DATAFRAMES
WHY Data Scientists don't use SQL
Online transaction processing (OLTP):
- transaction safe
- CRUD (Create, Read, Update, Delete) rows of data
- data stored row-wise
- SQL
- LINQ (C#)
- Examples: PostgreSQL, MySQL, SQLite, ...
OLTP
see: Wikipedia:LINQ
Online analytical processing (OLAP):
- complex queries
- reading, aggregation, filtering of historical data
- data stored column-wise
- MDX (MultiDimensional eXpressions)
- ELT pipeline to ingest data
- Examples: Clickhouse, Apache Druid, IBM Cognos, ...
OLAP
- in-memory OLAP tables
- complex queries
- reading, aggregation, filtering of historical data
- data stored column-wise
- custom query API
- Examples: pandas (Python), data.tables (R), pola-rs (Rust)
DATAFRAMES
see: https://www.pola.rs
Python dataframes
- In-memory, single-threaded: pandas
- In-memory, parallel on clusters: dask, ray
- Out-of-memory: vaex, modin
- Out-of-memory, parallel on clusters: pyspark
- In-memory, GPU-accelerated: RAPIDS cuDF
- In-memory, multi-threaded: pola-rs
R dataframes
- In-memory, multithreaded: data.table
- In-memory, out-of-memory, on clusters: dplyr
Other dataframes
Benchmark of dataframes
SQL PROS
- in-memory SQL DBs are as fast as dataframes
- no need to learn a new query language/API
- SQL statements are shorter
- SQL query optimiser (multithreaded)
- easy to scale data processing steps to persistent DBs
- SQL is a battle-proof industry standard
SQL CONS
- distributed SQL on clusters very difficult
- SQL standard has many dialects
- A single huge and confusing SQL statement is needed for best performance
- SQL is illogical (fixed with LINQ)
-
SELECT column FROM table
-
FROM table SELECT column
-
- SQL strings are not typesafe (fixed with LINQ)
- No GPU support
DATAFrames PROS
- in-memory dataframes need no database connection
- object oriented and type-safe query APIs
- API better suited for data exploration
- Easy to scale out to clusters
- GPU support
DATAFrames CONS
- every dataframe has a different query API
- difficult to scale data processing steps to persistent DBs (but see DuckDB)
- No query optimizer (except pyspark)
- Out-of-memory dataframe performance often slow
- No transaction safety
- No concurrent multi-user access
SQL
query = """
select
a.name,
p.year,
p.title,
p.abstract,
p.paper_text
from authors a
inner join paper_authors pa on pa.author_id = a.id
left join papers p on p.id = pa.paper_id
and p.event_type not in ('Oral', 'Spotlight', 'Poster')
order by name, year asc
"""
pd.read_sql(query, con=con)
Pandas
df_authors \
.merge(
df_papers_authors.drop(columns=["id"]),
left_on="id",
right_on="author_id",
how="inner") \
.drop(columns=["id"]) \
.merge(
df_papers \
.query("event_type in ('Oral', 'Spotlight', 'Poster')") \
[["id", "year", "title", "abstract", "paper_text"]],
left_on="paper_id",
right_on="id",
how="left") \
.drop(columns=["id", "paper_id", "author_id"]) \
.sort_values(by=["name", "year"], ascending=True)
- OLAP + OLTR combined:
SAP HANA is an OLTAP system
it is a column-oriented, in-memory database, that combines OLAP and OLTP operations into a single system
- But it better be open-source, easy-to-install and support more client languages
- Other interesting developments:
- pola-rs
- trino
- DuckDB
- Clickhouse
- see https://db-engines.com/en/ranking
FUTURE
THE END
dataframes
By andimai
dataframes
A first look
