�ݺ�ߣshows by User: DanielHochman

�ݺ�ߣshows by User: DanielHochman / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: DanielHochman / Mon, 17 Dec 2018 23:10:16 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: DanielHochman Instrumenting and Scaling Databases with Envoy /slideshow/instrumenting-and-scaling-databases-with-envoy/126151551 kubeconseattle2018-181217231016
Every request to a database at Lyft is proxied by Envoy, providing complete visibility into the L3/L4 aspects of database interactions. This allows engineers to easily visualize changes to a database's load profile and pinpoint the root cause if necessary. Lyft has also open-sourced codecs for MongoDB, DynamoDB, and Redis. Protocol codecs in combination with custom filters yield benefits ranging from operation-level observability to horizontal scalability via sharding. Using Envoy for this purpose means that enhancements are implemented once and usable across a polyglot stack. The talk demonstrates Envoy's utility beyond traditional RPC service interactions in the network.]]>
Every request to a database at Lyft is proxied by Envoy, providing complete visibility into the L3/L4 aspects of database interactions. This allows engineers to easily visualize changes to a database's load profile and pinpoint the root cause if necessary. Lyft has also open-sourced codecs for MongoDB, DynamoDB, and Redis. Protocol codecs in combination with custom filters yield benefits ranging from operation-level observability to horizontal scalability via sharding. Using Envoy for this purpose means that enhancements are implemented once and usable across a polyglot stack. The talk demonstrates Envoy's utility beyond traditional RPC service interactions in the network.]]> Mon, 17 Dec 2018 23:10:16 GMT /slideshow/instrumenting-and-scaling-databases-with-envoy/126151551 DanielHochman@slideshare.net(DanielHochman) Instrumenting and Scaling Databases with Envoy DanielHochman Every request to a database at Lyft is proxied by Envoy, providing complete visibility into the L3/L4 aspects of database interactions. This allows engineers to easily visualize changes to a database's load profile and pinpoint the root cause if necessary. Lyft has also open-sourced codecs for MongoDB, DynamoDB, and Redis. Protocol codecs in combination with custom filters yield benefits ranging from operation-level observability to horizontal scalability via sharding. Using Envoy for this purpose means that enhancements are implemented once and usable across a polyglot stack. The talk demonstrates Envoy's utility beyond traditional RPC service interactions in the network. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/kubeconseattle2018-181217231016-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Every request to a database at Lyft is proxied by Envoy, providing complete visibility into the L3/L4 aspects of database interactions. This allows engineers to easily visualize changes to a database's load profile and pinpoint the root cause if necessary. Lyft has also open-sourced codecs for MongoDB, DynamoDB, and Redis. Protocol codecs in combination with custom filters yield benefits ranging from operation-level observability to horizontal scalability via sharding. Using Envoy for this purpose means that enhancements are implemented once and usable across a polyglot stack. The talk demonstrates Envoy's utility beyond traditional RPC service interactions in the network.

Instrumenting and Scaling Databases with Envoy from Daniel Hochman

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0 Redis at Lyft: 2,000 Instances and Beyond /DanielHochman/redis-at-lyft-2000-instances-and-beyond lyftredisconf20181-180620235610
Talk given at RedisConf '18 by Daniel Hochman. Revisit Lyft's Redis infrastructure at a higher level following last year's focus on geospatial indexing. This year's talk is broadly applicable to optimizing any type of application powered by Redis. This session will take a broader look at Lyft's deployment of Redis and deep dive into what we've learned recently when writing our open-source Envoy Redis proxy. The talk will cover: deployment, configuration, partitioning, consistent hashing, data model optimization, and client abstractions.]]>
Talk given at RedisConf '18 by Daniel Hochman. Revisit Lyft's Redis infrastructure at a higher level following last year's focus on geospatial indexing. This year's talk is broadly applicable to optimizing any type of application powered by Redis. This session will take a broader look at Lyft's deployment of Redis and deep dive into what we've learned recently when writing our open-source Envoy Redis proxy. The talk will cover: deployment, configuration, partitioning, consistent hashing, data model optimization, and client abstractions.]]> Wed, 20 Jun 2018 23:56:10 GMT /DanielHochman/redis-at-lyft-2000-instances-and-beyond DanielHochman@slideshare.net(DanielHochman) Redis at Lyft: 2,000 Instances and Beyond DanielHochman Talk given at RedisConf '18 by Daniel Hochman. Revisit Lyft's Redis infrastructure at a higher level following last year's focus on geospatial indexing. This year's talk is broadly applicable to optimizing any type of application powered by Redis. This session will take a broader look at Lyft's deployment of Redis and deep dive into what we've learned recently when writing our open-source Envoy Redis proxy. The talk will cover: deployment, configuration, partitioning, consistent hashing, data model optimization, and client abstractions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lyftredisconf20181-180620235610-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Talk given at RedisConf '18 by Daniel Hochman. Revisit Lyft's Redis infrastructure at a higher level following last year's focus on geospatial indexing. This year's talk is broadly applicable to optimizing any type of application powered by Redis. This session will take a broader look at Lyft's deployment of Redis and deep dive into what we've learned recently when writing our open-source Envoy Redis proxy. The talk will cover: deployment, configuration, partitioning, consistent hashing, data model optimization, and client abstractions.

Redis at Lyft: 2,000 Instances and Beyond from Daniel Hochman

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0 Open-source Infrastructure at Lyft /slideshow/open-source-infrastructure-at-lyft/78323537 infrastructureandopensourceatlyft-170727201104
Lyft has built an internal platform to allow the backend to go from a single monolithic service to more than a hundred microservices. Several key components of the platform are available on GitHub. The talk will cover the following projects: - envoy: L7 edge and mesh proxy, enabling transparent networking for microservice architectures - discovery: track infrastructure topology in an eventually consistent manner - ratelimit: protect your systems from bad actors - confidant: store secrets encrypted at rest in DynamoDB]]>
Lyft has built an internal platform to allow the backend to go from a single monolithic service to more than a hundred microservices. Several key components of the platform are available on GitHub. The talk will cover the following projects: - envoy: L7 edge and mesh proxy, enabling transparent networking for microservice architectures - discovery: track infrastructure topology in an eventually consistent manner - ratelimit: protect your systems from bad actors - confidant: store secrets encrypted at rest in DynamoDB]]> Thu, 27 Jul 2017 20:11:04 GMT /slideshow/open-source-infrastructure-at-lyft/78323537 DanielHochman@slideshare.net(DanielHochman) Open-source Infrastructure at Lyft DanielHochman Lyft has built an internal platform to allow the backend to go from a single monolithic service to more than a hundred microservices. Several key components of the platform are available on GitHub. The talk will cover the following projects: - envoy: L7 edge and mesh proxy, enabling transparent networking for microservice architectures - discovery: track infrastructure topology in an eventually consistent manner - ratelimit: protect your systems from bad actors - confidant: store secrets encrypted at rest in DynamoDB <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/infrastructureandopensourceatlyft-170727201104-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Lyft has built an internal platform to allow the backend to go from a single monolithic service to more than a hundred microservices. Several key components of the platform are available on GitHub. The talk will cover the following projects: - envoy: L7 edge and mesh proxy, enabling transparent networking for microservice architectures - discovery: track infrastructure topology in an eventually consistent manner - ratelimit: protect your systems from bad actors - confidant: store secrets encrypted at rest in DynamoDB

Open-source Infrastructure at Lyft from Daniel Hochman

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0 Geospatial Indexing at Scale: The 15 Million QPS Redis Architecture Powering Lyft /slideshow/geospatial-indexing-at-scale-the-15-million-qps-redis-architecture-powering-lyft/76662828 lyft-redisconf-2017-dhochman-geospatialatscale-170605164624
Talk given at RedisConf 17 on June 1, 2017 by Daniel Hochman. A video will be published by the conference organizers. Abstract: Built-in GEO commands in Redis provide a solid foundation for location-based applications. The scale of Lyft requires a completely different approach to the problem. Learn how to push beyond your constraints to build a highly available, high throughput, horizontally scalable Redis architecture. The techniques presented in this case study are broadly applicable to scaling any type of application powered by Redis. The talk will cover data modeling, open-source solutions, reliability engineering, and Lyft platform.]]>
Talk given at RedisConf 17 on June 1, 2017 by Daniel Hochman. A video will be published by the conference organizers. Abstract: Built-in GEO commands in Redis provide a solid foundation for location-based applications. The scale of Lyft requires a completely different approach to the problem. Learn how to push beyond your constraints to build a highly available, high throughput, horizontally scalable Redis architecture. The techniques presented in this case study are broadly applicable to scaling any type of application powered by Redis. The talk will cover data modeling, open-source solutions, reliability engineering, and Lyft platform.]]> Mon, 05 Jun 2017 16:46:24 GMT /slideshow/geospatial-indexing-at-scale-the-15-million-qps-redis-architecture-powering-lyft/76662828 DanielHochman@slideshare.net(DanielHochman) Geospatial Indexing at Scale: The 15 Million QPS Redis Architecture Powering Lyft DanielHochman Talk given at RedisConf 17 on June 1, 2017 by Daniel Hochman. A video will be published by the conference organizers. Abstract: Built-in GEO commands in Redis provide a solid foundation for location-based applications. The scale of Lyft requires a completely different approach to the problem. Learn how to push beyond your constraints to build a highly available, high throughput, horizontally scalable Redis architecture. The techniques presented in this case study are broadly applicable to scaling any type of application powered by Redis. The talk will cover data modeling, open-source solutions, reliability engineering, and Lyft platform. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lyft-redisconf-2017-dhochman-geospatialatscale-170605164624-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Talk given at RedisConf 17 on June 1, 2017 by Daniel Hochman. A video will be published by the conference organizers. Abstract: Built-in GEO commands in Redis provide a solid foundation for location-based applications. The scale of Lyft requires a completely different approach to the problem. Learn how to push beyond your constraints to build a highly available, high throughput, horizontally scalable Redis architecture. The techniques presented in this case study are broadly applicable to scaling any type of application powered by Redis. The talk will cover data modeling, open-source solutions, reliability engineering, and Lyft platform.

Geospatial Indexing at Scale: The 15 Million QPS Redis Architecture Powering Lyft from Daniel Hochman

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/kubeconseattle2018-181217231016-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/instrumenting-and-scaling-databases-with-envoy/126151551 Instrumenting and Scal... https://cdn.slidesharecdn.com/ss_thumbnails/lyftredisconf20181-180620235610-thumbnail.jpg?width=320&height=320&fit=bounds DanielHochman/redis-at-lyft-2000-instances-and-beyond Redis at Lyft: 2,000 I... https://cdn.slidesharecdn.com/ss_thumbnails/infrastructureandopensourceatlyft-170727201104-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/open-source-infrastructure-at-lyft/78323537 Open-source Infrastruc...