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Re: Guidance Requested: Migrating Large-Scale DB2 Databases to PostgreSQL - Mailing list pgsql-admin

From Ron Johnson
Subject Re: Guidance Requested: Migrating Large-Scale DB2 Databases to PostgreSQL
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Msg-id CANzqJaDV559WS7oRupSBrRgLdArD0p53NX2EQA7B6R-Ke-yUog@mail.gmail.com
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In response to Guidance Requested: Migrating Large-Scale DB2 Databases to PostgreSQL  (mahamood hussain <hussain.ieg@gmail.com>)
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On Wed, Oct 15, 2025 at 5:14 PM mahamood hussain <hussain.ieg@gmail.com> wrote:

Hi Team,

We are in the process of migrating several DB2 databases to PostgreSQL, primarily to reduce the high licensing costs associated with DB2. These databases support retail applications (e.g., supermarkets and stores), and during peak hours, we anticipate over 100 concurrent connections.

Current Database Profile:

  • Approximately 3,000 tables in total

  • Around 100 tables contain active data

  • Most tables have low data volume

  • A few large tables range from 10 GB to 2 TB

  • The largest table contains approximately 80 billion rows

Migration Approach:

  • We are using Ispirer for code conversion (DB2 to PostgreSQL).

  • For data migration, we are evaluating Fivetran, but noted that it relies on the COPY method for data loading.

Questions & Areas Where We Need Guidance:

  1. Is Fivetran a suitable option for migrating very large datasets (e.g., tables with 80+ billion rows)?

  2. Are there any reliable open-source tools for DB2 to PostgreSQL data migration that we can use internally, without needing to invest in a tool like Fivetran?

  3. Are there more scalable or efficient alternatives for both the initial load and ongoing/incremental sync to PostgreSQL?

Additional Input Requested:

  • What are the key best practices (Do’s and Don’ts) to keep in mind during a large-scale DB2 → PostgreSQL migration?


Since this is retail, I bet the only two statements that ever touch the biggest tables are INSERT and SELECT.

In a similar situation (Oracle to PG, but that should not make a difference), I created per-month views on the source side, and then exported them to CSV files, which then got COPY-loaded into Postgresql.

For each table, I tracked in a spreadsheet which month-csv files had been exported, compressed. scp'd, loaded.and indexed.  Iteratively, I developed a pretty slick "parallel assembly line" process of cron jobs of continually looping shell scripts that exported views to CSV files, compressed CSV files who's export had completed, and then scp'd the files once the CSV files had finished compressing.  On the VM running PG, a cron job running a continuously-looping shell script would pick up successfully transferred files then decompress and load them into the appropriate table and create secondary indices as soon as a file finished being uploaded.  It was quite fast, 

On cutover day, I just had to move the current month's data from those tables, along with the (relatively) small amount of data in tables that get UPDATEd and then ran the CREATE INDEX and ALTER TABLE ... ADD FOREIGN KEY statements.

Honestly, that 10GB table is small enough that I'd leave until cutover day.

Note that I had six hours to do the final data moves.

  • Are there specific PostgreSQL settings or configurations we should pay attention to for optimizing performance, especially for large datasets and DB2-style workloads?

What's a DB2-style workload?
 

    We are keen to ensure performance

    The default autovacuum settings are too conservative IMO, so I set them thusly:
    autovacuum_analyze_scale_factor = 0.03
    autovacuum_vacuum_scale_factor = 0.03
    autovacuum_vacuum_insert_scale_factor = 0.03
    default_statistics_target = 5000

    And the memory configs for a system with 128GB RAM
    effective_cache_size = 108GB # RAM less room for Linux & Carbon Black
    shared_buffers = 32GB       # 25% of RAM
    work_mem = 164MB            # ECS/100 (expected max connections)
    maintenance_work_mem = 8GB

    Your mileage will vary.

    , data integrity,


    Build the PG instance with checksums enabled.  It's 2025; the computational overhead is minuscule.
    If you use Foreign Key constraints, verify that supporting indices exist.  They probably already do exist, but can't hurt to check...

    PgBackRest is my go-to backup and restore program.  Multi-threaded, with automatic PITR, compression and encryption.

    and scalability throughout this migration. Any insights—particularly from those with experience in similar large-scale PostgreSQL implementations—would be highly appreciated.


    You'll probably want to partition those big tables. Note, though, that PG requires that the partition field be part of the PK.

    If your existing PKs have transaction_date, invoice_date, etc as part of the PK, then hurray; your job is easy.  If not (which is likely the case if the PK is synthetic), then I'd partition by some number PK values.  Every 10,000,000 PK values or something.  I'd study the distribution patterns of the data.  For example, if there is lower volume in January through March, and higher volume in summer and December, then maybe partition every 7,000,000 PK values in the typically slow months, and 14,000,000 PK values in the busy months.  Gaps between PK values due to sequence caching will also affect that number.

    --
    Death to <Redacted>, and butter sauce.
    Don't boil me, I'm still alive.
    <Redacted> lobster!

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