Fast SQL Databases
Measured Query Performance|High-Volume .NET Data Processing|Correctness Under Load
Sphere 10 designs and improves SQL systems around their actual workload. We work across schema design, .NET data-access code, import pipelines and query execution, addressing the point where time and resources are being spent. Performance targets are agreed against representative data and concurrent business operations.
Measure the Work the Database Must Perform
We capture the important queries, their frequency, data volumes and response-time expectations. Execution plans, logical reads, lock waits and application timing show whether the problem is a missing index, an unsuitable query shape, excessive round trips or a transaction that holds resources too long. The baseline becomes the reference for every proposed improvement.
Schema changes follow access patterns and integrity requirements. We assess keys, indexes, data types and relationships alongside insert and update cost. A faster report must still return the correct records, and a faster import must preserve the constraints and recovery behaviour your application depends on.
Optimise the Whole .NET-to-SQL Path
Our BlockchainSQL Server product is a concrete reference for high-volume blockchain ingestion into SQL Server and queryable explorer data. Its processing separates source blocks, transformations and persistence. We apply that same end-to-end analysis to your imports, reporting services and transaction-heavy applications.
Sphere10 Framework.Data provides IDAC, SQL builders, schema inspection and explicit DACScope transactions. We inspect the selected provider before choosing batching or bulk operations, and use bound command parameters for untrusted values. NHibernate-based applications also receive attention to generated SQL, fetch plans and transaction scope so object mapping does not hide expensive database behaviour.
Accept the Improvement Without Losing Correctness
We compare before and after using the same dataset and operating conditions. Acceptance includes result equivalence, concurrent readers and writers, failed batches and rollback. For imports, we measure initial loading and ongoing updates separately, including the resources required to rebuild indexes or recover after interruption.
You receive the source changes, database scripts, measurements and deployment sequence. We document assumptions about data distribution, provider behaviour and hardware so the result can be repeated and revisited as the workload grows. The outcome is a defensible improvement to a working system, with enough evidence for your team to maintain it.
Platforms & Tooling
.NET C# SQL Server Sphere10 Framework NHibernate PostgreSQL SQLite
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