Database Scalability Services by Khajavi

For us, database performance and scalability cover everything from how quickly individual queries respond to how your system behaves as data and traffic grow over time. It includes schema and index design, query tuning, caching strategies, transaction patterns, infrastructure configuration, ETL and BI workloads and the way all of these interact under real user load.

We work most often with relational databases such as SQL Server, PostgreSQL and MySQL in cloud and on-premise environments. Because khajavi.tech also focuses on .NET and modern web applications, SQL Server shows up a lot, but the principles we apply are similar across engines. When specialised stores are needed for particular workloads, we design around that too.

Typical signs include slow or inconsistent response times, sudden spikes in CPU or I/O during certain operations, queries that sometimes time out, deadlocks, blocking chains, recurring incidents during peak traffic, ETL jobs that push into business hours and developers warning that “we should not touch that part of the database.” If any of these are familiar, it is usually worth investigating.

We start by looking at how your system behaves under real conditions: monitoring dashboards, slow query logs, traces, incident histories and code paths. From there we drill into specific queries, schemas, indexes and patterns in your application. The diagnosis is a mix of instrumentation, analysis and conversations with your team about what actually hurts day to day.

We usually need read access to production metrics, logs and sometimes anonymised data or query plans. When direct access is not possible, we work with exported plans, captured traces and staging environments that mirror production closely. Security, privacy and compliance come first; we adapt to the policies you already have in place.

The goal is always to minimise or avoid downtime. Many improvements – such as adding or adjusting indexes, query-level tuning or configuration changes – can be done with no or very low impact when planned carefully. For risky operations, we schedule maintenance windows, use rolling strategies or blue-green approaches and ensure rollback plans are in place.

We work at multiple levels. Sometimes a query or index change is enough. In other situations, a small schema adjustment, a better separation of workloads or the introduction of a replica or warehouse will make a bigger difference. We propose options with clear trade-offs so you can decide what level of change is appropriate for your current stage and risk appetite.

Hardware upgrades can be a reasonable short-term step, especially if you are clearly resource-constrained. But when hardware is repeatedly increased without addressing underlying inefficiencies, costs rise while performance still disappoints. We look at resource usage, query patterns and growth trends to decide whether tuning, hardware, or a mix of both will give you the best return at this moment in time.

Yes. We analyse how your system behaves under peak loads, identify which operations create the most pressure and design strategies such as caching, replicas, prioritisation of workloads and scheduling of heavy jobs. The goal is to handle spikes without resorting to “all hands on deck” every time your traffic increases or you run a campaign.

ORMs are valuable tools, but they must be used consciously. We do not require you to abandon your ORM. Instead, we examine generated queries, address patterns like N+1 queries, overly broad selects and misused features such as lazy loading in hot paths. Where necessary, we introduce targeted raw SQL or stored procedures to handle critical operations more directly and predictably.

Multi-tenant setups come in many forms: shared databases with tenant keys, separate schemas per tenant, or separate databases entirely. We review the current approach, evaluate how it scales in terms of performance, isolation and operational complexity and suggest improvements – whether that is better indexing by tenant, partitioning or gradual separation for very large tenants that need special handling.

We focus on indexes that support actual workloads. That means analysing slow queries, high-frequency queries and important business operations. For each index, we weigh its benefits against its cost on writes and maintenance. At the same time we look for rarely used or overlapping indexes that can be simplified or removed to improve write performance and reduce maintenance overhead.

Performance should not be a one-time project. As data, features and usage patterns change, what worked last year may not be ideal now. Many teams benefit from periodic reviews – for example, once or twice a year – plus more focused work when major feature launches or architectural changes are planned and before big growth milestones like new regions or large customers go live.

We treat transactional (OLTP) and analytical (OLAP) workloads as distinct concerns, even if they currently live in the same database. Often that means designing data flows and possibly separate stores for analytics, so that heavy reporting and BI work do not slow down user-facing operations. The exact approach depends on your stack, tools and scale, but the principle is to stop them fighting over the same resources.

Yes. khajavi.tech also focuses on BI and data platforms. We can help you design or refine your data warehouse schema, ETL or ELT pipelines and the way they interact with your production databases. The aim is a setup where analytics are trustworthy and timely without putting unnecessary load on live systems or forcing analysts to work around missing data.

We regularly work with managed services offered by major cloud providers. While core database concepts remain the same, managed environments have their own configurations, limits and features. We help you understand what your provider offers, how to configure it sensibly and how to use it effectively instead of defaulting to the most expensive tier or relying on magic “auto” settings that hide problems rather than solving them.

Security and compliance come first. We follow the access boundaries you define, work with anonymised or masked data when appropriate and document what we need to see and why. If you have specific regulatory constraints, we adapt our approach so that performance improvements do not conflict with your obligations and audits can clearly see what changed and when.

Performance work can reduce costs by making better use of existing resources, delaying or avoiding unnecessary hardware upgrades and identifying workloads that can be moved to cheaper, more appropriate environments. In some cases, we may recommend targeted increases in capacity where it clearly pays off in stability, user experience and reduced incident firefighting time for your team.

We encourage a combination of staged testing and careful rollout. That includes testing in environments that resemble production as closely as possible, capturing query plans and performance metrics and then rolling out changes gradually with monitoring. For high-risk operations, we plan explicit rollback paths in advance so the team knows exactly what to do if a change behaves differently than expected in the wild.

Yes. Observability is central to sustainable performance. We help identify the right metrics to track, set sensible thresholds for alerts and ensure that dashboards are understandable to the people who need them – not just database specialists. The result is a monitoring setup that supports day-to-day decisions instead of generating noise that everybody learns to ignore.

We usually start with quick wins that have clear impact and limited risk: fixing obvious query issues, adjusting problematic indexes, cleaning up heavy jobs. At the same time, we identify deeper structural problems and propose an incremental roadmap for addressing them. That way you see improvements early while still moving toward a more scalable architecture over the following months, not just days.

Yes. Locking and deadlocks are often symptoms of how work is scheduled and how transactions are scoped. We analyse patterns of access, isolation levels and specific sequences of operations that lead to contention. Solutions might involve reorganising work, splitting operations, adjusting isolation levels or modifying how and when long-running jobs run so they do not collide with peak user traffic anymore.

We first understand what those jobs do and why they need to run when they do. Then we explore options such as rescheduling, breaking jobs into smaller chunks, moving them to replicas or warehouses, or optimising the underlying queries and schema. The intention is to keep critical live traffic responsive while batch work still gets done reliably and within acceptable time windows for the business.

We design caching so that it clearly improves user experience without hiding deeper problems or creating stale-data issues. That means deciding what to cache, how long, where to store the cache and how to handle invalidation. We also look for places where “just cache it” has been used as a band-aid for inefficient queries that should instead be fixed or redesigned to be fast enough without relying on a fragile cache.

Write-heavy systems require careful attention to schema, indexing, transaction patterns and sometimes data distribution. We look at how writes are grouped, whether some can be buffered or batched, how often the same hot rows are touched and whether parts of the workload can be moved to specialised stores or partitioned tables. The specific strategy depends on your data model, consistency requirements and the types of operations your product needs to support at scale.

Constraints and validations cost something, but they also protect data quality and simplify reasoning for developers. We help you decide where constraints are essential and where they might be better handled in application code or data pipelines. Often, better schema design and indexing can offset the cost while retaining the benefits of strong data integrity and fewer confusing edge cases for both users and devs.

Partitioning starts to make sense when individual tables become very large and queries naturally target specific slices of data – for example, time ranges, regions or tenants. We look at your query patterns, maintenance routines and growth trajectory to decide whether partitioning would genuinely help or simply add complexity without clear benefit. When we do recommend it, we plan migration paths that keep risk and downtime under control.

When you reach a point where a single database or cluster can no longer handle your scale, sharding becomes an option. We help you evaluate sharding keys, trade-offs around cross-shard operations, operational complexity and migration paths. Because sharding is a big step, we recommend it only when other strategies are no longer sufficient and when your team is ready to operate a sharded environment responsibly.

In microservices, databases often become more isolated: each service may own its own data and schema. That changes how transactions, queries and reporting work. We help you design patterns for data ownership, communication, reporting and consistency so that performance and scalability remain manageable even as the number of services grows and the system evolves over multiple years and teams.

Changing database engines is a major decision. In many cases, significant performance improvements can be achieved without switching, by tuning the existing system. When a switch is truly justified – for example, because your workload no longer fits the current engine – we help plan and execute migrations in a controlled way, including data moves, compatibility issues and cut-over strategies that protect your users.

Yes. We often work in two layers: immediate stabilisation (reducing the frequency and severity of incidents) and longer-term design. Quick actions might include index tuning, re-scheduling heavy jobs or targeted query optimisation. In parallel, we map out the larger structural changes needed for durable scalability so you are not stuck in firefighting mode forever.

We look at historical data growth, current models and product roadmap to estimate how large tables and workloads will become over several years. From there we plan archiving, partitioning, warehouse offloading or other strategies to keep performance sustainable. The aim is to avoid the situation where a table silently grows until it becomes a crisis at the worst possible time for the business.

Yes. This is common and often the source of unpredictable performance. We assess which analytical workloads can be moved to replicas, warehouses or dedicated analytical stores. We then design data flows so that BI has what it needs without impacting day-to-day product usage, and we make sure the new setup is observable so teams actually trust the data they are seeing there.

Length depends on scope. A focused performance audit with targeted fixes might run over a few weeks. Larger engagements, including architectural changes or substantial data platform work, can span several months. Early in the process, khajavi.tech proposes a realistic timeline based on your priorities, team capacity and the current state of your system, then adjusts as we learn more together.

We work alongside your engineers, product managers, data people and operations teams. That can mean pairing on query tuning, reviewing code, explaining findings and decisions and helping shape internal practices around performance. The goal is not just to improve the system but to transfer understanding so your team grows stronger, not more dependent, over time.

Many systems we help are exactly in that state. We approach them with curiosity and care, mapping out what is actually in use, where risky assumptions exist and how to bring more structure gradually. Documentation often emerges as a by-product of the analysis, giving your team a clearer picture than they had before and reducing the fear of touching critical parts of the system after improvements are made.

Yes. If you operate in regulated industries, we adapt our access patterns, documentation and change processes accordingly. That includes limiting access, using anonymised datasets where possible and aligning performance work with your existing governance and audit requirements so that improvements pass scrutiny without creating compliance surprises later on.

khajavi.tech is comfortable working with fully remote, international teams across time zones. We use async updates, focused calls and clear written documentation so everyone stays aligned, regardless of location. This is particularly important for performance work, where context and history matter as much as specific query plans or metrics screenshots shared in a channel or document.

Products and organisations for whom data and uptime are central to value delivery benefit the most: SaaS platforms, marketplaces, analytics tools, internal operational systems and any web application where slow or unstable data layers directly affect customers and teams. If your data layer feels like a constraint, this work usually has a high return on attention and budget.

You can reach out via the contact options on khajavi.tech, or through WhatsApp or email. Share a short description of your product, current performance pain points and what you hope to achieve. From there, we can propose next steps, often starting with a focused performance and scalability assessment to understand where we can create the most value fastest.