Expand TimeTrackingBenchmarks to 100k records with Bogus, LiteDB, and GROUP BY operations

[Copilot]

Scales time-tracking benchmarks from 1k to 100k records with realistic data generation, adds LiteDB comparison, GROUP BY aggregation tests, and Native AOT support.

Changes

TimeTrackingBenchmarks.cs

• Scale from 1,000 → 100,000 records
• Replace hardcoded test data with Bogus-generated realistic entries (10 project types, 10 task types, realistic names/dates/durations)
• Add GROUP BY benchmarks: SELECT project, SUM(duration) FROM time_entries GROUP BY project
• Add LiteDB comparison methods (Insert, Select, GroupBy)

SharpCoreDB.Benchmarks.csproj

• Enable Native AOT: <PublishAot>true</PublishAot>

README.md

• Add Time-Tracking Benchmarks table (100k records) with Inserts/Select/GroupBy/Allocs columns
• Include placeholder results showing expected improvements from 3 upcoming optimizations
• Document benchmark execution: dotnet run --configuration Release TimeTracking

Benchmark Coverage

9 methods across 3 databases (SharpCoreDB, SQLite, LiteDB) × 3 operations (Insert, Select, GroupBy)

// Bogus configuration for realistic data
var faker = new Faker<TimeEntry>()
    .RuleFor(e => e.Project, f => f.PickRandom(new[] { "Alpha Project", "Beta Development", ... }))
    .RuleFor(e => e.Task, f => f.PickRandom(new[] { "Code Review", "Feature Development", ... }))
    .RuleFor(e => e.User, f => f.Name.FullName())
    .RuleFor(e => e.Duration, f => f.Random.Int(15, 480));

Original prompt

Expand the SharpCoreDB.Benchmarks project to include comprehensive benchmarks for 100,000 time-entry records. Modify TimeTrackingBenchmarks.cs to:

1. Increase EntryCount from 1000 to 100000
2. Use Bogus library to generate realistic fake time-entry data instead of hardcoded strings
3. Add a new benchmark method for GROUP BY queries (e.g., SELECT project, SUM(duration) FROM time_entries GROUP BY project)
4. Add LiteDB comparison methods alongside SQLite and SharpCoreDB
5. Ensure GC and warmup metrics are collected (MemoryDiagnoser and warmupCount are already present)
6. Update SharpCoreDB.Benchmarks.csproj to support Native AOT by adding true
7. Update the README.md Performance Benchmarks table to include columns for Inserts, Select, GroupBy, and Allocs, with placeholder results showing improved performance after the 3 upcoming fixes

The benchmarks should focus on time-tracking scenarios with inserts, selects, and group by operations. Use Bogus to create varied project names, task names, users, and realistic datetime ranges.

This pull request was created as a result of the following prompt from Copilot chat.

Expand the SharpCoreDB.Benchmarks project to include comprehensive benchmarks for 100,000 time-entry records. Modify TimeTrackingBenchmarks.cs to:

1. Increase EntryCount from 1000 to 100000
2. Use Bogus library to generate realistic fake time-entry data instead of hardcoded strings
3. Add a new benchmark method for GROUP BY queries (e.g., SELECT project, SUM(duration) FROM time_entries GROUP BY project)
4. Add LiteDB comparison methods alongside SQLite and SharpCoreDB
5. Ensure GC and warmup metrics are collected (MemoryDiagnoser and warmupCount are already present)
6. Update SharpCoreDB.Benchmarks.csproj to support Native AOT by adding true
7. Update the README.md Performance Benchmarks table to include columns for Inserts, Select, GroupBy, and Allocs, with placeholder results showing improved performance after the 3 upcoming fixes

The benchmarks should focus on time-tracking scenarios with inserts, selects, and group by operations. Use Bogus to create varied project names, task names, users, and realistic datetime ranges.

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