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.

90% of the scale-ups I meet could 2× their throughput with 0 new hires. Here are the 10 tweaks you’ll wish you’d applied yesterday 👇 1. One roadmap → One owner → One channel of truth → One weekly pulse. (Every extra layer costs you compound confusion.) 2. Ship > Perfect. Tie every project to a 14-day release cycle and force a demo at the end. (Deadlines create gravity; gravity creates progress.) 3. Document while doing. Hit “record”, talk through the task, hand the video to Ops. Ten minutes now = 100 minutes saved forever. 4. Replace “Who can do this?” with “Where does this live?” Tasks belong inside systems, not in people’s heads. The moment the system owns it, you’re free. 5. Daily Stand-down. One Slack thread, end of day: – What moved? – What’s stuck? – What’s next? Takes 4′, replaces four meetings, surfaces blockers before they fester. 6. Work-in-Progress cap = team size – 1. Fewer plates spinning → more dishes served. (Throughput is a flow problem, not a hustle problem.) 7. Ruthless tiering of requests. A-Level: impacts revenue or runway in ≤30 days. B-Level: everything else. You touch A’s first, always. Watch lag evaporate. 8. Automate the hand-off, not the whole job. Zapier, Make, or a 20-line script that just moves the ticket to the next lane. Tiny bridge, massive traffic gain. 9. Success metric = Cycle Time. Start-to-Done in days, not hours billed or tasks logged. Shorten the loop, watch margins widen. 10. Ops Debt Review, monthly. List every manual, repeatable, soul-sucking step. Score pain 1-5. Kill the highest score first. Repeat. (If it’s not on the kill list, it sneaks back in.) And remember: The easiest way to “hire” 2 extra people is to give every existing one back 20% of their week. Start with any tweak above, measure Cycle Time next Friday, then come thank me.

How to Improve API Performance? If you’ve built APIs, you’ve probably faced issues like slow response times, high database load, or network inefficiencies. These problems can frustrate users and make your system unreliable. But the good news? There are proven techniques to make your APIs faster and more efficient. Let’s go through them: 1. Pagination ✅ - Instead of returning massive datasets in one go, break the response into pages. - Reduces response time and memory usage - Helps when dealing with large datasets - Keeps requests manageable for both server and client 2. Async Logging ✅ - Logging is important, but doing it synchronously can slow down your API. - Use asynchronous logging to avoid blocking the main process - Send logs to a buffer and flush periodically - Improves throughput and reduces latency 3. Caching ✅ - Why query the database for the same data repeatedly? - Store frequently accessed data in cache (e.g., Redis, Memcached) - If the data is available in cache → return instantly - If not → query the DB, update the cache, and return the result 4. Payload Compression ✅ - Large response sizes lead to slower APIs. - Compress data before sending it over the network (e.g., Gzip, Brotli) - Smaller payload = faster download & upload - Helps in bandwidth-constrained environments 5. Connection Pooling ✅ - Opening and closing database connections is costly. - Instead of creating a new connection for every request, reuse existing ones - Reduces latency and database load - Most ORMs & DB libraries support connection pooling If your API is slow, it’s likely because of one or more of these inefficiencies. Start by profiling performance and identifying bottlenecks Implement one optimization at a time, measure impact A fast API means happier users & better scalability. ✅

Structured cabling is no longer passive. It’s programmable infrastructure... ...engineered for determinism, not just connectivity. 𝗙𝗿𝗼𝗺 $𝟭𝟰𝗕 𝗶𝗻 𝟮𝟬𝟮𝟰 𝘁𝗼 $𝟮𝟭.𝟲𝟵𝗕 𝗯𝘆 𝟮𝟬𝟮𝟵, ...driven by 400 Gbps fabrics, Wi-Fi 7, and edge compute... ...and none of it tolerates signal degradation, crosstalk, or choke points. If you're building for high-bandwidth throughput, tight thermal margins, and zero-touch ops- anchor your spec to Corning Incorporated. 1. 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗹𝗮𝘆 𝗰𝗮𝗯𝗹𝗲.  Decommission legacy copper Cat5e/6 and OM1/OM2 fibre.  Standardise to Corning OM4/OM5 multimode for ≤150m links.  Use Corning single-mode trunks where low loss over distance is critical.  For <30m links running 25/40 Gbps...  ...use Everon® Cat. 8.1 jacks Class I ISO/IEC 11801, rated for 2 GHz operation.    2. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗺𝗼𝗱𝘂𝗹𝗮𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗿𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆.  Manual terminations = variability.  Corning EDGE™ and EDGE8™ platforms deliver:   • MPO trunks factory-tested to IEC 61300-3-34   • Polarity-aligned connector schemas   • Zero field polishing, minimal insertion loss  This yields deterministic link budgets from day zero.    3. 𝗢𝗽𝘁𝗶𝗺𝗶𝘀𝗲 𝗳𝗼𝗿 𝗥𝗨 𝗱𝗲𝗻𝘀𝗶𝘁𝘆.  Stage front-access patches separately from rear trunk ingress to prevent cable crossovers.  Design rear trays to isolate thermals from active equipment exhaust zones.  Target 48 ports/RU with low-loss MTP cassettes and angled patch panels.  Maintain 30 mm bend radius backed by Corning ClearCurve® fibre    4. 𝗠𝗮𝗸𝗲 𝘁𝗼𝗽𝗼𝗹𝗼𝗴𝘆 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝗮𝘂𝗱𝗶𝘁𝗮𝗯𝗹𝗲.  Use Corning ClearTrack™ for link-level RFID/barcode tagging.  Ingest cable metadata into DCIM or NetBox.  Tie port IDs to MACs, serials, and circuit IDs.  Build a live, queryable physical twin...  ...enabling cable trace in seconds, not site visits.    5. 𝗖𝗲𝗿𝘁𝗶𝗳𝘆 𝗳𝗼𝗿 𝗦𝘂𝗿𝗲.  Use Fluke DSX-8000 for full-link testing...  ...length, attenuation, reflectance, and return loss.  Enforce IEC 61300-3-35 inspection before mating.  Validate end-to-end compliance with IEEE 802.3bs (100/200/400G).  Train field techs on airflow-aware routing, minimum bend radius, and physical strain relief best practices.    This is structured cabling reimagined as a data-driven subsystem. Because at 400 Gbps, you don’t get retries... ...you get signal or you don’t. Corning brings an integrated ecosystem... ...fibre, connectors, pre-termination, tagging, certification... ...to make physical layer performance predictable. Tell me - Which myth is still holding back your cabling refresh? 𝗣.𝗦. 𝗦𝗮𝘃𝗲 + 𝘀𝗵𝗮𝗿𝗲 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗶𝗻𝗳𝗿𝗮 𝘁𝗲𝗮𝗺.

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

Manufacturing Efficiency is More Than Numbers…It’s Transformational Science that Delivers Value. In my experience of deploying continuous process improvement, I’ve seen one truth repeat itself: small changes in cycle time create massive changes in organizational success. Consider a real-world example from a Fortune 500 distribution center. The facility struggled with a 12-hour lead time from order receipt to shipping. When we applied Manufacturing Cycle Time (MCT) and Manufacturing Cycle Efficiency (MCE) analysis, the data revealed that only 35 percent of production time was true value-added work. The rest was waiting, unnecessary movement, or inefficient scheduling. Through Lean tools like value stream mapping, Kaizen events, and standard work design, we cut average lead time from 12 hours to 8 hours. That 4-hour reduction meant faster customer fulfillment, increased throughput capacity, and a remarkable financial impact, more than 3.2 million dollars in annualized savings through reduced overtime, lower inventory holding costs, and fewer expedited shipments. The return on investment went far beyond financials. Employees who once felt pressured by bottlenecks were now empowered to work in a smoother, more predictable system. Morale increased as they could focus on craftsmanship and problem-solving rather than firefighting. When people feel their contributions directly improve performance, you build a culture of ownership and innovation. I have led these transformations across industries, from aerospace to government services and the outcomes are consistent. The combination of measuring cycle efficiency and acting on it with Lean methods delivers scalable success. Organizations gain profitability, employees gain pride, and customers gain trust. Continuous improvement is not just about efficiency metrics. It is about unlocking hidden capacity, protecting margins, and most importantly, enabling people to thrive in environments designed for excellence. That is the real power of Lean.🔋