Summary of "Стратегия тестирования баз данных — вебинар"

Webinar overview

This summary covers a webinar by Alexander (database engineer / team lead at Bestos Technologies) about database testing strategies for large schemas and migration projects (source → target DB). The talk focused on types of database tests, migration-specific challenges, test generation and automation, running tests at scale, measuring code coverage, and recommended processes and tooling.

Key concepts and testing types

• Test categories:

  • Unit tests: functions and procedures.
  • CRUD tests: insert/update/select/delete.
  • Integration tests: database ↔ other components.
  • Stress / load tests and performance tests.
  • Permission and configuration tests.

• Test structure:

  • Single test scripts (SQL) with parameterization.
  • Grouping of tests into projects or suites for individual or batch runs.

• Migration-specific checks:

  • Object presence and validity after migration.
  • Data comparisons, including type and whitespace/format issues.
  • Behavioral equivalence of stored code (procedures/functions).
  • Preservation and formatting of user-facing error messages.
  • Differences between DBMS dialects and transaction modes.

Test-first approach recommended: write tests for the source and expected target behavior before migration where possible.

Large-schema / migration project realities

• Example project scale cited: ~6,000 tables/views, ~5,000 triggers, ~100,000 stored procedures/functions across layers.
• Team and environment constraints:
  • Small test teams (2–5 people) vs huge object counts.
  • Limited test environments and many customer configurations.
  • Repository size limits (e.g., Bitbucket 2 GB) and VPN/access constraints.
• Maintenance cost: tests must be updated as refactorings and conversion rules change — recurring project expense.

Test coverage strategies and scoping

• Scoping choices to decide with stakeholders:

  • Test only objects actually called by applications (top-layer).
  • Cover layer-by-layer (progressively deeper).
  • Cover only migrated objects.
  • Attempt whole-database coverage (very costly).

• Prioritization guidance:

  • Start with objects exercised by applications (triggers, tables, views) to get early value.
  • Expand deeper by layer as time and budget allow.
  • Decide up front whether to include auxiliary objects (DB-admin, third-party).

Test creation approaches and automation

• Creation approaches:

  • Manual authoring (slow; ~15 minutes per test on average).
  • Template-based project generation.
  • Intelligent automated generation using schema metadata.

• Automation practices described:

  • Inspect the data dictionary (column types, defaults, constraints) to generate inputs.
  • Use customer-provided test data and application trace logs to derive parameters and sequences.
  • Use Perl scripts to generate project/test XML templates; store individual tests as editable JSON for quick fixes.
  • Use templates so mass changes (e.g., conversion-rule changes) can be applied programmatically.

• Benefits:

  • Faster ramp-up and less tedious than fully manual work.
  • Easier bulk adaptation after schema or type changes.

Test execution at scale

• Orchestration:
  • Jenkins runs test projects across cloud VMs/instances with multiple parallel runners to reduce wall time.
• Performance example:
  • Serial run on one 16 GB / 8-core machine: ~15–17 hours.
  • Parallel runs (2 machines) roughly halve run time; 4+ parallel instances can bring runs under a working day.
• Test design goal: minimize test mutual interference (locking) so concurrency is effective.

Code coverage: measuring and reporting

• Coverage metrics:
  • Count of covered objects.
  • Line-level vs basic code-block coverage.
  • Coverage ratio = covered blocks / total blocks.

• Best practices:

  • Start collecting coverage from day 0 to measure progress.
  • Define what “100%” means before collecting statistics.
  • Exclude unreachable code using pragmas/annotations so coverage is meaningful.

• Instrumentation and tooling examples:

  • Oracle: built-in coverage collection in recent versions (e.g., Oracle 12.2) — enable collection, run tests, disable, store stats.
  • Piggly (Ruby): compiles functions/procedures with debug calls to log execution and produce HTML reports. The speaker’s team adapted it to write debug data to DB tables.
  • Custom pipeline: collect logs/traces, filter by test-run sources (IP addresses) or use separate schemas to distinguish test vs application traffic, then merge coverage results.

• Reporting:

  • Jenkins + test-runner output provides pass/fail counts and code-coverage per schema/project.
  • Example reported scale: ~24k CRUD tests for ~3.5k tables, ~25k unit tests for ~15k functions, 39 test projects; fail rate ~0.2% for “core” tests in their setup.

Operational recommendations and best practices

• Define measurement scope and what “100%” means before collecting statistics.
• Decide scope with stakeholders early to avoid wasted effort.
• Automate test generation and project creation from day 1 where possible.
• Use metadata (dictionary) and application traces to generate realistic test inputs.
• Keep tests independent to enable parallel runs and reduce runtime.
• Plan and budget for test maintenance — updates are required as conversion rules or DB logic change.
• Separate test traffic from application traffic using separate schemas/databases or filtering to support parallel execution and merging of coverage data.
• Store tests in editable formats (XML/JSON) so individual tests can be quickly inspected and edited without reloading whole projects.

Tools and product mentions

• Bestos Technologies internal “test organizer” module (free mode available on their website).
• Jenkins for orchestration and parallel execution.
• Perl scripts for project/test generation from dictionary metadata.
• Piggly (open-source Ruby project) for instrumentation and coverage reporting (adapted).
• Bitbucket (mentioned for repository-size constraints).
• General use of cloud VMs / CI runners.

Practical outcomes and numbers from the presented project

• Object counts: thousands of DB objects (6k tables/views, 5k triggers, ~100k SPs/functions).
• Tests created: ~24k CRUD tests (covering ~3.5k tables), ~25k unit tests (covering ~15k functions), 39 projects.
• Test execution times: single-server serial runs ~15–17 hours; parallelization reduces runtime proportionally.
• Coverage behavior: adding new code increased object count coverage but reduced block-level percentage — illustrates the need for a consistent measurement baseline.

Challenges highlighted

• Limited staff and environments for very large projects.
• Repository size and access constraints (e.g., Bitbucket 2 GB cap).
• Mass refactorings requiring sweeping test updates unless templated/automated.
• Sparse initial documentation — tests often become the best living documentation.
• Difficulty convincing customers to invest in coverage and ongoing maintenance.

Actionable steps and tutorial-style guide

1. Define scope: decide which schemas and objects to include.
2. Prefer tests-first for new or changed objects when possible.
3. Use dictionary metadata and production traces to auto-generate realistic tests.
4. Store tests in editable formats (JSON/XML) to support quick edits and templated mass changes.
5. Automate project/test generation and CI execution early in the project.
6. Collect coverage metrics from day 0 and agree on what is measured.
7. Use pragmas/annotations to exclude unreachable code blocks from coverage calculations.

Speakers and sources

• Alexander — database engineer and team lead, Bestos Technologies (primary presenter).
• Bestos Technologies — provider of migration and testing services; maintains an internal test/organizer product.
• Other mentions: Katya (answered a Q&A question), Piggly, Jenkins, Bitbucket.

Category

Technology

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