This article documents a month‑long investigation and tuning of a Java backend server, detailing how FullGC frequency was cut from dozens per day to a handful by adjusting heap parameters, fixing a memory‑leak caused by anonymous inner‑class listeners, and optimizing metaspace settings.
Facing soaring data volumes and rising Elasticsearch costs, Pupu's platform team migrated its APM storage to ClickHouse, achieving up to three million spans per second writes, 38% storage savings, and faster query performance through materialized views and careful deployment tuning.
This article documents a real‑world Nginx optimization case where adjusting gzip compression levels and switching to static gzip reduced CPU usage dramatically, enabling a 9‑wan QPS load to be handled with only 7% CPU and achieving over a 50‑fold performance gain.
This guide provides practical Linux‑based MySQL administration scripts covering connection, backup and restore, table optimization, log cleanup, performance monitoring, automated cron jobs, security best practices, replication management, and user permission handling.
This article introduces several MySQL performance‑tuning utilities—including mysqltuner.pl, tuning-primer.sh, and Percona Toolkit’s pt-variable-advisor and pt-query-digest—explaining how to download, run, and interpret their reports to optimize database configuration, memory usage, and query efficiency.
This article walks through a real‑world case of a slow MySQL query that took 8 seconds, explains how the original use of EXISTS and NOT EXISTS caused performance bottlenecks, and details two successive rewrites—first using JOIN + GROUP BY, then replacing NOT EXISTS with NOT IN—bringing execution time down to 0.7 seconds while highlighting key indexing and testing lessons.
This guide explores the JDK command‑line utilities located in %JAVA_HOME%\bin, explaining how tools such as jps, jstat, jinfo, jmap, jhat and jstack can be used to monitor JVM processes, analyze performance bottlenecks, inspect memory usage, generate heap dumps, and adjust runtime parameters for more efficient and stable Java applications.
This article explains LSM‑Tree based HBase compaction concepts, compares Minor and Major compactions, and shares practical tuning steps—including disabling automatic major compactions, controlling merge size, leveraging off‑peak windows, and improving merge efficiency—to reduce I/O, CPU usage, and latency in production environments.
This article outlines eight comprehensive database optimization approaches—reducing data volume, using space for performance, selecting appropriate storage systems, and related techniques such as data archiving, intermediate tables, serialization, sharding, distributed caching, read‑write splitting, and CQRS—to help backend engineers systematically address common performance bottlenecks.
This article explains why RocketMQ messages can pile up, analyzes the consumer pull‑process bottlenecks of consumption latency and concurrency, and provides practical strategies—including logging, JVM stack inspection, and concurrency tuning—to identify and eliminate the root causes of message backlog.
When a system’s data grows, MySQL queries can become slow and cause latency; this article presents practical optimization strategies—including query tuning, pagination, join improvements, subquery replacement, order‑by and group‑by enhancements, and count() efficiency—illustrated with sample tables, code snippets, and performance screenshots.
This article presents a step‑by‑step investigation of a SELECT statement that became 1000× slower in OceanBase 3.2.3 BP8, explains why the optimizer chose an inefficient I5 index, describes the plan‑expiration logic, and provides reproducible scripts and practical recommendations for fixing the problem.
This article explains how to determine whether Redis is truly experiencing latency spikes, outlines a step‑by‑step benchmarking process, identifies common causes such as high‑complexity commands, big keys, memory limits, fork overhead, AOF settings, CPU binding, swap usage, and provides concrete configuration and code examples to resolve each issue.
This guide explains why thread pools are essential in Java, walks through factors such as CPU cores, I/O latency, and workload characteristics, and provides formulas and concrete code examples for calculating the ideal pool size for both CPU‑bound and I/O‑bound tasks.
This guide provides comprehensive techniques for optimizing PyTorch training performance and efficiency, covering all model types such as CNNs, RNNs, GANs, and transformers, and applicable across domains like computer vision and natural language processing, targeting AI/ML platform engineers, data engineers, backend developers, MLOps, SREs, architects, and machine learning engineers.
This article analyzes a slow MySQL query that scans millions of rows to find students scoring 100 in Chinese, then demonstrates how adding appropriate single‑column and composite indexes, rewriting the query as a join, and using covering indexes can reduce execution time from hours to milliseconds.
The article walks through a real‑world case of a slow MySQL query that took 8 seconds, explains why simple indexing wasn't enough, and demonstrates two successive rewrites using JOIN‑GROUP BY and NOT IN that reduced execution time to 0.7 seconds while detailing the EXPLAIN analysis.
In this article the author walks through a real‑world slow‑query case, using EXPLAIN, rewriting EXISTS clauses with JOIN + GROUP BY and swapping NOT EXISTS for NOT IN, ultimately reducing execution time from 8 seconds to 0.7 seconds while sharing key indexing insights.
This guide explains how Ant Group uses Alluxio to overcome storage I/O, capacity, and latency challenges, delivering stability, performance, and scalability improvements for large‑scale PyTorch model training while reducing infrastructure costs and providing practical optimization techniques and code examples.
This article explains the G1 garbage collector’s heap layout, collection cycles, pause prediction, log analysis, and monitoring tools, helping Java developers diagnose and resolve performance issues such as frequent restarts, OOM, CPU spikes, and periodic latency spikes.
The article examines how Apollo’s long‑polling and push‑pull design cause massive database bandwidth spikes during large namespace updates in promotion periods, analyzes the RemoteConfigLongPollService and repository code, and proposes three optimizations—key‑level synchronization, MD5‑based change detection, and circuit‑breaker handling—while weighing real‑time versus periodic update trade‑offs.
This article investigates why Sharding-JDBC takes over half of the application startup time to load metadata, explains the role of the max.connections.size.per.query setting, demonstrates how adjusting it speeds up startup, and discusses the trade‑offs and potential dead‑lock risks of increasing the value.
A Java ToB system struggled to meet a 500 requests‑per‑second target, revealing hidden bottlenecks such as slow SQL, excessive logging, thread‑pool misconfiguration, high CPU usage from Spring bean creation, and improper Redis usage, which were systematically identified and mitigated.
At the 18th China Linux Kernel Developer Conference, OPPO unveiled memory and scheduler optimizations—including a dynamic 64 KB huge‑page scheme, LRU reclamation improvements, and a latency‑aware scheduler balance—that collectively raise memory access performance by over 10 %, reduce lock contention, and lower frame‑drop and audio‑stutter rates, demonstrating the impact of programmable kernel technologies on Android smoothness.
This article presents a detailed reference of MySQL server configuration options (my.cnf), explaining each setting such as storage engine, networking, replication, logging, cache sizes, and performance‑related parameters to help administrators fine‑tune their databases.
This guide explains how to optimize Linux TCP performance by classifying and tuning kernel parameters related to connection establishment, packet reception, and packet transmission, covering queues, buffers, NIC bonding, multi‑queue IRQ affinity, RingBuffer settings, and offloading features.
A load test revealed Redis latency soaring to 1.2 seconds, and a systematic investigation traced the root cause to mis‑configured JedisPool parameters, leading to excessive connection wait times and churn, which were fixed by aligning pool settings and enabling proper eviction, dramatically improving latency.
This article deeply dissects Kafka Broker's network architecture and request‑processing pipeline, covering sequential, multithreaded, and event‑driven designs, the Reactor pattern, Acceptor and Processor threads, core request flow, and practical tuning parameters for high‑throughput, low‑latency deployments.
When a Redis‑backed service suddenly experiences high response times, this guide walks through systematic troubleshooting steps—checking server resources, network latency, Redis metrics, slow‑log, memory fragmentation, big keys, and hot keys—culminating in practical solutions such as pipelining, read/write splitting, and cache layering.
This article analyzes a large MySQL table that caused SLA alerts due to master‑slave lag, examines slow‑query logs with pt‑query‑digest, identifies index and delete inefficiencies, proposes index redesign and batch‑delete strategies, and validates the improvements with testing and DDL tools.
This article provides a comprehensive overview of Apache Kafka, covering its architecture, core concepts such as topics, partitions, and replicas, common operational commands, and practical performance‑tuning tips for high‑throughput, low‑latency streaming workloads.
This guide presents nine practical Linux server optimization techniques—including time synchronization, SYN‑cookie protection, Squid and Nginx tuning, file‑descriptor limits, service minimization, IPv6 disabling, NIC activation, and memory‑management insights—to boost precision, stability, performance, and security.
The article details how a sudden surge in goroutine and heap usage was traced to repeatedly creating gRPC clients instead of reusing a singleton, leading to blocked goroutines and TCP connections, and explains using pprof, stack traces, and tools like goleak to detect and prevent such leaks.
This comprehensive guide explains how to ensure Kafka stability by applying proactive prevention, continuous runtime monitoring, and effective fault‑resolution strategies, covering producer and consumer tuning, cluster configuration, performance optimization, alerting, and idempotent consumption to prevent message loss and service disruption.
This article examines Spring Boot 2.7.10’s embedded Tomcat configuration, detailing default connection queue sizes, thread pool parameters, key Tomcat settings such as AcceptCount and MaxConnections, internal thread components, and practical testing results that reveal how connection limits affect client‑server handshakes.
During a March 2023 load test the team discovered Redis request latency soaring to 1.2 seconds, traced the issue to mis‑configured JedisPool parameters and excessive connection churn, and resolved it by adjusting pool settings, dramatically improving P50, P90 and P99 response times.
This article documents a month‑long JVM garbage‑collection tuning process that reduced daily FullGC occurrences from over 40 to less than one per ten days by adjusting heap parameters, fixing memory leaks, and tuning Metaspace, while providing detailed analysis, code snippets, and performance graphs.
This article explains the background of cloud‑native microservices, introduces the open‑source SkyWalking observability platform and its core components (Agent, OAP server, storage, UI), and demonstrates how to extend SkyWalking with custom plugins and tune its performance to minimize monitoring overhead.
The article describes how vivo’s core music service reduced frequent garbage collections, long GC pauses, and RPC timeouts by switching to ParNew+CMS collectors, implementing fault‑transfer monitoring, optimizing large response objects, and adding non‑invasive payload checks, resulting in dramatically lower YGC/FGC frequencies and improved memory stability.
This article presents practical MySQL optimization techniques—including proper index creation, avoiding index invalidation, choosing appropriate lock granularity, efficient pagination strategies, steering clear of SELECT *, and using EXPLAIN and SHOW PROFILE—to help developers write faster, more reliable SQL queries.
This article documents a month‑long JVM garbage‑collection tuning effort that lowered FullGC from around 40 daily occurrences to one every ten days, halved YoungGC time, identified and fixed a memory‑leak caused by anonymous inner‑class listeners, and refined heap and metaspace settings to improve overall server throughput.
This article explains the purpose of CPU caches, their hierarchical structure and internal designs—including direct‑mapped, set‑associative, and fully‑associative caches—and demonstrates how understanding cache behavior can dramatically improve program performance, illustrated with C++ traversal benchmarks.
This article explains HBase’s LSM‑Tree compaction fundamentals—including minor and major compaction triggers, file‑selection policies, dynamic throughput throttling, and practical tuning examples that show how adjusting size limits, thread pools, and off‑peak settings can dramatically improve read latency and cluster stability.
This article presents a comprehensive guide to SQL performance tuning, covering real‑world interview scenarios, deep‑pagination pitfalls, and fifteen concrete optimization techniques—including index usage, limit tricks, proper joins, batch operations, and data‑type choices—to help developers write faster, more efficient MySQL queries.
Didi upgraded its self‑developed Elasticsearch from JDK 11/G1 to JDK 17, adopting ZGC for latency‑critical clusters and tuned G1 for throughput, which eliminated long GC pauses, reduced query latency by up to 96%, cut CPU usage, and dramatically improved stability across multiple production clusters.
This guide explains how to improve Spring Cloud's low default performance by configuring and tuning components such as the servlet container (switching to Undertow), Feign, Hystrix, Ribbon, and Zuul, providing detailed code snippets and parameter recommendations for high‑concurrency scenarios.
This article walks through JVM GC log analysis and tuning, covering monitoring steps, parameter settings, log interpretation, and using the free GCEasy visualizer to identify performance issues and optimize Java applications.
This article explains MySQL's doublewrite buffer mechanism, how it ensures data consistency by writing to a redo log before disk, provides configuration steps to enable it, and outlines performance optimization techniques such as adjusting redo log size, separating log files, and tuning flush settings.
This article explains the core trade‑offs of JVM tuning—short pause times versus high throughput—provides quantitative goals, outlines when tuning is needed, details step‑by‑step optimization procedures, and lists common GC strategies and command‑line parameters for effective Java performance tuning.
This article provides a concise yet comprehensive overview of the Java Virtual Machine, covering its core components, how Java source files are compiled and executed, class‑loader mechanisms, runtime memory areas, garbage‑collection algorithms, and practical JVM tuning parameters for optimal performance.
HAProxy is an open‑source L4/L7 load balancer offering reverse‑proxy, session persistence, diverse balancing algorithms, ACL‑based routing, health checks, and a web UI; the guide details its high‑performance architecture, high‑availability deployment with Keepalived, configuration examples, and extensive kernel and system tuning for massive traffic.
This article provides a detailed overview of Elasticsearch, covering its underlying Lucene technology, data types, indexing mechanisms, cluster architecture, shard and replica management, mapping definitions, installation steps, health monitoring, write and storage processes, and performance optimization techniques for production deployments.
This article deeply analyzes Kafka broker’s network architecture and request‑handling pipeline, walking through simple sequential models, multithreaded async designs, the Reactor pattern with Java NIO, key thread roles, core processing flow, and practical tuning parameters for high‑throughput, low‑latency deployments.
This article compiles practical operations guidelines covering safe testing, rigorous confirmation before commands, limiting multi‑person access, mandatory backups, careful use of destructive commands, SSH hardening, firewall rules, fine‑grained permissions, continuous monitoring, performance tuning steps, and a disciplined mindset to avoid costly incidents.
The team discovered that frequent Logback file rotations created thousands of FileOutputStream objects whose finalize‑based FinalReference instances flooded the G1GC reference‑processing phase, causing 13‑second pauses, and resolved the issue by enabling parallel reference processing, enlarging log rotation size, and adjusting GC initiation thresholds.
This article details the end‑to‑end process of moving Qunar's massive Elasticsearch logging cluster from a saturated data‑center to a new facility, covering background constraints, migration planning, manual and automated steps, performance‑tuning parameters, shard‑balancing techniques, and the final outcomes achieved.
This article analyzes a sluggish MySQL query, explains why the original plan performs a full table scan, and demonstrates how rewriting the EXISTS clause as an INNER JOIN and substituting the OR condition with a UNION dramatically reduces execution time from 42 seconds to 18 milliseconds.
This article explains how improper bucket and partition settings in Apache Doris can degrade read/write performance, provides quantitative guidelines for choosing bucket and partition counts, and introduces the automatic bucket feature with practical syntax and usage tips.
The article explains how to boost Android app performance by programmatically raising CPU frequency using Qualcomm’s BoostFramework, setting thread‑core affinity with native sched_setaffinity calls, and increasing execution priority via Process.setThreadPriority, providing Kotlin and C examples that demonstrate measurable speed gains.
This guide outlines eight practical Linux performance‑tuning methods—including kernel parameter tweaks, filesystem and network optimizations, process priority adjustments, memory management, scheduler configuration, software bloat removal, and log cleanup—while showcasing key tools and visual diagrams for effective system optimization.
This article compiles a comprehensive guide to Linux performance analysis, covering essential tools such as vmstat, iostat, dstat, top, htop, pidstat, strace, netstat, lsof, perf and sar, explaining their usage, output interpretation, and how they fit into a systematic performance tuning workflow.
This article presents comprehensive best‑practice recommendations for optimizing Elasticsearch deployments, covering hardware selection, RAID choices, index and shard planning, query and caching strategies, bulk indexing, refresh intervals, monitoring tools, version upgrades, and lifecycle management to achieve high performance, reliability, and scalability.
This guide explains why IntelliJ IDEA often feels sluggish due to low default memory limits, shows how to view current memory usage, and provides step‑by‑step instructions—including editing VM options, adjusting -Xmx and -Xms values, and clearing caches—to boost performance for Java developers.
A detailed investigation reveals that excessive Redis latency was caused by misconfigured JedisPool parameters, leading to long connection wait times and frequent connection churn, and shows how adjusting pool settings dramatically improved response times and overall system performance.
This article reviews the Java HotSpot memory model, explains when JVM tuning is needed, details common OutOfMemoryError scenarios with reproducible code, introduces built‑in monitoring utilities and third‑party tools, and provides practical tuning parameters and case studies for optimizing Java applications.
This article explains how to optimize Flink’s RocksDB state backend for large‑scale streaming jobs, covering state types, enabling latency tracking, incremental checkpoints, predefined options, and advanced memory and thread settings, with practical configuration examples and performance comparisons.
This article explains Tomcat's component architecture, describes the three critical parameters (maxThreads, maxConnections, acceptCount) that affect request handling, and provides practical tuning recommendations to improve backend performance without additional hardware costs.
This article details a November 2022 database outage caused by a sudden CPU and load surge, explains how the team diagnosed the issue, outlines the emergency steps taken, and shares practical SQL performance optimization recommendations to prevent similar incidents.
This article demonstrates multiple strategies for inserting 300,000 rows into a MySQL table using MyBatis and JDBC, comparing batch‑insert, single‑row loops, packet size limits, transaction handling, and performance tuning techniques such as batch size, waiting intervals, and index management.
This article provides an in-depth overview of Kafka, covering its core value as a message queue, fundamental concepts, cluster architecture, producer and consumer configurations, scaling strategies, monitoring tools, and practical operational commands for building and maintaining high‑throughput, highly available streaming systems.
The article investigates why pure‑read QPS drops significantly after bulk updates in OceanBase, reproduces the issue with a sysbench workload, analyses flame‑graph and SQL audit data, explains the LSM‑Tree read‑amplification mechanism, and proposes practical mitigation steps such as major freeze, plan binding, index creation, and the queuing‑table feature.
This guide explains how to optimize NGINX on Linux by adjusting worker processes, connection limits, GZIP compression, timeout values, buffer sizes, log handling, static content caching, and open file caching, providing step‑by‑step commands and configuration snippets for maximum server efficiency.
This article explains why SQL queries become slow, how to capture slow queries using MySQL's slow query log, and provides detailed step‑by‑step methods for analyzing execution plans, profiling, optimizer tracing, and I/O parameters to identify and resolve performance bottlenecks.
This article explains why MySQL slave lag is a critical issue, compares the official parallel replication schemes—including Commit‑Parent‑Based, Lock‑Based, and WRITESET—details their internal mechanisms, presents benchmark results, and provides the exact configuration parameters needed to enable high‑performance parallel replication.
Drawing from years of sysadmin experience, this guide lists concrete Linux operational habits—such as rigorous backups, cautious use of rm‑rf, single‑person changes, SSH hardening, firewall rules, monitoring, and disciplined performance tuning—to help teams avoid costly production failures.
Effective PostgreSQL performance tuning requires a holistic approach, covering hardware sizing, OS sysctl tweaks, filesystem choices, query and schema design, key PostgreSQL parameters, concurrency settings, cache management, and careful scheduling of automated maintenance tasks to avoid resource conflicts.
This article provides a comprehensive guide on optimizing Spark and Flink workloads, covering parallelism settings, garbage‑collection tuning, out‑of‑memory mitigation, and full production‑grade configuration examples for both frameworks.
This article provides a comprehensive technical overview of Kafka, covering its core message‑queue value, architecture components such as producers, consumers, topics, partitions and replication, high‑performance mechanisms like zero‑copy and OS cache, resource planning for disks, memory, CPU and network, operational tools and commands, consumer‑group management, rebalance strategies, and internal scheduling mechanisms such as the time‑wheel.
This article analyzes a production incident where excessive Full GC pauses during a high‑traffic promotion were traced to stale database connections in the DBCP pool, explains the investigation steps, root cause, and presents several JVM and connection‑pool configuration solutions to prevent similar performance degradations.
This guide explains why Linux performance depends on both hardware and software, outlines key factors such as CPU, memory, disk I/O, network, OS installation, kernel parameters, file systems, and application design, and provides a step‑by‑step process for administrators, architects, and developers to diagnose and optimize system speed.
This article provides a detailed analysis of the OMS community edition 3.3.1 architecture, explains each internal process, describes the migration workflow and monitoring metrics, and offers practical optimization tips to improve data migration performance.
This article presents a comprehensive, step‑by‑step guide on estimating capacity, selecting and configuring garbage collectors, sizing heap, young and survivor spaces, tuning thread stacks, and adding diagnostic options for a Java service that must handle one million login requests per day on a server with 8 GB of memory.
This article provides a comprehensive overview of Kafka’s core concepts, high‑performance design, cluster planning, resource evaluation, deployment steps, producer and consumer configurations, fault‑tolerance mechanisms, and operational tools, offering practical guidance for building and managing a high‑throughput Kafka production environment.
This guide outlines the most common PostgreSQL performance bottlenecks—from hardware and OS configuration to SQL, schema design, and parameter tuning—and provides concrete recommendations and sysctl settings to help DBAs and developers achieve a smoother, high‑throughput database operation.
This article walks through a step‑by‑step approach to sizing JVM heap, young generation, GC selection, and related parameters for a platform handling one million login requests per day on a node with 8 GB memory, providing practical formulas, example configurations, and tuning tips.
This guide explains common causes of 502 Bad Gateway in Nginx‑PHP‑MySQL stacks—such as memory limits, PHP‑FPM process settings, buffer sizes, timeouts, and system limits—and provides detailed configuration adjustments and command‑line checks to eliminate the errors.
This article presents a step‑by‑step guide on how to size and configure JVM parameters—including heap, young generation, stack, GC selection, and Metaspace—to reliably support a platform that processes one million login requests per day on a server with 8 GB of memory.
This article explains a step‑by‑step methodology for estimating capacity, selecting appropriate garbage collectors, sizing JVM memory regions, and tuning advanced parameters such as survivor ratio, tenuring threshold, and Metaspace to reliably support a login service that processes one million requests per day on a machine with 8 GB of RAM.
This article explains common scenarios that cause CPU usage to soar above 200% in production, outlines step‑by‑step diagnosis and remediation for MySQL and Java processes, and shares real‑world case studies with command‑line tools, indexing, caching, and code adjustments to bring CPU load back to normal levels.
This article walks through a practical SQL optimization workflow, showing how to detect problematic queries via system metrics and execution plans, analyze table structures and indexes, apply concrete ALTER statements and index changes, and iteratively verify performance improvements, illustrated with a detailed MySQL case study that achieved a twenty‑fold speed increase.
This article walks through a systematic approach to sizing and configuring JVM parameters—including heap, young generation, GC algorithm, Metaspace, and safety‑point settings—for a high‑traffic login service that receives one million requests per day on a server with 8 GB of memory, providing both practical code examples and interview‑style explanations.
This article walks through a systematic approach to sizing and configuring JVM memory and garbage‑collector parameters for a service handling one million login requests per day on an 8 GB server, covering capacity planning, GC selection, heap region sizing, thread‑stack settings, dynamic age thresholds, large‑object handling, and diagnostic options such as heap dumps and GC logging.
This article walks through a hands‑on process for detecting problematic SQL statements, interpreting execution plans, and applying concrete optimizations such as index adjustments and data‑type fixes to dramatically improve query performance, illustrated with a real MySQL case study.
This article documents the evolution of a data platform, explains Elasticsearch and Lucene fundamentals, and presents practical index and search performance optimizations—including bulk writes, refresh control, memory allocation, doc‑values tuning, and pagination strategies—that enable cross‑month queries and sub‑second responses on billions of records.
This comprehensive guide outlines 46 practical MySQL optimization rules—from understanding the query execution flow and naming conventions to index design, join strategies, pagination techniques, and safe use of data types—providing concrete examples and code snippets that help developers dramatically improve database performance and reliability.
This article outlines the ten most frequent PostgreSQL pitfalls—from default configurations and poor schema design to inadequate backups and mismanaged extensions—providing concrete SQL queries, tuning tools, and best‑practice recommendations to help DBAs detect, diagnose, and prevent each issue before it harms production workloads.
This article summarizes practical experiences with ClickHouse log storage, covering how to size and tune clusters, key table schema design considerations, partitioning strategies, index choices, compression algorithms, and provides a demo CREATE TABLE script for production use.
This article explores Linux kernel TCP connection handling by examining socket data structures, port range and file descriptor tuning, core functions like tcp_v4_rcv, and lookup mechanisms, while offering practical tips to boost client-side concurrent connections beyond traditional limits.
This guide walks you through eight essential NGINX configuration adjustments—including worker processes, connection limits, GZIP compression, timeout values, buffer sizes, logging, static content caching, and open file caching—to maximize web server performance on Linux systems.
This article explains why low‑latency Nginx HTTPS is crucial for instant search, breaks down TLS handshake round‑trips, and provides step‑by‑step configuration changes—such as enabling HTTP/2, adjusting ciphers, activating OCSP stapling, tweaking buffer sizes and session cache—to achieve roughly a 30% reduction in request latency.
