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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteHigh traffic does not hit a database as a neat count of customers. Each app request can trigger many computations and calls across services; peak-hour surges can multiply that work, and a cache failure can push it back onto persistent storage. Swiggy’s engineering posts explain these pressures and describe resilience techniques for containing them, but they do not disclose a complete current database architecture or how Swiggy partitions its data.
Why a home-screen request can create so much work
A food-delivery home screen involves more than finding nearby restaurants. In a March 15, 2021 Swiggy Bytes article, engineering author Vijay Seshadri described calculations for serviceability, delivery estimates, personalized promotions, restaurant ranking, and relevant collections. Serviceability itself can depend on road access, traffic, travel time, and delivery capacity.
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Much of this work is specific to a customer and restaurant combination. Seshadri illustrated the possible scale with a dense Bangalore scenario: more than 1,000 serviceable restaurants and 1,000 people opening the app at once could imply more than 1 million computations per second. That is an illustrative scenario from the post, not a measured current throughput figure. Swiggy Bytes, March 15, 2021
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Why peak traffic matters more than a daily average
Demand is uneven, so infrastructure sized for an average can fall behind at lunch or dinner. Seshadri’s 2021 post reported that one storefront service handled roughly 100,000 requests per minute at lower load, about 300,000 at lunch, and around 500,000 at dinner. The article also described four to six peak hours and six to eight hours with practically no traffic each day, with weekends somewhat busier.
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These are company-reported examples for the period and service discussed, not current measurements for all of Swiggy. The engineering trade-off is broader: scaling compute and storage with demand can cost less than keeping enough capacity provisioned for the peak throughout the day, but scaling must happen quickly enough to meet the surge.
How microservice fan-out amplifies database pressure
A request may travel through a graph of services rather than one application and one database. Swiggy’s 2021 overview said hundreds of services could participate in order fulfillment and described an average fan-out factor of 3–5x in some flows. Parallel calls can shorten the time a customer waits, but they also create more downstream work.
Consider two upstream services that both call the same downstream service. That shared dependency receives the combined traffic. In a larger call graph, several paths can converge on one service or persistence layer. A slowdown there can then affect multiple upstream requests; without controls, the disturbance can spread into a cascading failure.
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Why caching helps—and why cache failures are dangerous
Caching can make repeated reads faster and reduce the number of calls that reach persistent storage. But cached data must remain coherent with the underlying data, and the system needs to be able to handle misses. Swiggy’s overview says it uses cache-coherence strategies, but does not identify a particular invalidation protocol.
The risk is a sudden fallback: if a cache fails or many requests miss at once, the persistence layer can receive a burst of work it was not serving moments earlier. Seshadri warned that the resulting load can exceed a service’s latency budget and trigger cascading failure. A cache therefore reduces pressure only when its update behavior and failure mode are part of the design, rather than treated as someone else’s problem.
How latency budgets and resilience controls limit failures
In the 2021 overview, Swiggy described latency budgets near 50 milliseconds at P99 for several services, as part of rendering a page in a few hundred milliseconds. P99 refers to the 99th percentile: the target is concerned with slower requests near the tail, not just the average. When a downstream service consumes too much of the overall time budget, a request can miss its end-to-end deadline even if other services respond quickly.
In a separate Swiggy Bytes article published November 24, 2021, Seshadri described resilience techniques for microservices. He framed the challenge as fulfilling orders to customer expectations even when individual services fail, and discussed the following controls:
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- Circuit breakers: Detect repeated dependency failures or slow responses and stop sending traffic in a way that would compound the problem.
- Controlled retries: Retry selectively rather than automatically multiplying load. Seshadri cautioned that misconfigured retries can turn a brownout—where some requests succeed or miss latency targets—into a blackout where requests fail outright.
- Graceful degradation: Return a useful result using a fallback when an exact dependency is temporarily unavailable. The article gives estimating a delivery agent’s location when exact location data is unavailable as an example, subject to a timing threshold.
- Adaptive timeouts: Allocate time across dependencies in light of observed behavior while preserving the overall request deadline, rather than assuming every dependency always deserves the same fixed slice.
Those practices are described in Swiggy’s engineering guidance; they should not be read as a complete account of its current implementation. At the time of the November 2021 article, Swiggy reported more than 400 microservices in production and more than 130 directly in the order-fulfillment path. Those dated counts describe the system then, not its present service inventory. Swiggy Bytes, “Designing Resilient Microservices — Part 1,” November 24, 2021
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where sharding fits—and what is not public about Swiggy
When one database cannot serve a workload within its latency, capacity, or availability requirements, distributing data across machines is one general option. This is called sharding. It can spread storage and request load, but the application must route each operation to the right shard, and the partitioning scheme affects how data is moved as the system grows.
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Meta Engineering’s August 24, 2020 article explains why scaling stateful databases is more complicated than adding stateless web servers. It contrasts modulo hashing, which can require broad reshuffling when servers are added, with consistent hashing, which can limit how much data moves but does not automatically satisfy every constraint, such as geographic placement. It also discusses explicit shard allocation, replicas, failover, load balancing, and changing replication factors. These are general concepts illustrated by Meta, not evidence that Swiggy uses Meta’s tools or any particular sharding approach. Meta Engineering, “Scaling Instagram Infrastructure: Part 2,” August 24, 2020
Read replicas are another general design option: they can distribute eligible reads, but introduce replication lag and require careful routing. Whether caching, replicas, or sharding is appropriate depends on the read/write mix, consistency needs, latency target, failure behavior, operational complexity, and cost. Swiggy’s cited posts do not establish its database vendor, replica setup, or shard design.
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Database overload is often the endpoint of a chain: expensive request work creates downstream calls; uneven traffic concentrates them into peaks; a slow dependency or failed cache redirects more work toward persistence; and retries can amplify the surge. The countermeasures work together: scale for demand, understand cache behavior, set end-to-end latency budgets, and contain failures instead of allowing every layer to keep retrying.
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