際際滷shows by User: zacharycox / http://www.slideshare.net/images/logo.gif 際際滷shows by User: zacharycox / Sat, 29 Oct 2016 13:24:29 GMT 際際滷Share feed for 際際滷shows by User: zacharycox Updating materialized views and caches using kafka /zacharycox/updating-materialized-views-and-caches-using-kafka updatingmaterializedviewsandcachesusingkafka-161029132430
https://github.com/zcox/twitter-microservices-example]]>
https://github.com/zcox/twitter-microservices-example]]> Sat, 29 Oct 2016 13:24:29 GMT /zacharycox/updating-materialized-views-and-caches-using-kafka zacharycox@slideshare.net(zacharycox) Updating materialized views and caches using kafka zacharycox https://github.com/zcox/twitter-microservices-example <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/updatingmaterializedviewsandcachesusingkafka-161029132430-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> https://github.com/zcox/twitter-microservices-example
Updating materialized views and caches using kafka from Zach Cox
]]> 2403 4 https://cdn.slidesharecdn.com/ss_thumbnails/updatingmaterializedviewsandcachesusingkafka-161029132430-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Replicating application data into materialized views /slideshow/replicating-application-data-into-materialized-views/55851933 replicatingapplicationdataintomaterializedviews1-151205172816-lva1-app6892
Many applications start out storing their data in a relational database in a well-defined, normalized schema. Over time, this primary data store often begins to fail to meet some of the application's needs. Perhaps some queries that need to run against it are too slow or complex, or maybe the application puts too much load on the database. Traditional solutions to these problems such as batch loads into other data stores, or the addition of a remote cache like Memcached or Redis to the application, suffer from problems such as long delays and cache misses. If changes to the tables in the primary database are also captured in Kafka topics, then we can build better solutions that stream data into materialized views and in-process data caches. We'll explore these ideas and see code examples using Scala, Postgres, Kafka, Elasticsearch and RocksDB.]]>
Many applications start out storing their data in a relational database in a well-defined, normalized schema. Over time, this primary data store often begins to fail to meet some of the application's needs. Perhaps some queries that need to run against it are too slow or complex, or maybe the application puts too much load on the database. Traditional solutions to these problems such as batch loads into other data stores, or the addition of a remote cache like Memcached or Redis to the application, suffer from problems such as long delays and cache misses. If changes to the tables in the primary database are also captured in Kafka topics, then we can build better solutions that stream data into materialized views and in-process data caches. We'll explore these ideas and see code examples using Scala, Postgres, Kafka, Elasticsearch and RocksDB.]]> Sat, 05 Dec 2015 17:28:16 GMT /slideshow/replicating-application-data-into-materialized-views/55851933 zacharycox@slideshare.net(zacharycox) Replicating application data into materialized views zacharycox Many applications start out storing their data in a relational database in a well-defined, normalized schema. Over time, this primary data store often begins to fail to meet some of the application's needs. Perhaps some queries that need to run against it are too slow or complex, or maybe the application puts too much load on the database. Traditional solutions to these problems such as batch loads into other data stores, or the addition of a remote cache like Memcached or Redis to the application, suffer from problems such as long delays and cache misses. If changes to the tables in the primary database are also captured in Kafka topics, then we can build better solutions that stream data into materialized views and in-process data caches. We'll explore these ideas and see code examples using Scala, Postgres, Kafka, Elasticsearch and RocksDB. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/replicatingapplicationdataintomaterializedviews1-151205172816-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Many applications start out storing their data in a relational database in a well-defined, normalized schema. Over time, this primary data store often begins to fail to meet some of the application&#39;s needs. Perhaps some queries that need to run against it are too slow or complex, or maybe the application puts too much load on the database. Traditional solutions to these problems such as batch loads into other data stores, or the addition of a remote cache like Memcached or Redis to the application, suffer from problems such as long delays and cache misses. If changes to the tables in the primary database are also captured in Kafka topics, then we can build better solutions that stream data into materialized views and in-process data caches. We&#39;ll explore these ideas and see code examples using Scala, Postgres, Kafka, Elasticsearch and RocksDB.
Replicating application data into materialized views from Zach Cox
]]> 662 4 https://cdn.slidesharecdn.com/ss_thumbnails/replicatingapplicationdataintomaterializedviews1-151205172816-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Event Stream Processing with Kafka and Samza /zacharycox/event-stream-processing-with-kafka-and-samza event-stream-processing-with-kafka-and-samza-141216111130-conversion-gate02
Event Stream Processing with Kafka and Samza, presented at Iowa Code Camp Fall 2014.]]>
Event Stream Processing with Kafka and Samza, presented at Iowa Code Camp Fall 2014.]]> Tue, 16 Dec 2014 11:11:30 GMT /zacharycox/event-stream-processing-with-kafka-and-samza zacharycox@slideshare.net(zacharycox) Event Stream Processing with Kafka and Samza zacharycox Event Stream Processing with Kafka and Samza, presented at Iowa Code Camp Fall 2014. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/event-stream-processing-with-kafka-and-samza-141216111130-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Event Stream Processing with Kafka and Samza, presented at Iowa Code Camp Fall 2014.
Event Stream Processing with Kafka and Samza from Zach Cox
]]> 8460 7 https://cdn.slidesharecdn.com/ss_thumbnails/event-stream-processing-with-kafka-and-samza-141216111130-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Metrics /slideshow/metrics-20756091/20756091 metrics-cijug-130507185325-phpapp02
Talk on Metrics at Central Iowa Java User Group.]]>
Talk on Metrics at Central Iowa Java User Group.]]> Tue, 07 May 2013 18:53:25 GMT /slideshow/metrics-20756091/20756091 zacharycox@slideshare.net(zacharycox) Metrics zacharycox Talk on Metrics at Central Iowa Java User Group. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/metrics-cijug-130507185325-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Talk on Metrics at Central Iowa Java User Group.
Metrics from Zach Cox
]]> 1535 3 https://cdn.slidesharecdn.com/ss_thumbnails/metrics-cijug-130507185325-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-zacharycox-48x48.jpg?cb=1529686466 Technical leader, startup founder and software developer focused on solving real, valuable business problems using new technology. Design and build large, scalable, distributed web services, web apps, mobile apps and desktop apps. Understand and implement highly technical algorithms. Foster local developer communities. theza.ch https://cdn.slidesharecdn.com/ss_thumbnails/updatingmaterializedviewsandcachesusingkafka-161029132430-thumbnail.jpg?width=320&height=320&fit=bounds zacharycox/updating-materialized-views-and-caches-using-kafka Updating materialized ... https://cdn.slidesharecdn.com/ss_thumbnails/replicatingapplicationdataintomaterializedviews1-151205172816-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/replicating-application-data-into-materialized-views/55851933 Replicating applicatio... https://cdn.slidesharecdn.com/ss_thumbnails/event-stream-processing-with-kafka-and-samza-141216111130-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds zacharycox/event-stream-processing-with-kafka-and-samza Event Stream Processin...