db2look with ibm_db: Alternatives for DDL Generation
- THE MAG POST
- Aug 19
- 11 min read
db2look with ibm_db is a topic that sits at the intersection of practical database administration and Python scripting. In many real-world workflows, developers want to reproduce exactly how a database object was defined, including tables, indexes, constraints, and dependent objects. The IBM Db2 environment provides robust tooling, yet the Python ibm_db library focuses on data access rather than DDL generation. This article explores how to approach DDL extraction without a direct ibm_db analogue to db2look, and demonstrates both native catalog-based reconstruction and safe shelling-out approaches. We will compare strategies, highlight trade-offs, and propose a reliable workflow that you can adapt in your own projects, keeping the spirit of db2look with ibm_db alive.
By the end, you will understand practical options to recreate DDL, including using the undocumented DB2-LUW procedure sysproc.DB2LK_GENERATE_DDL, querying catalog views, or shelling out to the db2look tool when the workstation contains a Fat Client. The aim is not to chase a perfect one-to-one ibm_db feature, but to provide a disciplined path that yields equivalent scripts safely and predictably. As you read, consider how permissions, version differences, and catalog accuracy influence results. With the right approach, db2look with ibm_db becomes less of a missing feature and more of a well-supported, auditable process embedded in your data engineering toolkit.
This article examines how to approach the challenge of extracting complete DDL definitions when using the Python ibm_db library, given that there isn't a direct ibm_db analogue of the db2look tool. We will explore practical routes for reproducing table definitions, indexes, and constraints, and compare the trade-offs of each path. The goal is to empower you to generate reliable, auditable scripts without being stranded by missing functionality in ibm_db. By walking through catalog-based reconstruction, undocumented-db2-LUW procedures, and safe shelling-out, you can maintain the spirit of db2look with ibm_db in your data engineering toolkit.
db2look vs IBM Db2: What ibm_db Can and Cannot Do
In many Db2 workflows, developers want a precise replay of how a database object was defined, including tables, indexes, constraints, and dependent objects. The IBM Db2 environment ships powerful DDL-generation capabilities, but the Python ibm_db library is primarily a data-access interface. The absence of a built-in db2look-equivalent in ibm_db means you must combine alternatives to achieve a similar outcome. This section outlines why a direct ibm_db replacement does not exist and sets the stage for robust, workmanlike solutions that maintain fidelity to the original definitions.
db2look concept
db2look is designed to extract a complete, ready-to-run DDL script for database objects. It can cover tables, indexes, keys, constraints, and dependent objects, outputting a script that mirrors the live schema. When we translate this concept to ibm_db, we must simulate the same fidelity using available interfaces such as catalog views, optional stored procedures, or external tooling. The key idea is to recreate the exacting granularity of DDL generation without compromising security or portability. The challenge is not merely to dump definitions but to assemble them with correct dependencies and ordering, as db2look would do automatically.
In practice, the goal is to compose a script that can recreate the schema accurately. That means recognizing that a direct one-to-one ibm_db replacement does not exist, and instead leveraging a mixture of catalog-driven queries, undocumented procedures when permitted, and external tooling where appropriate. This approach aligns with how teams build robust data pipelines that survive DB2 upgrades and environment shifts, preserving the fidelity of the original DDL through careful reconstruction.
ibm_db limitations
The ibm_db library excels at data access and execution of SQL, but it does not expose a dedicated API that returns a full DDL script for a database object. You cannot rely on ibm_db alone to emit a turnkey CREATE statement for a table with all its indexes and constraints. Moreover, some approaches require elevated privileges or access to server-side features that may be restricted in production environments. This section emphasizes that while ibm_db is not missing as a tool, you must compose a multi-pronged strategy to obtain equivalent DDL responsibly and reproducibly.
To bridge the gap, you can either shell out to db2look from a workstation with a Db2 client installed, or invoke a server-side process (when allowed) that runs db2look and returns the script to your Python process. Additionally, you can rely on the undocumented DB2-LUW procedure sysproc.DB2LK_GENERATE_DDL() (subject to rights), or you can craft your own queries against catalog views to re-create DDL step by step. Each path has its own prerequisites, risks, and benefits that we will analyze next.
Catalog-based DDL generation with ibm_db
If you cannot execute db2look directly, you can reconstruct DDL by interrogating catalog views and assembling the statements in a controlled order. This method leverages system catalogs such as SYSIBM.SYSTABLES, SYSIBM.SYSINDEXES, SYSIBM.SYSTABCOLS, and related views to piece together the CREATE TABLE, CREATE INDEX, and constraint definitions. While more manual, this approach gives you full control over the generated scripts and helps you build repeatable pipelines that operate strictly within the privileges of the invoking user. It also provides a transparent audit trail for schema evolution.
To implement this approach, you query catalog views to collect column definitions, data types, nullability, default values, and constraints, then assemble DDL fragments in the correct dependency order. You can store intermediate results in a staging area and render final scripts that you can store in version control. The process scales with automation; you can automate the assembly for an entire schema by iterating over tables and their dependent objects. This method mirrors the intent of db2look with ibm_db, albeit with more manual orchestration.
Query catalog views
Querying catalog views is a powerful, portable approach. It requires you to gather object metadata, such as table definitions, column specifications, and index configurations, and then assemble the DDL statements programmatically. The reliability comes from using database catalogs, which are maintained by the DB2 server and reflect the current schema state. With careful scripting, you can reconstruct a faithful representation of the original DDL without depending on external tooling.
Practical steps include enumerating tables, collecting column types and constraints, assembling CREATE TABLE statements, then adding CREATE INDEX and constraint definitions in a deterministic order. This strategy is robust across environments and DB2 versions, provided catalog schemas remain consistent. Keep in mind that the complexity grows with advanced features like partitioning, triggers, and views. Nevertheless, catalog-based reconstruction is a solid foundation for a portable DDL-generation workflow using ibm_db.
Stored procedures approach
Another option is to leverage stored procedures that generate DDL, when available. The undocumented but widely referenced sysproc.DB2LK_GENERATE_DDL() can yield DDL scripts for tables and indexes if your account has the necessary rights. Using such a procedure from Python via ibm_db requires careful security considerations and clear auditing. While not officially supported for all environments, this approach can provide a direct route to DDL when permitted, with results that you can capture and persist for versioned schema definitions.
Implementing this approach involves invoking the procedure against target objects and capturing the returned DDL text. You should validate the output, handle privileges securely, and maintain a log of who invoked the procedure and when. When used responsibly, this method can complement catalog-based generation and provide a pragmatic bridge to db2look-like functionality from ibm_db.
Using the DB2LK_GENERATE_DDL stored procedure (sysproc)
The undocumented DB2-LUW procedure sysproc.DB2LK_GENERATE_DDL() is referenced in several approaches to obtain DDL from Db2. While not part of the public API, its usage pattern is discussed in internal guides and community examples. If your environment permits, calling this procedure can produce DDL for a specified object, which you can then programmatically handle in Python. Always verify compatibility with your DB2 version and ensure you have explicit permission to invoke such server-side facilities.
What it does
In representative use, the procedure generates a textual DDL script that recreates the definition of a given database object, such as a table or an index. The outcome is intended to mirror the original create statements, including dependencies. This can save significant time in environments where external tooling is restricted or where you rely on server-side generation for consistency across deployment targets.
Interacting with this procedure from Python via ibm_db involves issuing a CALL or SELECT statement that references DB2LK_GENERATE_DDL, then fetching the resulting script text. While convenient, this approach requires careful permission management and thorough testing to ensure the returned DDL aligns with the target DB2 version and dialect. Always audit and validate results as part of your data governance processes.
Permissions and caveats
Access to sysproc.DB2LK_GENERATE_DDL() is controlled, and not all environments expose it publicly. If you do have rights, document who can invoke it and under which conditions. Server-side generation can be sensitive to DB2 version differences and object state, so include version tagging and environment checks in your workflow. This method complements catalog-based reconstruction and shelling-out strategies, offering another path to the same end: a faithful DDL script ready for review or deployment.
Security-conscious teams should implement strict auditing and restrict usage to trusted automation pipelines. In regulated environments, consider embedding the DDL output in a controlled repository with access controls and change-management procedures. When used properly, sysproc.DB2LK_GENERATE_DDL() can be a powerful ally in the db2look with ibm_db toolkit.
Shelling out from Python to db2look
When the workstation running Python has the Db2 fat client installed and the database catalogued, you can shell out to the db2look command line tool to generate DDL scripts. This approach parallels the traditional db2look workflow and can be implemented in a controlled, auditable manner from Python via subprocess. The advantage is fidelity to the original tool, while the drawback is reliance on a specific client and potential platform dependencies. Use this method when you require exact parity with db2look output and your environment supports the Db2 client installation.
Using subprocess
In practice, you can invoke db2look with a carefully prepared command line from Python, capture stdout/stderr, and store the resulting script for versioned deployment. This technique preserves the exact semantics of the original tool and can be integrated into CI/CD pipelines. Always validate that the target catalog is the one you intend to script and monitor the command's exit status for reliability.
Shelling out should be considered a pragmatic fallback when native ibm_db capabilities fall short and when you have a dependable client environment. It is essential to manage credentials securely and to ensure the generated scripts reflect the correct schema, database, and object scope. This approach complements the other methods discussed and offers a practical route to obtaining DDL in real-world workflows.
Security considerations
Running external tools from a Python process introduces several security considerations: protecting sensitive credentials, validating input parameters, and restricting execution to trusted code paths. On production systems, use securely stored connection details, avoid hard-coded values, and log all invocations with user identifiers and timestamps. When combined with proper access controls, shelling out to db2look can be a robust component of a comprehensive DDL-generation strategy.
Practical workflow and best practices
A disciplined workflow combines the strengths of catalog interrogation, server-side generation, and safe tooling to achieve deterministic DDL output. Start with a clear scope: identify the target schema, objects, and dependencies. Use catalog queries to enumerate objects, then apply a stable assembly order to create a complete DDL script. When automation is essential, integrate a fallback to server-side generation for edge cases and rely on shelling out only when you can guarantee client-server alignment. The db2look with ibm_db approach thrives when you treat DDL generation as a repeatable, auditable process rather than a one-off task.
Step-by-step workflow
1) Identify target objects and their namespaces. 2) Collect metadata from catalog views. 3) Assemble DDL fragments in dependency order. 4) Validate scripts against a reference DB2 instance. 5) Store results in version control with change history. 6) Implement access controls and auditing for all generation steps.
In addition to the above, maintain a changelog that records the rationale behind each DDL generation, especially when legacy objects or custom definitions are involved. This makes the db2look with ibm_db workflow auditable and future-proof. Finally, periodically verify that the generated DDL aligns with the actual database state after migrations or upgrades. By adopting a structured workflow, you reduce drift and improve governance across data environments.
Performance and permissions
Performance considerations include caching catalog reads, parallelizing independent object extractions, and minimizing round-trips between Python and the database. Permissions should be scoped to the minimum privileges required for DDL generation: read access to catalogs, and any rights needed for optional procedures or shelling-out tools. Auditing and strict access control ensure your generation process remains secure and reproducible over time.
Ultimately, the db2look with ibm_db workflow benefits from combining multiple approaches. Leverage catalog-based reconstruction for portability, server-side generation for fidelity and simplicity, and shelling-out when a trusted client-based script best fits the environment. A well-balanced strategy yields reliable, auditable SQL definitions suitable for review, deployment, and governance.
Final Solution
There is no single ibm_db feature that mirrors db2look exactly. The best practice is to combine catalog-based reconstruction, the undocumented sysproc.DB2LK_GENERATE_DDL() where permitted, and shelling-out to db2look from a properly provisioned workstation. This triad delivers faithful DDL scripts while preserving security and portability. When access to server-side procedures is restricted, rely on catalog views to recreate DDL step by step, ensuring reproducibility and auditability. In all cases, validate results against a reference instance and maintain versioned scripts for traceability.
Key takeaways emphasize that db2look with ibm_db becomes a flexible, multi-path pipeline rather than a single command. With careful permissions, disciplined workflow, and robust testing, you can achieve the reliability of the original tool while staying within the Python/ibm_db ecosystem. Use the documented approaches where possible, and reserve the undocumented procedure for carefully controlled scenarios with proper governance in place.
Similar Problems
Problem 1: Recreate a single table DDL using catalog views; Solution: assemble CREATE TABLE from column definitions and constraints.
Problem 2: Generate DDL for all tables in a schema; Solution: loop over catalog tables, assemble per-table DDL in dependency order.
Problem 3: Retrieve index definitions for a table; Solution: query SYSINDEXES and related catalog views, then assemble CREATE INDEX statements.
Problem 4: Use a server-side procedure to emit DDL for a view; Solution: call the procedure and capture the returned script.
Problem 5: Compare generated DDL with production state; Solution: run both scripts, diff the outputs, and log discrepancies.
Additional Code Illustrations
Code blocks follow outside HTML tags to illustrate practical variants and extensions of the main approach.
# Python: fetch DDL via catalog queries (illustrative, not production-ready)
import ibm_db
def fetch_table_ddl(conn_str, schema, table):
conn = ibm_db.connect(conn_str, '', '')
# This is schematic: actual catalog queries depend on your DB2 version
sql = f"SELECT * FROM SYSIBM.SYSTABLES WHERE CREATOR = '{schema}' AND NAME = '{table}'"
stmt = ibm_db.exec_immediate(conn, sql)
row = ibm_db.fetch_assoc(stmt)
ibm_db.close(conn)
return row
# Python: shell out to db2look (example)
import subprocess
def run_db2look_shell(db, out_file, schema=None, table=None):
cmd = ["db2look", "-d", db, "-e", "-o", out_file]
if schema:
cmd.extend(["-t", f"{schema}.{table}" if table else f"{schema}"])
subprocess.run(cmd, check=True)
# SQL-like note: illustrate catalog-based DDL piece (pseudo)
SELECT 'CREATE TABLE ' || rtrim(T.TABSCHEMA) || '.' || rtrim(T.TABNAME) || ' (...);'
FROM SYSIBM.SYSTABLES T
WHERE T.TABSCHEMA = 'MYSCHEMA' AND T.TABNAME = 'MYTABLE';
# Undocumented DB2LK_GENERATE_DDL usage (schematic)
-- This is a conceptual placeholder; actual syntax depends on environment
SELECT SYSIBM.DB2LK_GENERATE_DDL('TABLE', 'MYSCHEMA.MYTABLE') FROM SYSIBM.SYSDUMMY1;
# Halving-style illustrative variant (conceptual)
def generate_ddl_halving(x):
# Pseudo-logic to illustrate alternative strategy; not a real DB2 call
return f"DDL for {x}"
Aspect | Key Takeaways |
db2look concept | db2look provides full DDL; ibm_db has no exact equivalent |
Catalog-based approach | Query catalog views to rebuild DDL with control |
sysproc.DB2LK_GENERATE_DDL | Undocumented procedure; depends on rights and environment |
Shelling out | Run db2look from a workstation with Db2 client installed |
Workflow considerations | Permissions, version, auditing, and reproducibility matter |
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