tsql-fabric-debugger

Step-by-step debugger for T-SQL stored procedures on the Microsoft Fabric Warehouse — without changing a single line of your .sql.

This README is the quick tour. The deep dive — why Fabric has no native debugger, how the state engine works inside, the full API reference, and usage from a local machine, a Fabric notebook or AI agents/MCP — lives in docs/DOCUMENTATION.md.

Why

The Fabric Warehouse has no T-SQL debugger: no breakpoints, no watch, no way to run half a procedure. A whole procedure is a single statement to any SQL client, and DECLARE variables die at the end of each batch — so "run one piece at a time" doesn't work naively.

This library solves that by parsing the procedure in memory: it slices the body into steps and preserves variable state between them. Each step runs as one batch on the same session:

DECLARE <all variables>;
SELECT @a = ?, @b = ?, ...;        -- state re-injection (pyodbc parameters)
<original statement, untouched>;
SELECT '__hcap__', @@ROWCOUNT, @a, @b, ...;   -- capture of the new state

@@ROWCOUNT and ERROR_MESSAGE() are rewritten to preserve cross-batch semantics, and the procedure's BEGIN CATCH is emulated when a step fails. Everything runs inside a transaction with ROLLBACK at the end by default — nothing persists in the Warehouse unless you ask for it.

Installation

pip install tsql-fabric-debugger            # Fabric notebook
pip install "tsql-fabric-debugger[local]"   # local machine (az login)
pip install "tsql-fabric-debugger[all]"     # + pandas and sqlparse

Outside Fabric you need the ODBC Driver 18 for SQL Server installed and a valid az login.

IDE debugging (DAP)

tsql-fabric-dap speaks the Debug Adapter Protocol over stdio: point any DAP client (VS Code, nvim-dap, ...) at it and debug the .sql visually — gutter breakpoints, step over/into/out, variables pane, hover evaluation. Never commits; disconnect rolls back.

Rows a step returns are surfaced two ways: printed as a text table to the Debug Console, and emitted as a tsqlFabricResultSet custom DAP event ({line, sets: [{columns, rows, truncated}]} — one event per step, carrying all of that step's result sets) so a client can render them in a grid — the VS Code extension shows a Result Set panel beside the editor.

A VS Code extension that wires this up (F5 on a .sql, no launch.json needed) lives in editors/vscode/ — preview/MVP.

Interactive usage

from tsql_fabric_debugger import TSQLDebugger

dbg = TSQLDebugger(
    "prd_load.sql",                        # the original .sql, untouched
    params={"@year": 2015},                # test values
    server="<endpoint>.datawarehouse.fabric.microsoft.com",
    database="my_warehouse",
)

No local .sql? Debug the deployed procedure by name — the source is fetched straight from the warehouse (OBJECT_DEFINITION):

dbg = TSQLDebugger(proc_name="dbo.load_sales",
                   params={"@year": 2015}, server=..., database=...)

Prefer the context-manager form — it guarantees ROLLBACK + close even when an exception interrupts the session, so no orphan transaction is left holding locks on the warehouse:

with TSQLDebugger("prd_load.sql", params={"@year": 2015},
                  server=..., database=...) as dbg:
    dbg.run_all()

dbg.list_steps()      # list the numbered steps, without executing
dbg.step()            # run the next step ("step over": whole IF/WHILE)
dbg.step_into()       # step into an IF/WHILE: evaluates the condition
                      #   server-side, picks the branch, yields sub-steps
dbg.step_out()        # finish the current block/child and stop one level up
dbg.run_all(into=True)   # run to the end the step_into() way: every IF/WHILE
                         #   expanded, each loop iteration its own logged step
child = dbg.step_into()   # on an `EXEC dbo.child ...` step: fetches the child's
                          #   source from the warehouse and returns a CHILD
                          #   debugger sharing this session; debug it, then the
                          #   parent's next step() collects the OUTPUT values
                          #   (an unhandled child error reaches the parent CATCH)
dbg.run_until(15)     # run up to step 15 (breakpoint)
dbg.run_until("MAX(SEQREC)")   # ...or up to the step whose command has that text
dbg.jump_to(line=40)  # ...or position by file line; find_step() returns the number
dbg.show_vars()       # state of every variable (OUTPUT params included)
dbg.eval("@a * @b")   # evaluate one T-SQL expression with the CURRENT variables
dbg.stack()           # where am I? procedures + expanded blocks + iteration
dbg.sql("SELECT COUNT(*) FROM dbo.movements")   # query on the SAME session
dbg.jump_to(17)       # move the cursor without running earlier steps
dbg.set_var("@sqlSrc", "...")                   # build state by hand
dbg.run_step(17)      # run ONLY step 17 (does not move the cursor)
dbg.set_log_level("full")   # full command + SQL batch on errors
dbg.show_detail()     # last step in full (command + error + batch)
dbg.show_error()      # the step that FAILED, in full — the one-call idiom
                      #   after a failed run_all() (last_error() for the dict)
dbg.last_results()    # result sets the procedure itself produced
dbg.watch("(SELECT COUNT(*) FROM stg.movements)", "stg")   # tracked every step
dbg.log_at(8, "@fat")               # logpoint: print the value there, never stop
dbg.clear_logpoints()               # remove one logpoint (by line) or all
dbg.break_at(42, "@code = 31000")   # run_all() stops there when it's true
dbg.break_at(8, hits=4)             # ...or from the 4th pass on (once=True: fire once)
dbg.save_state("st.json")           # variables snapshot (JSON) ...
dbg.load_state("st.json")           # ... resume tomorrow with jump_to()
dbg.reset()           # rollback + replay from step 1 on a fresh session
dbg.close()           # ROLLBACK and close (commit=True to persist)

from tsql_fabric_debugger import diff_logs
diff_logs(log_2015, log_2016)       # divergences between two runs

Each step shows the line in the original file, the variables that changed and, on errors, the clean SQL Server message:

[  5] line   19 | ok   | SELECT @offset = ISNULL(MAX(id), 0) FROM dbo.movements
      -> @offset = 184230
[  6] line   21 | ok   | condition: @year < 2018
      -> condition = True

Batch mode and CLI

from tsql_fabric_debugger import run_procedure, summarize

log = run_procedure("prd_load.sql", params={"@year": 2015},
                    server=..., database=..., save_csv="log.csv")
summarize(log)   # "OK — all N steps ran" or "FAILED at step X (line Y): <msg>"

summarize(log) is the "just tell me what happened" verdict — it prints one line and returns the facts (ok, error_step, error_line, error, handled), so you rarely need the step-by-step API for a quick check.

tsql-debug prd_load.sql --param @year=2015 \
    --server <endpoint> --database my_warehouse --csv log.csv

Server and database can also come from the FABRIC_TSQL_SERVER and FABRIC_TSQL_DATABASE environment variables. Files without a CREATE PROCEDURE go through run_script() — split on GO lines when present, otherwise per statement via the library's own scanner (a ; inside a string never splits) — also inside a transaction with ROLLBACK by default. On the CLI, --param values can be quoted (--param @code='00123') to force a string and keep leading zeros; unquoted values infer int/float strictly (no scientific notation, no nan/inf). Exit codes: 0 = clean, 1 = at least one step recorded an ERROR (even if the procedure's CATCH handled it), 2 = usage/file error. .sql files may be UTF-8 (with or without BOM), UTF-16 with BOM (SSMS default) or cp1252.

Constructor parameters

Log columns

log_df() / --csv (procedure mode): step, line (file line), kind (stmt/declare/if_block/while_block/cond/exec/eval/return/throw/params), status (SUCCESS/ERROR/REGISTERED), rows_affected (only for captured steps; None otherwise), duration_s, command (truncated preview), changed_vars (truncated — show_detail() has the full values), result_sets, post_rollback (True for steps that ran after an error-triggered rollback), error. The script mode (run_script) logs a smaller schema: step, status, rows_affected, duration_s, command, error.

Security

• Entra ID authentication only: the notebook token on Fabric, AzureCliCredential elsewhere. No passwords, ever.
• Execution inside a transaction with ROLLBACK by default; close(commit=True) is an explicit decision.
• Variable re-injection through pyodbc parameters — no value concatenation into SQL.
• Only debug files you trust: the .sql is code executed under your identity, and rollback-by-default is a convenience, not a security boundary (a COMMIT hidden in dynamic SQL persists — the parser warns about literal and string-embedded transaction control, and flags EXEC calls it cannot see into).
• Console output and CSV logs contain real data from the warehouse (variable values, result-set rows) — treat them like the data itself.

Operational notes (read before debugging a shared warehouse)

• Required permissions: the debugger does NOT EXECUTE the procedure — it runs the body's statements directly under your identity. You need SELECT/INSERT/UPDATE/DELETE on every object the procedure touches (ownership chaining does not apply), and row-level security/column masks apply to you, which can make results diverge from a real execution.
• Long transactions hold locks: the debug session keeps one transaction open from the first step until close(). Locks from completed steps are retained the whole time — an interactive session parked for an hour blocks concurrent writers and DDL on the touched tables. Debug in a dev warehouse/schema, use the with form, and close() as soon as you are done. step_timeout bounds a running statement only.
• The session identifies itself as tsql-fabric-debugger in sys.dm_exec_sessions.program_name.
• Instances are not thread-safe (one session, one shared environment).
• Memory: the full text of every executed batch is kept for show_detail(); very long sessions over procedures with multi-MB dynamic SQL grow accordingly.

Semantics preserved

• RETURN ends the debug wherever it appears — top level, guard clause inside an IF, or inside the emulated CATCH — just as it would end the real execution.
• Each BEGIN TRY gets its own emulated CATCH; after the CATCH handles the error, the debug skips the rest of that TRY (nested TRY blocks included) and continues after END CATCH — the same T-SQL semantics. A THROW inside the CATCH aborts the debug like the real re-raise would.
• The full ERROR_*() family works in the emulated CATCH: ERROR_MESSAGE(), ERROR_NUMBER(), ERROR_PROCEDURE(), ERROR_LINE() (the file line of the failing step), plus ERROR_SEVERITY()/ERROR_STATE() as RAISERROR-style defaults (16/1) — the driver does not expose the real ones.
• DECLARE inside the CATCH (the classic DECLARE @msg = ERROR_MESSAGE();) is emulated correctly.
• IF x SET a = 1 ELSE SET a = 2 — no ; before the ELSE — parses and steps correctly.
• Bodies without an outer BEGIN...END (bare statements, or starting straight at BEGIN TRY) are supported.
• Session SET options (NOCOUNT, XACT_ABORT, ...) run unparameterized so they persist for the following steps, as they would in a real execution.
• SELECTs produced by the procedure itself (diagnostics, samples) are captured and displayed (last_results()), not discarded — including the ones produced before a step failed.
• Procedures with inner COMMIT/BEGIN TRAN raise a parse-time warning — including transaction keywords spotted inside string literals (dynamic SQL); EXEC calls are flagged as opaque.
• After an error-triggered rollback, the debug warns that following steps run against post-rollback data, and marks them with post_rollback=True in the log.
• step_timeout=<seconds> in the constructor bounds every step; Ctrl+C cancels the running statement server-side and rolls back.

Practical limits

• Table variables: DECLARE @t TABLE (...) is declared in every batch (references compile) and triggers a warning, but its content does not survive across steps — step over the block that fills and consumes it, or use #temp.
• step_into on a WHILE with BREAK/CONTINUE falls back to atomic mode (with a warning).
• GOTO, cursors and WAITFOR are out of scope.
• A single failing statement keeps the previous variable values (same T-SQL semantics); when an atomic block fails, use jump_to(n) + step_into() to pinpoint the exact statement. After an error, the ROLLBACK undoes the data effects of earlier steps, but the captured variables remain.

Tests

pytest                       # unit (offline, no warehouse)
FABRIC_TSQL_SERVER=... FABRIC_TSQL_DATABASE=... pytest -m integration

Mutation testing

Suite quality is measured with mutmut:

pip install "tsql-fabric-debugger[dev]"
mutmut run        # ~1,700 mutants over scanner.py and parser.py
mutmut results    # survivors; `mutmut show <id>` prints the diff

The scope covers scanner, parser and engine — the engine is exercised offline through a programmable fake pyodbc session (tests/conftest.py), so its logic (state capture, error routing, CATCH emulation, breakpoints, nested EXEC, offload) is mutation-tested without a warehouse. connection/runner/cli stay out (thin driver/warehouse glue). tests/test_mutation_hardening.py pins exact parser/scanner behavior and tests/test_engine_offline.py drives the engine; the remaining survivors are dominated by equivalent mutants.

License

MIT © RedRex