This interview is with Ayush Raj Jha, Senior Software Engineer, Oracle Corporation.
For Featured readers, how do you introduce yourself and the kind of cloud, database, and API‑driven systems you build as a Senior Software Engineer in information technology and services?
I’m Ayush, a Senior Software Engineer based in Santa Clara with a decade of experience designing and delivering mission-critical cloud infrastructure, distributed systems, and data platforms that operate reliably at scale.
At Oracle, I architect backend services on OCI, handling everything from large-scale healthcare data exports to multi-region deployments across the US, UK, and UAE. My work spans the full lifecycle, from designing distributed processing frameworks and optimizing REST APIs with caching layers to automating infrastructure with Terraform and managing zero-downtime database migrations. I take seriously the details that keep systems healthy: latency, uptime, fault tolerance, and making deployments predictable and repeatable.
On the data side, I have deep experience with Oracle 19c and 23AI, PostgreSQL, MongoDB, Redis, and Elasticsearch. I spend a lot of time thinking about the right data architecture for the problem at hand, whether that involves schema migration strategy, caching patterns, or cross-region replication.
I also work regularly across the DevOps stack, including Kubernetes, Docker, Jenkins, and CI/CD pipelines. If a process can be automated and made more resilient, that is usually where my attention goes.
Beyond my engineering role, I conduct independent research in AI, software infrastructure, and applied data science, with published work presented at IEEE conferences covering topics like reinforcement learning for CI/CD optimization and AI readiness in enterprise systems. I also peer-review for international journals and have contributed to open source projects, including the AWS Strands Agents SDK.
I enjoy connecting with engineers and builders working on meaningful infrastructure challenges or practical AI applications in enterprise environments.
Looking back, what key experiences or decisions guided your path into cloud computing and software architecture and led to your current role?
Honestly, it was less of a straight line and more of a series of problems that kept pulling me deeper.
My early years at Tata Consultancy Services were foundational in ways I did not fully appreciate at the time. I was working on financial systems, processing hundreds of thousands of daily transactions, optimizing CLI tools, and building reporting pipelines. The work was unglamorous, but it taught me to think seriously about performance, about what happens when your code runs at volume, and about the cost of getting architecture wrong early. Those years gave me a foundation that I still draw on.
Moving to Motorola Solutions shifted my perspective significantly. I was building systems for law enforcement agencies, software where reliability was not a nice-to-have. Designing an API gateway handling tens of thousands of concurrent connections, implementing geospatial routing algorithms, and integrating real-time logging infrastructure pushed me toward thinking about distributed systems more seriously. When the stakes are high, you start caring about fault tolerance, concurrency, and observability in a different way.
Oracle was where cloud infrastructure became the central focus. Working on OCI, touching multi-region deployments, large-scale data exports, database migrations, and caching layers, it became clear to me that modern software engineering and cloud architecture are inseparable. The problems I find most interesting now live exactly at that boundary.
The research work came organically from wanting to stay connected to where things are heading, not just where they are today. Publishing at IEEE conferences and doing peer reviews keeps me engaged with ideas that have not made it into production systems yet, and that perspective genuinely influences how I approach architecture decisions at work.
Looking back, every role added a layer: performance, reliability, scale, cloud, and now AI systems. Each stage felt like a natural next problem to solve!
When you assess cloud workloads, what production signals tell you a service is safely over‑provisioned and ready for right‑sizing?
Right-sizing conversations tend to go sideways when teams treat utilization metrics in isolation. A CPU sitting at 15% average does not tell you much on its own. The signal that actually matters is the shape of the data over time and whether your headroom is there when you genuinely need it versus just sitting idle because someone added a buffer three years ago and nobody revisited it.
The first thing I look at is the gap between average utilization and peak utilization across a meaningful window, not a week, but across traffic patterns that capture your real load cycles. If your p99 CPU during peak is still well below 60% consistently, and memory pressure is low with no swap activity, that is a reasonable starting signal. But I pair that with latency percentiles. If your p95 and p99 response times are stable and not trending upward, the service is absorbing its load comfortably.
Garbage collection behavior is something I watch closely for JVM-based services specifically. Excessive GC pauses or frequent full GCs often tell a different story than raw memory utilization numbers suggest. A service can look fine on a dashboard and still be struggling under the surface.
I also look at thread pool saturation and connection pool metrics. A service might show modest CPU and memory numbers but be quietly queuing work because its concurrency limits are too tight for the instance size, or conversely have headroom that is never touched.
On the infrastructure side, network throughput and disk I/O relative to provisioned limits matter depending on the workload type. For data-heavy services especially, those constraints surface before compute does.
The honest answer is that no single signal authorizes a right-sizing decision. What gives me confidence is when utilization headroom, latency stability, error rates, and resource saturation metrics all tell the same story consistently over time. When they align, you have a real case. When even one of them is telling a different story, I want to understand why before touching anything in production.
Building on that, what practices have helped you implement autoscaling on Kubernetes or Azure while protecting SLAs and reliability?
Autoscaling sounds straightforward until you have actually broken an SLA because your scaling policy was too slow, too aggressive, or firing on the wrong signal entirely.
The first thing I got serious about was separating scaling signals by what they actually represent. CPU and memory are lagging indicators. By the time your HPA reacts to CPU pressure, the damage to latency is often already done. Moving toward custom metrics such as queue depth, active connections, and request rate gives your scaler something to act on earlier in the pressure curve.
Scaling up fast and scaling down slowly became a principle I apply almost universally. Aggressive scale-in is one of the more common ways teams introduce instability. I set conservative scale-down cooldown windows and, in some cases, use separate policies for up and down behavior explicitly.
Pod disruption budgets are non-negotiable for anything with an SLA attached to it. They are easy to overlook during initial setup and painful to discover as missing during a node drain or rolling deployment. I treat them as a baseline requirement rather than an optional hardening step.
Readiness probes also deserve more attention than they typically get. A pod that registers as ready before it is actually warm will absorb live traffic too early, and under autoscaling conditions, that gap shows up in your latency percentiles. Getting probe timing right, especially for services with slower initialization paths, is worth the effort.
Ultimately, the practices that have held up best are grounded in understanding your actual traffic patterns and failure modes rather than copying a configuration template. Autoscaling is only as reliable as your understanding of what the service needs to stay healthy.
Shifting to resilience, which design patterns in your Java microservices have most improved behavior under partial failures such as network partitions or duplicate messages?
Partial failures are where microservice architecture either proves itself or falls apart. The patterns that have genuinely moved the needle for me are the ones that treat failure as a normal operating condition rather than an exceptional case.
Circuit breakers made the biggest early difference. Without them, a degraded downstream dependency becomes your problem too, and fast. The pattern itself is straightforward, but the configuration decisions matter more than people realize. Setting the right failure threshold, half-open probe intervals, and fallback behavior requires understanding your actual traffic patterns. A circuit breaker tuned against synthetic load behaves differently in production.
Idempotency handling for duplicate messages was something I had to get disciplined about relatively early. In distributed systems, you cannot guarantee exactly-once delivery, so your consumers need to handle duplicates gracefully. Idempotency keys tied to a durable store, checking before processing rather than after, became standard practice for anything touching financial or healthcare data where processing something twice has real consequences.
Bulkheads are underused in my experience. Isolating thread pools by downstream dependency means one slow service cannot exhaust your entire executor and take unrelated functionality down with it. At my current Fortune 100 company, separating resource pools across service boundaries was part of how we got from 97.99% to 99.99% SLA. It is not a glamorous pattern, but it carries real weight in production.
Retry logic with exponential backoff and jitter sounds basic, but getting it wrong is surprisingly common. Retrying too aggressively under degraded conditions is how you turn a partial outage into a full one. Adding jitter prevents retry storms when multiple instances react to the same failure simultaneously.
The honest takeaway is that no single pattern handles partial failures completely. What works is layering them thoughtfully so each one covers a failure mode that the others do not.
On the API front, how do you approach REST API design and versioning so teams can ship quickly without breaking consumers?
Breaking a consumer in production is one of those experiences that clarifies your thinking on API design pretty quickly. Most of the practices I follow now trace back to either preventing that or recovering from it faster.
The foundation is treating your API contract as a first-class concern from the start, not something you formalize after the fact. That means being deliberate about what is public surface area versus internal implementation detail. The more you expose, the more you are committing to maintain. I have seen teams paint themselves into corners by surfacing too much too early and then struggling to evolve without breaking downstream consumers.
On versioning, URI versioning is straightforward and explicit, which I prefer for services with external consumers or integrations you do not fully control. The tradeoff is that it requires consumers to migrate actively when versions change. For internal services where you own both sides of the contract, header-based versioning gives you more flexibility without cluttering your URL structure.
The practice that has helped most in fast-moving teams is additive change by default. Adding fields, adding endpoints, and expanding enum values are generally safe. Removing fields, renaming properties, and changing response structure require a versioning strategy. When the discipline around that distinction is consistent, teams can ship quickly without constantly negotiating with consumers about what changed.
Contract testing deserves more adoption than it typically gets. Having consumers define their expectations explicitly and running those checks in your pipeline catches breaking changes before they reach production rather than after. It shifts the conversation from blame to prevention.
Documentation as a living artifact rather than a one-time deliverable also matters more than teams admit. An accurate OpenAPI spec that stays current with the implementation is genuinely useful. One that drifts becomes noise that erodes trust in the API itself.
The goal is to give consuming teams confidence that shipping on your side does not mean surprises on theirs.
Turning to data, what criteria guide your choice between MySQL, Redis, and Hadoop/Hive when modeling a new service’s storage layer?
The choice rarely comes down to a single criterion. What I actually do is work backward from the access patterns, consistency requirements, and operational characteristics of the service before touching any technology decision.
The first question I ask is what the data looks like structurally and how it will be read. Relational data with clear relationships, transactional boundaries, and queries that benefit from joins points toward a relational database. MySQL fits well when your data model is stable, your consistency requirements are strong, and your read and write patterns are predictable. Where I have seen teams go wrong is reaching for relational storage out of familiarity when the access pattern does not actually need it.
Redis enters the conversation when latency is the dominant constraint. Sub-millisecond reads, session state, leaderboards, rate-limiting counters, and distributed locks are problems Redis solves cleanly. At Oracle, I used Coherence with a cache-aside pattern to bring provisioning response times from fifteen seconds down to ninety milliseconds. The key discipline with caching layers is being explicit about the invalidation strategy and what staleness your service can tolerate. Cache correctness bugs are subtle and expensive to debug in production.
Hadoop and Hive belong to a different category of problems entirely. When you are dealing with analytical workloads, historical data at scale, and batch processing across terabytes, that is where that ecosystem earns its place. It is not a transactional store, and treating it like one creates problems. The access patterns are fundamentally different: high throughput sequential reads rather than low latency random access.
In practice, many services end up using more than one solution: a transactional store for your source of truth, a caching layer for hot read paths, and potentially an analytical store for reporting or audit history. The mistake I try to avoid is forcing one technology to serve access patterns it was not designed for just to keep the architecture simpler on paper.
Zooming into performance, which JVM tuning or Java coding techniques have delivered the biggest latency or throughput wins in your production systems?
Most of the meaningful performance wins I have seen in production came not from exotic tuning but from fixing architectural mistakes that should not have been there in the first place. That said, once the obvious problems are resolved, JVM tuning and coding discipline do move the needle in measurable ways.
Garbage collection is where I have spent the most time. Switching from CMS to G1GC and eventually ZGC for latency-sensitive services made a real difference. ZGC’s concurrent collection model keeps pause times consistently low in a way that matters when you have tight p99 SLA commitments. However, the tuning that helps most is reducing allocation pressure in the first place. Fewer short-lived objects mean less GC work, regardless of which collector you are using. That points back to code patterns, such as:
- Object pooling for frequently allocated structures,
- Reusing buffers,
- Being deliberate about what you create inside hot paths.
Non-blocking I/O was a significant unlock at Motorola when designing the API gateway handling tens of thousands of concurrent connections. Netty’s event loop model allowed us to serve that concurrency without a thread per connection, which fundamentally changes the resource profile of the service. For I/O-bound workloads, the threading model matters as much as anything else.
Coherence caching at my current Fortune 100 company reduced provisioning response time from fifteen seconds to ninety milliseconds. That number sounds dramatic, but the underlying principle is straightforward: eliminate redundant computation and repeated database round trips on hot paths. Profiling almost always reveals a small number of call sites responsible for a disproportionate share of latency.
On the coding side, avoiding synchronized blocks on hot paths, preferring lock-free structures from java.util.concurrent, and being careful about autoboxing in performance-critical code are habits that compound over time.
The honest answer is that profiling under realistic load reveals more than intuition does. I have been surprised enough times by where the actual bottleneck was that I stopped guessing and started measuring first.
To close us out, what one practice would you recommend every team adopt next sprint to improve reliability and cost efficiency in cloud‑native Java systems?
If I had to pick one practice that delivers on both reliability and cost efficiency simultaneously, it would be implementing structured observability with actionable alerting rather than just collecting metrics.
Most teams I have encountered have monitoring. Dashboards exist, metrics are flowing, and logs are somewhere. What is missing is the connective tissue between what the system is telling you and decisions you can actually act on. That gap is where both reliability incidents and cost waste quietly live.
Structured logging with consistent correlation IDs across service boundaries sounds unglamorous, but it fundamentally changes how fast you can diagnose a problem in production. When every log entry carries context about the request, the service, and the downstream calls it made, you stop spending forty minutes reconstructing what happened and start spending five. That time difference matters during an incident, and it matters even more in the hours before something becomes an incident.
The cost efficiency angle is less obvious but equally real. Teams that cannot observe their systems clearly tend to over-provision defensively. When you have confidence in what your services are actually doing under load, right-sizing decisions become evidence-based rather than anxiety-based. The observability investment pays for itself in infrastructure spend relatively quickly.
The specific recommendation I would give a team next sprint is to pick one service, instrument it properly with structured logs and meaningful custom metrics beyond default JVM stats, set up alerts that fire on symptoms your users actually experience rather than arbitrary thresholds, and use that as the template for everything else.
Start with one service done properly rather than ten services done superficially. The pattern spreads naturally once the team sees how much faster debugging becomes and how much clearer capacity decisions get. That combination of faster incident response and better provisioning confidence is about as close to a free lunch as cloud-native engineering offers.