Hi Mich!
First, thanks for the clear explanation!
However, it doesn't really answer my question. I understand why the JDBC
Data Source works the way it does. I also understand and accept that
moving data OUT of Spark is never going to get as much attention from
the Spark devs as moving INTO Spark gets :)
My question is, if I do need to upload lots of data, the JDBC source
just doesn't cut it. Right now my users export to CSV and use external
tooling to import that in our database, but I would prefer them to just
be able to use dataframe.write().
So if I write a new Data Source specific to my database, how do I best
go about it? Do I use the v2 API and if so, how do I integrate into its
table detection/creation framework? Or do still use the v1 API
even though v2 is available?
I'm currently just adding my own table detection and creation code but I
imagine that is not really 'the Spark way'. But I can't see the forest
for the trees!
Joeri
On 3/6/26 11:06 PM, Mich Talebzadeh wrote:
Hi,
Spark uses the JDBC data source / connector. to read data. Internally
the database performs a scan of the table and streams the rows out,
i.e sequential table scan or streaming result set
Writing through JDBC is different. Each row must be processed as a
transaction operation.
Spark row
│
▼
INSERT statement
│
▼
database engine (monetDB, Oracle etc)
├─ constraint checks
├─ index updates
├─ transaction logging
└─ storage update
So the database does a lot more work for each row. This creates a
row-by-row workflow, which looks serial and inefficient as you observed
INSERT row 1
INSERT row 2
Spark’s generic JDBC writer deliberately avoids database-specific
features and therefore falls back to the safest universally supported
mechanism i.e standard SQL INSERT statements.
JDBC is designed to work with all relational databases, such
as Oracle, MonetDB etc. Because of that, Spark must use the lowest
common denominator that every database supports.
That lowest common denominator is
INSERT INTO table VALUES (...)
In short, because the JDBC interface is database-agnostic, Spark uses
the safest universally supported operation (standard SQL INSERT).
Since Spark cannot assume the availability of database-specific bulk
loaders, the generic implementation often inserts rows individually.
HTH,
Dr Mich Talebzadeh,
Data Scientist | Distributed Systems (Spark) | Financial Forensics &
Metadata Analytics | Transaction Reconstruction | Audit &
Evidence-Based Analytics
**view my Linkedin profile
<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
On Fri, 6 Mar 2026 at 12:43, Joeri van Ruth via dev
<[email protected]> wrote:
Hi!
I'm a developer working on MonetDB, a column-oriented SQL
database. See
https://www.monetdb.org.
I've created a JdbcDialect for MonetDB, it seems to work fine. The
source code is at https://github.com/MonetDB/monetdb-spark.
Unfortunately it turns out the JDBC Data Source is good at downloading
data from the database but really slow when uploading. The reason it's
so slow is that it uses a separate INSERT statement for each row.
To work around this, I implemented a custom data source that uses
MonetDB's COPY BINARY INTO feature to more efficiently upload data.
This is orders of magnitude faster, but it currently only supports
Append mode. I would like to also support Overwrite mode. This
turned out to be harder than expected.
It seems the table existence checks and creation functionality is part
of org.apache.spark.sql.catalog.Catalog. Do I have to hook into that
somehow? And if so, how does my
dataframe
.write()
.source("org.monetdb.spark")
.mode(SaveMode.Overwrite)
.option("url", url)
.option("dbtable", "foo")
.save()
find my catalog? The Catalog interface also contains lots of methods
that I don't really understand, do I have to implement all of these?
Can someone give me an overview of the big picture?
Note: another approach would be to not try to implement a v2
DataSource but
more or less "subclass" the v1 JDBC Data Source like the now abandoned
SQL Server dialect seems to do:
https://github.com/microsoft/sql-spark-connector.
Would that still be the way to go?
Best regards,
Joeri van Ruth
---------------------------------------------------------------------
To unsubscribe e-mail: [email protected]
