Key Techniques for Achieving High Throughput

A sluggish API isn't just a technical hiccup – it's the difference between retaining and losing users to competitors. Let me share some battle-tested strategies that have helped many  achieve 10x performance improvements: 1. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Not just any caching – but strategic implementation. Think Redis or Memcached for frequently accessed data. The key is identifying what to cache and for how long. We've seen response times drop from seconds to milliseconds by implementing smart cache invalidation patterns and cache-aside strategies. 2. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Large datasets need careful handling. Whether you're using cursor-based or offset pagination, the secret lies in optimizing page sizes and implementing infinite scroll efficiently. Pro tip: Always include total count and metadata in your pagination response for better frontend handling. 3. 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is often overlooked, but crucial. Using efficient serializers (like MessagePack or Protocol Buffers as alternatives), removing unnecessary fields, and implementing partial response patterns can significantly reduce payload size. I've seen API response sizes shrink by 60% through careful serialization optimization. 4. 𝗧𝗵𝗲 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗞𝗶𝗹𝗹𝗲𝗿 This is the silent performance killer in many APIs. Using eager loading, implementing GraphQL for flexible data fetching, or utilizing batch loading techniques (like DataLoader pattern) can transform your API's database interaction patterns. 5. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 GZIP or Brotli compression isn't just about smaller payloads – it's about finding the right balance between CPU usage and transfer size. Modern compression algorithms can reduce payload size by up to 70% with minimal CPU overhead. 6. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹 A well-configured connection pool is your API's best friend. Whether it's database connections or HTTP clients, maintaining an optimal pool size based on your infrastructure capabilities can prevent connection bottlenecks and reduce latency spikes. 7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗟𝗼𝗮𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Beyond simple round-robin – implement adaptive load balancing that considers server health, current load, and geographical proximity. Tools like Kubernetes horizontal pod autoscaling can help automatically adjust resources based on real-time demand. In my experience, implementing these techniques reduces average response times from 800ms to under 100ms and helps handle 10x more traffic with the same infrastructure. Which of these techniques made the most significant impact on your API optimization journey?

𝗦𝗶𝘇𝗶𝗻𝗴 𝗮 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗰𝗹𝘂𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝗮 𝗵𝗶𝗴𝗵-𝘃𝗲𝗹𝗼𝗰𝗶𝘁𝘆 𝘀𝘁𝗿𝗲𝗮𝗺 (𝟮𝗚𝗕/𝗺𝗶𝗻): Sizing a Databricks cluster requires balancing throughput (processing the data fast enough to avoid lag) with latency (how quickly each record is processed). At 2GB per minute, you are looking at roughly 120GB per hour or ~2.8TB per day. This is a substantial workload that usually necessitates an optimized Spark configuration. 𝟭. 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗶𝗻𝗴 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁: To handle 2GB/min, your cluster must be capable of processing more than 2GB/min to handle "catch-up" scenarios (e.g., after a restart or a spike in data). A good rule of thumb is to aim for 1.5x to 2x your average ingestion rate. Target Processing Rate: 3GB - 4GB per minute. Core Scaling: Generally, one modern worker core (like those on an i3.xlarge or Standard_DS3_v2) can handle 10MB–50MB of data per second depending on the complexity of transformations. 2GB/min = ~33MB/sec. If your transformations are simple (JSON to Parquet), you might only need 4–8 cores. If you have heavy windowing, joins, or UDFs, you may need 16–32 cores. 𝟮. 𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗜𝗻𝘀𝘁𝗮𝗻𝗰𝗲 𝗧𝘆𝗽𝗲𝘀: For streaming, Compute Optimized or Memory Optimized instances are preferred over General Purpose ones. Worker Type: Use Delta Live Tables (DLT) if possible, as it handles autoscaling more intelligently for streaming. Otherwise, use m5d or Standard_D series. Local SSDs: Choose instances with "d" (e.g., Standard_DS3_v2). Streaming often involves "checkpointing" and "shuffling." Having local SSDs significantly speeds up these disk-heavy operations. 𝟯. 𝗞𝗲𝘆 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀: A. Use Enhanced Autoscaling Standard Spark autoscaling is often too slow for streaming. If using Delta Live Tables, enable Enhanced Autoscaling, which is specifically designed to add resources based on the "backlog" of the stream. B. Optimize the Trigger Interval Using Trigger(processingTime='1 minute') or Trigger(availableNow=true) can help. However, for a constant 2GB/min flow, Continuous Processing Mode or a very short processingTime (e.g., 10-30 seconds) is usually better to keep the micro-batches small and manageable. C. Partitioning and Shuffle With 2GB/min, your default spark.sql.shuffle.partitions (usually 200) might be too high or too low. Rule of thumb: Aim for 128MB–200MB per partition. If a micro-batch is 2GB, 10–20 partitions might be enough for the shuffle, but more cores will allow for more parallelism. 4. 𝗘𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗼𝗻 𝗖𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁: Factor Recommendation Worker Count Start with 4-8 workers (4 cores each) and monitor CPU. Instance Type m5d.2xlarge or Standard_DS4_v2 (high I/O). Max Offsets per Trigger Set this to limit how much data one batch pulls (e.g., 100,000 rows) to prevent OOM errors. RocksDB State Store If doing "stateful" streaming (aggregations), enable RocksDB to manage memory better. #databricks #sizing

From processing 10 records per minute to 200 records per second: Anatomy of an ETL Rescue. Sometimes, the most sophisticated problems require the simplest tools to solve: a marker and a whiteboard. We recently took a legacy ETL pipeline from a state of constant timeouts to high-throughput stability. The diagram sketches out that journey, but the real lesson was about respecting the physics of I/O. Functional Overview  To understand the optimization, you first need to understand the workload. The system operates as an asynchronous, state-aware ETL engine designed to handle high-frequency updates to complex datasets. 1/ Hierarchical Decomposition: Large, nested "monoliths" are deconstructed into atomic units to enable parallel processing and prevent blocking. 2/ Asynchronous Distribution: Deconstructed segments are buffered via a message broker, allowing the transformation layer to scale horizontally independent of ingestion rates. 3/ State-Aware Transformation: The engine performs complex reconciliation, including historical merging, dimensional expansion (exploding dense data), and schema validation. 4/ Optimized Persistence: Transformed states are committed to a document database using bulk-write patterns to maximize throughput and minimize network latency. The "Death by 1,000 Cuts" Phase (Left Side) Despite a solid functional design, our initial architecture choked in production. Why? 1/ Sequential Processing: The "one-at-a-time" approach ignored the batching power of our broker, causing excessive network round-trips. 2/ Blocking Disk I/O: Synchronous, granular logging meant the CPU spent more time waiting for the disk than computing transformations. 3/ High-Contention Persistence: Overlapping updates on the same resource keys led to massive document locking and transaction failures. The Optimization Strategy (Right Side) We didn't rewrite the business logic; we changed the flow. Step 1: "True" Micro-Batching: We moved to Windowed Aggregation. Accumulating messages reduced persistence round-trips by orders of magnitude. Step 2: Intelligent Deduplication: We implemented State Consolidation in memory. Why write to the DB five times in a millisecond? We merge redundant updates before they hit the persistence layer. Step 3: Observability Decoupling: We shifted logging from the record level to the batch level. We restored visibility without the performance penalty of per-record I/O. Step 4: Concurrency Tuning: We adjusted load generation for Key Collision Avoidance (ensuring high cardinality) and tuned the broker for maximum link pool saturation. Latency is rarely about code speed; it’s almost always about I/O wait time. If you want to go fast, stop talking to the disk so much.

The GPUs were top-tier. The models were solid. Training was still slow. The real problem? The data pipeline feeding them. GPU performance is rarely limited by compute alone. It’s limited by how efficiently data moves, loads, and synchronizes. Here’s the structured 10-step path 👇 Step 1: Define Target GPU Throughput Start by calculating samples per second per GPU and defining a minimum sustained throughput target. Design for steady performance, not peak spikes. Step 2: Co-Locate Compute and Data Keep data physically close to GPUs to reduce cross-rack traffic, latency variability, and east-west congestion that silently kills scaling. Step 3: Implement Multi-Level Caching Use layered caching - object storage, distributed cache, node-local SSD, and memory buffers - to keep GPUs continuously fed. Cold storage should never directly serve GPUs. Step 4: Parallelize Data Loading Increase data loader workers, enable asynchronous prefetching, and overlap I/O with compute. If GPUs wait for data, your scaling breaks. Step 5: Design for Distributed Synchronization Align shard distribution across training nodes, avoid duplicate reads, and balance partitions evenly to prevent gradient sync delays and network spikes. Step 6: Select the Right Storage Architecture Evaluate object storage for durability, distributed file systems for throughput, and NVMe for hot data. Hybrid storage layers outperform single-tier designs. Step 7: Optimize Data Format and Serialization Adopt columnar formats like Parquet, compress intelligently, and reduce decoding overhead. Inefficient serialization wastes more compute than expected. Step 8: Minimize CPU Bottlenecks Monitor CPU saturation, optimize preprocessing, and remove heavy Python loops. GPUs depend on CPUs to prepare data efficiently. Step 9: Map the Data Access Pattern Analyze sequential vs random reads, shuffle frequency, augmentation intensity, and batch size. Most inefficiencies come from misunderstood access patterns. Step 10: Monitor and Continuously Benchmark Track GPU utilization, data loader wait time, and end-to-end samples per second. You cannot optimize what you don’t measure. The core principle: Throughput > Theoretical FLOPS. AI performance is a pipeline problem, not just a hardware problem. If your GPUs aren’t hitting expected utilization, the bottleneck is probably upstream.

If you’re clustering or partitioning your data on timestamp-based keys—especially in systems like BigQuery or Snowflake, etc. this diagram should look familiar 👇 Hotspots in partitioned databases are one of those things you don’t notice until your write performance nosedives. When I work with teams building time-series datasets or event logs, one of the most common pitfalls I see is sequential writes to a single partition. Timestamp as a partition key sounds intuitive (and easy), but here’s what actually happens: 🔹 Writes start hitting a narrow window of partitions (like t1–t2 in this example) 🔹 That partition becomes a hotspot, overloaded with inserts 🔹 Meanwhile, surrounding partitions (t0–t1, t2–t3) sit nearly idle 🔹 Performance drops, latency increases, and in some systems—throughput throttling or even write failures kick in This is why choosing the right clustering/partitioning strategy is so critical. A few things that’ve worked well for us: ✅ Add high-cardinality attributes (like user_id, region, device) to the partitioning scheme ✅ Randomize write distribution if real-time access isn’t required (e.g., hash bucketing) ✅ Use ingestion time or write time sparingly, only when access patterns make sense ✅ Monitor partition skew early and often—tools like system views and query plans help! Partitioning should balance read performance and write throughput. Optimizing for just one leads to trouble. If you're building on time-series data, don’t sleep on this. The write patterns you define today can make or break your infra six months from now. #dataengineering

A new dimension for multiplexing mass spec analysis -- time -- is enabling scaling up throughput at new levels. ➡️ We demonstrate 27-plex DIA enabling over 500 samples / day and project that timePlex will enable throughputs exceeding 1,000 samples / day. This project was led by Jason Derks at Parallel Squared Technology Institute Liquid chromatography-mass spectrometry (LC-MS) can enable precise and accurate quantification of analytes at high-sensitivity, but the rate at which samples can be analyzed remains limiting. Throughput can be increased by multiplexing samples in the mass domain with plexDIA, yet multiplexing along one dimension will only linearly scale throughput with plex. To enable combinatorial-scaling of proteomics throughput we developed a complementary multiplexing strategy in the time domain, termed `timePlex'. timePlex staggers and overlaps the separation periods of individual samples. This strategy is orthogonal to isotopic multiplexing, which enables combinatorial multiplexing in mass and time domains when paired together and thus multiplicatively increased throughput. We demonstrate this with 3-timePlex and 3-plexDIA, enabling the multiplexing of 9 samples per LC-MS run, and 3-timePlex and 9-plexDIA exceeding 500 samples / day with a combinatorial 27-plex. Crucially, timePlex supports sensitive analyses, including of single cells. These results establish timePlex as a methodology for label-free multiplexing and for combinatorially scaling the throughput of LC-MS proteomics. We project this combined approach will eventually enable an increase in throughput exceeding 1,000 samples / day. https://lnkd.in/eYi7jwZ4

🚀 Handling High Traffic in Web Applications Designing systems that handle high traffic requires a combination of scalability, performance optimization, and resilient architecture. Below is a practical explanation of the key strategies used in real-world applications. Load balancing ensures that incoming user requests are evenly distributed across multiple servers. This prevents any single server from becoming a bottleneck and improves overall system availability. In production environments, tools like Azure Load Balancer or Application Gateway are commonly used to achieve this. Microservices architecture allows applications to be broken down into smaller, independent services. Each service can be deployed and scaled individually based on demand. For example, if a payment service experiences high traffic, it can scale independently without affecting other parts of the system. Caching plays a critical role in reducing latency and database load. Frequently accessed data is stored in fast in-memory systems like Redis, allowing applications to return responses quickly without repeatedly querying the database. Event driven architecture enables systems to handle large volumes of requests asynchronously. Technologies like Apache Kafka or Azure Service Bus are used to process tasks in the background, ensuring that the main application remains responsive even during peak loads. Database optimization focuses on improving query performance and efficient data access. Techniques such as indexing, query tuning, and optimized ORM usage help maintain low latency even when handling millions of records. Content Delivery Networks improve performance by serving static content such as images, scripts, and stylesheets from servers located closer to the user. This reduces latency and enhances the user experience globally. Monitoring and auto scaling ensure that the system adapts dynamically to traffic changes. Tools like Azure Monitor and CloudWatch track system performance and automatically scale resources up or down to maintain stability and cost efficiency. 💡 Final Thought Handling high traffic is about building systems that distribute load efficiently, scale intelligently, and maintain performance under pressure. #DotNet #Microservices #Azure #Kafka #SystemDesign #Scalability #SoftwareEngineering #CloudComputing

Valkey can do more than one-million requests per process, but how did our engineers figure out how to optimize the engine to get here? The short answer is we do performance profiling, we find the bottlenecks, we work to optimize, and we repeat. A recent example is we achieved a 4% throughput improvement by using Intel vectorization to improve the speed of key-value lookups. Intel Corporation wrote up how they found this bottleneck, as well as two other real world examples, here https://lnkd.in/ghS2tD_X. If you're curious about how low-level optimizations can produce real differences, it's worth a read.