for Location = 'Goa' and calculate their complete worth. Right here is the code.
def duckdb_q3():
return duckdb.question("""
SELECT SUM(worth)
FROM bank_data
WHERE location = 'Goa'
""").to_df()
mem_before = memory_usage(-1)[0]
begin = time.time()
duckdb_q3()
finish = time.time()
mem_after = memory_usage(-1)[0]
duckdb_results.append({
"engine": "DuckDB",
"question": "Filter by location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in duckdb_results if p["query"] == "Filter by location"]
Right here is the output.
// SQLite Efficiency
We filter transactions for Location = 'Goa' and sum their values. Right here is the code.
def sqlite_q3():
return pd.read_sql_query("""
SELECT SUM(worth) AS total_value
FROM bank_data
WHERE location = 'Goa'
""", conn_sqlite)
mem_before = memory_usage(-1)[0]
begin = time.time()
sqlite_q3()
finish = time.time()
mem_after = memory_usage(-1)[0]
sqlite_results.append({
"engine": "SQLite",
"question": "Filter by location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in sqlite_results if p["query"] == "Filter by location"]
Right here is the output.
// Total Efficiency Evaluation
Now let’s examine execution time and reminiscence utilization. Right here is the code.
import pandas as pd
import matplotlib.pyplot as plt
filter_results = [r for r in (pandas_results + duckdb_results + sqlite_results)
if r["query"] == "Filter by location"]
df_filter = pd.DataFrame(filter_results)
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
df_filter.plot(x="engine", y="time", sort="barh", ax=axes[0], legend=False, title="Execution Time (s)")
df_filter.plot(x="engine", y="reminiscence", sort="barh", coloration="salmon", ax=axes[1], legend=False, title="Reminiscence Utilization (MB)")
plt.tight_layout()
plt.present()
Right here is the output.
DuckDB is the quickest and best; Pandas is slower with greater reminiscence utilization; and SQLite is the slowest however lighter on reminiscence.
// Question 4: Group by Area & Location
// Pandas Efficiency
We calculate the typical transaction worth grouped by each Area and Location. Right here is the code.
def pandas_q4():
return df.groupby(['Domain', 'Location'])['Value'].imply()
mem_before = memory_usage(-1)[0]
begin = time.time()
pandas_q4()
finish = time.time()
mem_after = memory_usage(-1)[0]
pandas_results.append({
"engine": "Pandas",
"question": "Group by area & location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in pandas_results if p["query"] == "Group by area & location"]
Right here is the output.
// DuckDB Efficiency
We calculate the typical transaction worth grouped by each area and location. Right here is the code.
def duckdb_q4():
return duckdb.question("""
SELECT area, location, AVG(worth) AS avg_value
FROM bank_data
GROUP BY area, location
""").to_df()
mem_before = memory_usage(-1)[0]
begin = time.time()
duckdb_q4()
finish = time.time()
mem_after = memory_usage(-1)[0]
duckdb_results.append({
"engine": "DuckDB",
"question": "Group by area & location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in duckdb_results if p["query"] == "Group by area & location"]
Right here is the output.
// SQLite Efficiency
We calculate the typical transaction worth grouped by each area and location. Right here is the code.
def sqlite_q4():
return pd.read_sql_query("""
SELECT area, location, AVG(worth) AS avg_value
FROM bank_data
GROUP BY area, location
""", conn_sqlite)
mem_before = memory_usage(-1)[0]
begin = time.time()
sqlite_q4()
finish = time.time()
mem_after = memory_usage(-1)[0]
sqlite_results.append({
"engine": "SQLite",
"question": "Group by area & location",
"time": spherical(finish - begin, 4),
"reminiscence": spherical(mem_after - mem_before, 4)
})
[p for p in sqlite_results if p["query"] == "Group by area & location"]
Right here is the output.
// Total Efficiency Evaluation
Now let’s examine execution time and reminiscence utilization. Right here is the code.
import pandas as pd
import matplotlib.pyplot as plt
gdl_results = [r for r in (pandas_results + duckdb_results + sqlite_results)
if r["query"] == "Group by area & location"]
df_gdl = pd.DataFrame(gdl_results)
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
df_gdl.plot(x="engine", y="time", sort="barh", ax=axes[0], legend=False,
title="Execution Time (s)")
df_gdl.plot(x="engine", y="reminiscence", sort="barh", ax=axes[1], legend=False,
title="Reminiscence Utilization (MB)", coloration="salmon")
plt.tight_layout()
plt.present()
Right here is the output.
DuckDB handles multi-field group-bys quickest with average reminiscence use, Pandas is slower with very excessive reminiscence utilization, and SQLite is the slowest with substantial reminiscence consumption.
# Ultimate Comparability Throughout All Queries
We’ve in contrast these three engines in opposition to one another by way of reminiscence and velocity. Let’s verify the execution time as soon as once more. Right here is the code.
import pandas as pd
import matplotlib.pyplot as plt
all_results = pd.DataFrame(pandas_results + duckdb_results + sqlite_results)
measure_order = [
"Total transaction value",
"Group by domain",
"Filter by location",
"Group by domain & location",
]
engine_colors = {"Pandas": "#1f77b4", "DuckDB": "#ff7f0e", "SQLite": "#2ca02c"}
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.ravel()
for i, q in enumerate(measure_order):
d = all_results[all_results["query"] == q]
axes[i].barh(d["engine"], d["time"],
coloration=[engine_colors[e] for e in d["engine"]])
for y, v in enumerate(d["time"]):
axes[i].textual content(v, y, f" {v:.3f}", va="heart")
axes[i].set_title(q, fontsize=10)
axes[i].set_xlabel("Seconds")
fig.suptitle("Per-Measure Comparability — Execution Time", fontsize=14)
plt.tight_layout()
plt.present()
Right here is the output.
This chart exhibits that DuckDB persistently maintains the bottom execution occasions for nearly all queries, apart from the overall transaction worth the place Pandas edges it out; SQLite is the slowest by a large margin throughout the board. Let’s verify reminiscence subsequent. Right here is the code.
import pandas as pd
import matplotlib.pyplot as plt
all_results = pd.DataFrame(pandas_results + duckdb_results + sqlite_results)
measure_order = [
"Total transaction value",
"Group by domain",
"Filter by location",
"Group by domain & location",
]
engine_colors = {"Pandas": "#1f77b4", "DuckDB": "#ff7f0e", "SQLite": "#2ca02c"}
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.ravel()
for i, q in enumerate(measure_order):
d = all_results[all_results["query"] == q]
axes[i].barh(d["engine"], d["memory"],
coloration=[engine_colors[e] for e in d["engine"]])
for y, v in enumerate(d["memory"]):
axes[i].textual content(v, y, f" {v:.1f}", va="heart")
axes[i].set_title(q, fontsize=10)
axes[i].set_xlabel("MB")
fig.suptitle("Per-Measure Comparability — Reminiscence Utilization", fontsize=14)
plt.tight_layout()
plt.present()
Right here is the output.
This chart exhibits that SQLite swings between being the most effective and the worst in reminiscence utilization, Pandas is excessive with two finest and two worst instances, whereas DuckDB stays persistently within the center throughout all queries. Consequently, DuckDB proves to be probably the most balanced selection total, delivering persistently quick efficiency with average reminiscence utilization. Pandas exhibits extremes—generally the quickest, generally the heaviest—whereas SQLite struggles with velocity and infrequently finally ends up on the inefficient facet for reminiscence.
Nate Rosidi is an information scientist and in product technique. He is additionally an adjunct professor educating analytics, and is the founding father of StrataScratch, a platform serving to information scientists put together for his or her interviews with actual interview questions from prime corporations. Nate writes on the newest developments within the profession market, offers interview recommendation, shares information science tasks, and covers every thing SQL.