> Understand your problems: performance problem (slow for a single user) or scalability problem (fast for a single user but slow under heavy load) by reviewing some [basic design concepts](#basic).
* [How to deal with latency](http://highscalability.com/latency-everywhere-and-it-costs-you-sales-how-crush-it)
* [Dropbox case: Striving for maximal throughput with acceptable latency](https://blogs.dropbox.com/tech/2017/09/optimizing-web-servers-for-high-throughput-and-low-latency/)
* [What is ACID?](http://highscalability.com/drop-acid-and-think-about-data)
* [Design for Loose-coupling](https://dzone.com/articles/the-importance-of-loose-coupling-in-rest-api-desig)
* [Design for Resiliency](http://highscalability.com/blog/2012/12/31/designing-for-resiliency-will-be-so-2013.html)
* [Design for Self-healing when failures occur](https://docs.microsoft.com/en-us/azure/architecture/guide/design-principles/self-healing)
* [Design for Scale out](https://docs.microsoft.com/en-us/azure/architecture/guide/design-principles/scale-out)
* [Design for Scale: Three best practices](http://highscalability.com/blog/2015/5/11/designing-for-scale-three-principles-and-three-practices-fro.html)
* [Design for Evolution](https://docs.microsoft.com/en-us/azure/architecture/guide/design-principles/design-for-evolution)
* [Distributed tracing with Pintrace at Pinterest](https://medium.com/@Pinterest_Engineering/distributed-tracing-at-pinterest-with-new-open-source-tools-a4f8a5562f6b)
* [Analyzing distributed trace data at Pinterest](https://medium.com/@Pinterest_Engineering/analyzing-distributed-trace-data-6aae58919949)
* [Scalable and reliable log ingestion at Pinterest](https://medium.com/@Pinterest_Engineering/scalable-and-reliable-data-ingestion-at-pinterest-b921c2ee8754)
* [Logging Service with Spark at CERN Accelerator](https://databricks.com/blog/2017/12/14/the-architecture-of-the-next-cern-accelerator-logging-service.html)
* [Moving to MemSQL for solving problems at Tapjoy: horizontally scalable, ACID compliant, MySQL compatibility](http://eng.tapjoy.com/blog-list/moving-to-memsql)
* [Storing Images in Cassandra at Walmart Scale](https://medium.com/walmartlabs/building-object-store-storing-images-in-cassandra-walmart-scale-a6b9c02af593)
* [eBay: Building Mission-Critical Multi-Data Center Applications with MongoDB](https://www.mongodb.com/blog/post/ebay-building-mission-critical-multi-data-center-applications-with-mongodb)
* [The AWS and MongoDB Infrastructure of Parse (acquired by Facebook)](https://medium.baqend.com/parse-is-gone-a-few-secrets-about-their-infrastructure-91b3ab2fcf71)
* [Practical NoSQL resilience design pattern for the enterprise (eBay)](https://www.ebayinc.com/stories/blogs/tech/practical-nosql-resilience-design-pattern-for-the-enterprise/)
* [Why SQL is beating NoSQL, and what this means for the future of data](https://blog.timescale.com/why-sql-beating-nosql-what-this-means-for-future-of-data-time-series-database-348b777b847a)
* [Sharding MySQL at Pinterest](https://medium.com/@Pinterest_Engineering/sharding-pinterest-how-we-scaled-our-mysql-fleet-3f341e96ca6f)
* [How Airbnb Partitioned Main MySQL Database in Two Weeks](https://medium.com/airbnb-engineering/how-we-partitioned-airbnb-s-main-database-in-two-weeks-55f7e006ff21)
* [Replication is the Key for Scalability & High Availability](http://basho.com/posts/technical/replication-is-the-key-for-scalability-high-availability/)
* [Scaling MySQL-based financial reporting system at Airbnb](https://medium.com/airbnb-engineering/tracking-the-money-scaling-financial-reporting-at-airbnb-6d742b80f040)
* [Time Series Database (TSDB)](https://www.influxdata.com/time-series-database/)
* [Beringei: high-performance time series storage engine at Facebook](https://code.facebook.com/posts/952820474848503/beringei-a-high-performance-time-series-storage-engine/)
* [Atlas: In-memory dimensional time series database at Netflix](https://medium.com/netflix-techblog/introducing-atlas-netflixs-primary-telemetry-platform-bd31f4d8ed9a)
* [Concurrency series by Larry Osterman (Principal SDE at Microsoft)](https://social.msdn.microsoft.com/Profile/Larry%2bOsterman%2b%5BMSFT%5D/activity)
* [Part 8 – Concurrency for scalability](https://blogs.msdn.microsoft.com/larryosterman/2005/02/28/concurrency-part-8-concurrency-for-scalability/)
* [Part 9 - APIs that enable scalable programming](https://blogs.msdn.microsoft.com/larryosterman/2005/03/02/concurrency-part-9-apis-that-enable-scalable-programming/)
* [Part 10 - How do you know if you’ve got a scalability issue?](https://blogs.msdn.microsoft.com/larryosterman/2005/03/03/concurrency-part-10-how-do-you-know-if-youve-got-a-scalability-issue/)
* [Autoscaling Pub-Sub Consumers at Spotify](https://labs.spotify.com/2017/11/20/autoscaling-pub-sub-consumers/)
* [Pulsar: Pub-Sub Messaging at Scale at Yahoo](https://yahooeng.tumblr.com/post/150078336821/open-sourcing-pulsar-pub-sub-messaging-at-scale)
* [Wormhole: Pub-Sub system at Facebook (2013)](https://code.facebook.com/posts/188966771280871/wormhole-pub-sub-system-moving-data-through-space-and-time/)
* [Bullet: Forward-Looking Query Engine for Streaming Data at Yahoo](https://yahooeng.tumblr.com/post/161855616651/open-sourcing-bullet-yahoos-forward-looking)
* [Load Balancing infrastructure to support more than 1.3 billion users at Facebook](https://www.usenix.org/conference/srecon15europe/program/presentation/shuff)
* [Load Balancing with Eureka at Netflix](https://medium.com/netflix-techblog/netflix-shares-cloud-load-balancing-and-failover-tool-eureka-c10647ef95e5)
* [Load Balancing at Yelp](https://engineeringblog.yelp.com/2017/05/taking-zero-downtime-load-balancing-even-further.html)
* [Load Balancing at Github](https://githubengineering.com/introducing-glb/)
* [Consistent Hashing to Improve Load Balancing at Vimeo](https://medium.com/vimeo-engineering-blog/improving-load-balancing-with-a-new-consistent-hashing-algorithm-9f1bd75709ed)
* [UDP Load Balancing at 500 pixel](https://developers.500px.com/udp-load-balancing-with-keepalived-167382d7ad08)
* [Loop Parallelism Pattern: Extracting parallel tasks from loops](https://www.cs.umd.edu/class/fall2001/cmsc411/projects/unroll/main.htm)
* [Fork/Join Pattern: Good for recursive data processing](http://highscalability.com/learn-how-exploit-multiple-cores-better-performance-and-scalability)
* [MapReduce Pattern: Born for Big Data](http://static.googleusercontent.com/media/research.google.com/en/us/archive/mapreduce-osdi04.pdf)
* [Parallelize the rendering of web pages: Use case of Yelp.com](https://engineeringblog.yelp.com/2017/07/generating-web-pages-in-parallel-with-pagelets.html)
* [Scaling Gradient Boosted Trees for Click-Through-Rate Prediction at Yelp](https://engineeringblog.yelp.com/2018/01/building-a-distributed-ml-pipeline-part1.html)
* [TensorFlowOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo](https://yahooeng.tumblr.com/post/157196488076/open-sourcing-tensorflowonspark-distributed-deep)
* [CaffeOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo](https://yahooeng.tumblr.com/post/139916828451/caffeonspark-open-sourced-for-distributed-deep)
* [Every Day Is Monday in Operations - LinkedIn (11 part series)](https://www.linkedin.com/pulse/introduction-every-day-monday-operations-benjamin-purgason)
* [Always use timeouts (if possible)](https://www.javaworld.com/article/2824163/application-performance/stability-patterns-applied-in-a-restful-architecture.html)
* [Let it crash/Supervisors: Embrace failure as a natural state in the life-cycle of the application](http://erlang.org/doc/design_principles/sup_princ.html)
* [Crash early: An error now is better than a response tomorrow](http://odino.org/better-performance-the-case-for-timeouts/)
* [Bulkheads: Partition and tolerate failure in one part](https://skife.org/architecture/fault-tolerance/2009/12/31/bulkheads.html)
* [Steady state: Always put logs on separate disk](https://docs.microsoft.com/en-us/sql/relational-databases/policy-based-management/place-data-and-log-files-on-separate-drives)
* [Throttling: Maintain a steady pace](http://www.sosp.org/2001/papers/welsh.pdf)
* [Multi-clustering: Improving Resiliency and Stability of a Large-scale Monolithic API Service at LinkedIn](https://engineering.linkedin.com/blog/2017/11/improving-resiliency-and-stability-of-a-large-scale-api)
* [Configuration management for distributed systems (using GitHub and cfg4j) at Flickr](https://code.flickr.net/2016/03/24/configuration-management-for-distributed-systems-using-github-and-cfg4j/)
* [Seagull: Distributed system that helps running > 20 million tests per day at Yelp](https://engineeringblog.yelp.com/2017/04/how-yelp-runs-millions-of-tests-every-day.html)
* [Designing for Failure: Scaling Uber's Backend by Breaking Everything - Matt Ranney, Chief Systems Architect at Uber](https://www.youtube.com/watch?v=nuiLcWE8sPA)
