Иван Бурмистров "Строго ориентированная последовательность временных событий"...it-people
Ivan Burmistrov presents an algorithm for using Cassandra as an event logger. The algorithm aims to minimize time lag between writes and reads while ensuring write safety. Events are initially written in a "bad" state and only transition to "good" once no conflicting events exist. This allows reads to utilize caching while avoiding reading unstable data. The algorithm provides advantages like small time lags, cacheability and independence from synchronization, though writes are slower. Potential improvements and applications are also discussed.
Lessons from Cassandra & Spark (Matthias Niehoff & Stephan Kepser, codecentri...DataStax
We built an application based on the principles of CQRS and Event Sourcing using Cassandra and Spark. During the project we encountered a number of challenges and problems with Cassandra and the Spark Connector.
In this talk we want to outline a few of those problems and our actions to solve them. While some problems are specific to CQRS and Event Sourcing applications most of them are use case independent.
About the Speakers
Matthias Niehoff IT-Consultant, codecentric AG
works as an IT-Consultant at codecentric AG in Germany. His focus is on big data & streaming applications with Apache Cassandra & Apache Spark. Yet he does not lose track of other tools in the area of big data. Matthias shares his experiences on conferences, meetups and usergroups.
Stephan Kepser Senior IT Consultant and Data Architect, codecentric AG
Dr. Stephan Kepser is an expert on cloud computing and big data. He wrote a couple of journal articles and blog posts on subjects of both fields. His interests reach from legal questions to questions of architecture and design of cloud computing and big data systems to technical details of NoSQL databases.
A key feature when monitoring and debugging any Cloud infrastructure is to provide the ability to trace, track, and collate all the individual, discrete steps that compose an event. A typical resource action in OpenStack is often a combination of smaller tasks -- which given the distributed nature of OpenStack -- can fail at unpredictable points in the workflow. By collecting the appropriate events, operators can view all events within Ceilometer, filter on a failed action and trace back the history of related events to spot anomalies or errors. In this talk, we provide an overview of the recent enhancements made in Ceilometer to support the collection of event notifications from OpenStack services. We will describe: how events are processed, transformed and stored in Ceilometer; how you can derive metrics from events; and how it’s possible to track the events of a resource and analyse where errors occur.
The document summarizes optimisation opportunities and testing results for Gnocchi v3 compared to v2. It discusses improvements to coordination and scheduling to minimize contention, performance improvements for large datasets, a new storage format to reduce operations and disk size, and benchmark results showing processing time reductions of up to 23% and write throughput increases.
Building a Fast, Resilient Time Series Store with Cassandra (Alex Petrov, Dat...DataStax
Cassandra is awesome for many things. One of the things it's awesome for is Time Series. Combining the power of Cassandra with APIs of existing Time Series tools, such as Graphite can yield interesting results.
Cyanite is a Time Series aggregator and store built on top of Cassandra. It's fully compatible with Graphite, can serve as a plug-in replacement for Graphite and Graphite web.
Cyanite is using SASI indexes to make glob metric path queries, can query and aggregate, store, display and analyse metrics from hundreds and thousands of servers.
Which data modelling practices work best for Time Series, which new awesome Cassandra features you can use to make your Time Series analysis better.
About the Speaker
Alex Petrov Software Engineer, DataStax
Polyglot programmer. Interested in algorithms, distributed systems, algebra and high performance solutions.
This document summarizes the author's experience optimizing Gnocchi, an open source time-series database, to store metrics for hundreds of thousands of resources over many months. The author describes improving performance by adding Ceph storage nodes, tuning Ceph configurations, minimizing I/O operations, and improving the storage format. Benchmark results show the new version achieves 50% higher write throughput, 40-60% faster computation times, 30-60% better overall performance, and 30-40% fewer operations. Usage hints are also provided to help optimize for different use cases.
early benchmarks on pre-release Gnocchi v4. includes benchmark comparison between all-ceph v3.x driver versus all-ceph v4 driver. also, shows benchmark using redis+ceph deployment.
Иван Бурмистров "Строго ориентированная последовательность временных событий"...it-people
Ivan Burmistrov presents an algorithm for using Cassandra as an event logger. The algorithm aims to minimize time lag between writes and reads while ensuring write safety. Events are initially written in a "bad" state and only transition to "good" once no conflicting events exist. This allows reads to utilize caching while avoiding reading unstable data. The algorithm provides advantages like small time lags, cacheability and independence from synchronization, though writes are slower. Potential improvements and applications are also discussed.
Lessons from Cassandra & Spark (Matthias Niehoff & Stephan Kepser, codecentri...DataStax
We built an application based on the principles of CQRS and Event Sourcing using Cassandra and Spark. During the project we encountered a number of challenges and problems with Cassandra and the Spark Connector.
In this talk we want to outline a few of those problems and our actions to solve them. While some problems are specific to CQRS and Event Sourcing applications most of them are use case independent.
About the Speakers
Matthias Niehoff IT-Consultant, codecentric AG
works as an IT-Consultant at codecentric AG in Germany. His focus is on big data & streaming applications with Apache Cassandra & Apache Spark. Yet he does not lose track of other tools in the area of big data. Matthias shares his experiences on conferences, meetups and usergroups.
Stephan Kepser Senior IT Consultant and Data Architect, codecentric AG
Dr. Stephan Kepser is an expert on cloud computing and big data. He wrote a couple of journal articles and blog posts on subjects of both fields. His interests reach from legal questions to questions of architecture and design of cloud computing and big data systems to technical details of NoSQL databases.
A key feature when monitoring and debugging any Cloud infrastructure is to provide the ability to trace, track, and collate all the individual, discrete steps that compose an event. A typical resource action in OpenStack is often a combination of smaller tasks -- which given the distributed nature of OpenStack -- can fail at unpredictable points in the workflow. By collecting the appropriate events, operators can view all events within Ceilometer, filter on a failed action and trace back the history of related events to spot anomalies or errors. In this talk, we provide an overview of the recent enhancements made in Ceilometer to support the collection of event notifications from OpenStack services. We will describe: how events are processed, transformed and stored in Ceilometer; how you can derive metrics from events; and how it’s possible to track the events of a resource and analyse where errors occur.
The document summarizes optimisation opportunities and testing results for Gnocchi v3 compared to v2. It discusses improvements to coordination and scheduling to minimize contention, performance improvements for large datasets, a new storage format to reduce operations and disk size, and benchmark results showing processing time reductions of up to 23% and write throughput increases.
Building a Fast, Resilient Time Series Store with Cassandra (Alex Petrov, Dat...DataStax
Cassandra is awesome for many things. One of the things it's awesome for is Time Series. Combining the power of Cassandra with APIs of existing Time Series tools, such as Graphite can yield interesting results.
Cyanite is a Time Series aggregator and store built on top of Cassandra. It's fully compatible with Graphite, can serve as a plug-in replacement for Graphite and Graphite web.
Cyanite is using SASI indexes to make glob metric path queries, can query and aggregate, store, display and analyse metrics from hundreds and thousands of servers.
Which data modelling practices work best for Time Series, which new awesome Cassandra features you can use to make your Time Series analysis better.
About the Speaker
Alex Petrov Software Engineer, DataStax
Polyglot programmer. Interested in algorithms, distributed systems, algebra and high performance solutions.
This document summarizes the author's experience optimizing Gnocchi, an open source time-series database, to store metrics for hundreds of thousands of resources over many months. The author describes improving performance by adding Ceph storage nodes, tuning Ceph configurations, minimizing I/O operations, and improving the storage format. Benchmark results show the new version achieves 50% higher write throughput, 40-60% faster computation times, 30-60% better overall performance, and 30-40% fewer operations. Usage hints are also provided to help optimize for different use cases.
early benchmarks on pre-release Gnocchi v4. includes benchmark comparison between all-ceph v3.x driver versus all-ceph v4 driver. also, shows benchmark using redis+ceph deployment.
Eric Lubow discussed the challenges his company SimpleReach faced when using Cassandra counters at scale to track metrics for their content analytics platform. SimpleReach processes huge volumes of data, writing 250,000-300,000 counter values per second. Early on they encountered performance issues with counters but were able to optimize their implementation through techniques like pre-aggregating writes, using counter batches, extensive monitoring, upgrading hardware, and tuning Cassandra and JVM settings. Upgrading to Cassandra 2.1 also helped improve counter performance.
Cassandra Community Webinar: Apache Cassandra InternalsDataStax
Apache Cassandra solves many interesting problems to provide a scalable, distributed, fault tolerant database. Cluster wide operations track node membership, direct requests and implement consistency guarantees. At the node level, the Log Structured storage engine provides high performance reads and writes. All of this is implemented in a Java code base that has greatly matured over the past few years.
In this webinar Aaron Morton will step through read and write requests, automatic processes and manual maintenance tasks. He will also discuss the general approach to solving the problem and drill down to the code responsible for implementation.
Speaker: Aaron Morton, Apache Cassandra Committer
Aaron Morton is a Freelance Developer based in New Zealand, and a Committer on the Apache Cassandra project. In 2010 he gave up the RDBMS world for the scale and reliability of Cassandra. He now spends his time advancing the Cassandra project and helping others get the best out of it.
This talk is about Taskerman, a distributed cluster task manager built on top of AWS SQS, Zookeeper and Yelp PaaSTA. The talk was given at Imperial College, London as part of its 'Application of Computing in Industry' series: http://www.imperial.ac.uk/computing/industry/aci/yelp/
Cassandra Backups and Restorations Using Ansible (Joshua Wickman, Knewton) | ...DataStax
A solid backup strategy is a DBA's bread and butter. Cassandra's nodetool snapshot makes it easy to back up the SSTable files, but there remains the question of where to put them and how. Knewton's backup strategy uses Ansible for distributed backups and stores them in S3.
Unfortunately, it's all too easy to store backups that are essentially useless due to the absence of a coherent restoration strategy. This problem proved much more difficult and nuanced than taking the backups themselves. I will discuss Knewton's restoration strategy, which again leverages Ansible, yet I will focus on general principles and pitfalls to be avoided. In particular, restores necessitated modifying our backup strategy to generate cluster-wide metadata that is critical for a smooth automated restoration. Such pitfalls indicate that a restore-focused backup design leads to faster and more deterministic recovery.
About the Speaker
Joshua Wickman Database Engineer, Knewton
Dr. Joshua Wickman is currently part of the database team at Knewton, a NYC tech company focused on adaptive learning. He earned his PhD at the University of Delaware in 2012, where he studied particle physics models of the early universe. After a brief stint teaching college physics, he entered the New York tech industry in 2014 working with NoSQL, first with MongoDB and then Cassandra. He was certified in Cassandra at his first Cassandra Summit in 2015.
SSTable Reader Cassandra Day Denver 2014Ben Vanberg
The document describes how the speaker's company built a splittable input format for reading Cassandra SSTables directly in Hadoop MapReduce jobs. This allowed them to perform analytics on Cassandra data more efficiently by processing SSTables in parallel across multiple nodes. It reduced their processing time from 3-10 days to 10 hours and lowered costs by 94%. They open sourced the project to help others analyze Cassandra data using Hadoop.
Cassandra Summit 2014: Cassandra at Instagram 2014DataStax Academy
Presenter: Rick Branson, Infrastructure Engineer at Instagram
As Instagram has scaled to over 200 million users, so has our use of Cassandra. We've built new features and rebuilt old on Cassandra, and it's become an extremely mission-critical foundation of our production infrastructure. Rick will deliver a refresh of our use cases and go deep on the technical challenges we faced during our expansion.
Instaclustr Webinar 50,000 Transactions Per Second with Apache Spark on Apach...Instaclustr
This document describes Instaclustr's implementation of using Apache Spark on Apache Cassandra to monitor over 600 servers running Cassandra and collect metrics over time for tuning, alerting, and automated response systems. Key aspects of the implementation include writing data in 5 minute buckets to Cassandra, using Spark to efficiently roll up the raw data into aggregated metrics on those time intervals, and presenting the data. Optimizations that improved performance included upgrading Cassandra version and leveraging its built-in aggregates in Spark, reducing roll-up job times by 50%.
C* Summit 2013: Cassandra at Instagram by Rick BransonDataStax Academy
Speaker: Rick Branson, Infrastructure Engineer at Instagram
Cassandra is a critical part of Instagram's large scale site infrastructure that supports more than 100 million active users. This talk is a practical deep dive into data models, systems architecture, and challenges encountered during the implementation process.
This document discusses how to monitor Apache Cassandra clusters in real-time using Riemann. It describes how to set up Riemann to receive metrics collected by Cassandra and generate events. Metrics like read/write latencies, request counts, JVM stats and more can be monitored. Events are sent to Riemann which processes them through streams of filters and sends alerts. The metrics can be visualized in real-time graphs in Riemann's web dashboard. Thresholds can also trigger alerts when breached.
Slides from #PromCon2018 Munich.
https://promcon.io/2018-munich/talks/thanos-prometheus-at-scale/
Bartłomiej Płotka
Fabian Reinartz
The Prometheus Monitoring system has been thriving for several years. Along with its powerful data model, operational simplicity and reliability have been a key factor in its success. However, some questions were still largely unaddressed to this day. How can we store historical data at the order of petabytes in a reliable and cost-efficient way? Can we do so without sacrificing responsive query times? And what about a global view of all our metrics and transparent handling of HA setups?
Thanos takes Prometheus' strong foundations and extends it into a clustered, yet coordination free, globally scalable metric system. It retains Prometheus's simple operational model and even simplifies deployments further. Under the hood, Thanos uses highly cost-efficient object storage that's available in virtually all environments today. By building directly on top of the storage format introduced with Prometheus 2.0, Thanos achieves near real-time responsiveness even for cold queries against historical data. All while having virtually no cost overhead beyond that of the underlying object storage.
We will show the theoretical concepts behind Thanos and demonstrate how it seamlessly integrates into existing Prometheus setups.
OpenTSDB is used at Criteo for monitoring their large Hadoop infrastructure which includes over 2500 servers running many different services like HDFS, YARN, HBase, Kafka, and Storm. OpenTSDB was chosen because it can handle the scale of metrics collected, store metrics for long periods of time with fine-grained resolution, and is easily extensible to add new metrics. It uses HBase for storage which is optimized for the time series data stored in OpenTSDB and can scale to meet Criteo's needs of storing billions of data points and handling high query loads.
Speakers: Chris Larsen (Limelight Networks) and Benoit Sigoure (Arista Networks)
The OpenTSDB community continues to grow and with users looking to store massive amounts of time-series data in a scalable manner. In this talk, we will discuss a number of use cases and best practices around naming schemas and HBase configuration. We will also review OpenTSDB 2.0's new features, including the HTTP API, plugins, annotations, millisecond support, and metadata, as well as what's next in the roadmap.
Openstack on Fedora, Fedora on Openstack: An Introduction to cloud IaaSSadique Puthen
Openstack is an open source cloud operating system that provides infrastructure as a service capabilities. It includes components for compute (Nova), storage (Cinder, Swift, Manila), networking (Neutron), orchestration (Heat), metering (Ceilometer), and dashboard (Horizon). The document discusses these components in depth and how they provide infrastructure services. It also covers deployment options like Packstack, TripleO, and Ironic as well as other Openstack projects. The presentation introduces Openstack and its capabilities and components.
The Cassandra architecture shines at ensuring a very high availability of data even while nodes are failing or are overloaded. On the other hand, query latency will often rise during these events, especially on the higher percentiles. Many improvements have been made to reduce this effect over the past years. This talk will focus on one in particular: Speculative Retries. Introduced in Cassandra 2.0 on the server side and in the Java Driver 3.0 on the client side, this strategy remains complex to fully understand and to finely tune. This talk will deep dive into theoretical and practical aspects of Speculative Retries, showing the effect of tuning strategies with ad-hoc benchmarks.
About the Speakers
Michael Figuiere Cloud Platform Engineer, Netflix
Michael is a senior software engineer at Netflix where he works on improving the cloud storage infrastructure. He previously worked at Apple and DataStax where he worked for several years on creating Drivers and Developer Tools for Cassandra. At ease with both enterprise applications and lower level technologies, he specializes in distributed architectures and topics such as databases, search engines, and cloud.
Minh Do Senior Distributed Engineer, Netflix
Minh Do has been working at Netflix for the last several years to run, patch, and troubleshoot Cassandra on both server and client sides, and is also a co-creator of Dynomite project. Prior to Netflix, at Tango, he spearheaded its Big Data pipeline system from the ground using Spark/Hadoop. Before that, at Qualys, he built a distributed queue system that bridges traffics between all major components. He has passion in distributed system, machine learning/deep learning, and data storages.
Cassandra Summit 2014: Reading Cassandra SSTables Directly for Offline Data A...DataStax Academy
Presenter: Ben Vanberg, Senior Software Engineer at FullContact
Here at FullContact we have lots and lots of contact data. In particular we have more than a billion profiles over which we would like to perform ad hoc data analysis. Much of this data resides in Cassandra, and we have many analytics MapReduce jobs that require us to iterate across terabytes of Cassandra data. To solve this problem we've implemented our own splittable input format which allows us to quickly process large SSTables for downstream analytics.
Optimizing Your Cluster with Coordinator Nodes (Eric Lubow, SimpleReach) | Ca...DataStax
With the addition of vnodes (Virtual Nodes), Cassandra users were able to gain a few benefits as a result of streaming when it came to bootstrapping and decommissioning nodes. On the flip side, having to route requests on larger clusters became a lot more intensive of a workload for all nodes that were then forced to act coordinator nodes. By setting up a tier of proxy nodes, we were able to have our cluster of 50 nodes perform with a 300% improvement on average in a mixed workload environment. This is an explanation of what we did, how we did it, and why it works.
About the Speaker
Eric Lubow CTO, SimpleReach
Eric Lubow is CTO of SimpleReach, where he builds highly-scalable distributed systems for processing analytics data. Eric is also a DataStax MVP for Cassandra, and co-author of Practical Cassandra. In his spare time, Eric is a skydiver, motorcycle rider, mixed martial artist, and dog dad.
The document discusses managing logs for Black Friday in Elasticsearch. It covers the Elastic Stack components including Beats, Logstash, Elasticsearch and Kibana. It then discusses monitoring architectures, techniques for optimally sizing Elasticsearch clusters and shards, optimizing bulk indexing size, and distributing load across nodes. The presentation aims to provide guidance on log management strategies for handling high volume traffic periods like Black Friday.
Orchestrating Cassandra with Kubernetes: Challenges and OpportunitiesRaghavendra Prabhu
This is a talk about orchestration of Cassandra with cassandra operator, kubernetes and Yelp PaaSTA (https://github.com/Yelp/paasta).
The talk was presented at Computer Laboratory, University of Cambridge as part of the Engineering, Science and Technology Event (https://www.careers.cam.ac.uk/recruiting/event2Tech.asp) in November 2019.
This summary provides an overview of the key points from the document in 3 sentences:
The document outlines the agenda for Season 3 Episode 1 of the Netflix OSS podcast, which includes lightning talks on 8 new projects including Atlas, Prana, Raigad, Genie 2, Inviso, Dynomite, Nicobar, and MSL. Representatives from Netflix, IBM Watson, Nike Digital, and Pivotal then each provide a 3-5 minute presentation on their featured project. The presentations describe the motivation, features and benefits of each project for observability, integration with the Netflix ecosystem, automation of Elasticsearch deployments, job scheduling, dynamic scripting for Java, message security, and developing microservices
Eric Lubow discussed the challenges his company SimpleReach faced when using Cassandra counters at scale to track metrics for their content analytics platform. SimpleReach processes huge volumes of data, writing 250,000-300,000 counter values per second. Early on they encountered performance issues with counters but were able to optimize their implementation through techniques like pre-aggregating writes, using counter batches, extensive monitoring, upgrading hardware, and tuning Cassandra and JVM settings. Upgrading to Cassandra 2.1 also helped improve counter performance.
Cassandra Community Webinar: Apache Cassandra InternalsDataStax
Apache Cassandra solves many interesting problems to provide a scalable, distributed, fault tolerant database. Cluster wide operations track node membership, direct requests and implement consistency guarantees. At the node level, the Log Structured storage engine provides high performance reads and writes. All of this is implemented in a Java code base that has greatly matured over the past few years.
In this webinar Aaron Morton will step through read and write requests, automatic processes and manual maintenance tasks. He will also discuss the general approach to solving the problem and drill down to the code responsible for implementation.
Speaker: Aaron Morton, Apache Cassandra Committer
Aaron Morton is a Freelance Developer based in New Zealand, and a Committer on the Apache Cassandra project. In 2010 he gave up the RDBMS world for the scale and reliability of Cassandra. He now spends his time advancing the Cassandra project and helping others get the best out of it.
This talk is about Taskerman, a distributed cluster task manager built on top of AWS SQS, Zookeeper and Yelp PaaSTA. The talk was given at Imperial College, London as part of its 'Application of Computing in Industry' series: http://www.imperial.ac.uk/computing/industry/aci/yelp/
Cassandra Backups and Restorations Using Ansible (Joshua Wickman, Knewton) | ...DataStax
A solid backup strategy is a DBA's bread and butter. Cassandra's nodetool snapshot makes it easy to back up the SSTable files, but there remains the question of where to put them and how. Knewton's backup strategy uses Ansible for distributed backups and stores them in S3.
Unfortunately, it's all too easy to store backups that are essentially useless due to the absence of a coherent restoration strategy. This problem proved much more difficult and nuanced than taking the backups themselves. I will discuss Knewton's restoration strategy, which again leverages Ansible, yet I will focus on general principles and pitfalls to be avoided. In particular, restores necessitated modifying our backup strategy to generate cluster-wide metadata that is critical for a smooth automated restoration. Such pitfalls indicate that a restore-focused backup design leads to faster and more deterministic recovery.
About the Speaker
Joshua Wickman Database Engineer, Knewton
Dr. Joshua Wickman is currently part of the database team at Knewton, a NYC tech company focused on adaptive learning. He earned his PhD at the University of Delaware in 2012, where he studied particle physics models of the early universe. After a brief stint teaching college physics, he entered the New York tech industry in 2014 working with NoSQL, first with MongoDB and then Cassandra. He was certified in Cassandra at his first Cassandra Summit in 2015.
SSTable Reader Cassandra Day Denver 2014Ben Vanberg
The document describes how the speaker's company built a splittable input format for reading Cassandra SSTables directly in Hadoop MapReduce jobs. This allowed them to perform analytics on Cassandra data more efficiently by processing SSTables in parallel across multiple nodes. It reduced their processing time from 3-10 days to 10 hours and lowered costs by 94%. They open sourced the project to help others analyze Cassandra data using Hadoop.
Cassandra Summit 2014: Cassandra at Instagram 2014DataStax Academy
Presenter: Rick Branson, Infrastructure Engineer at Instagram
As Instagram has scaled to over 200 million users, so has our use of Cassandra. We've built new features and rebuilt old on Cassandra, and it's become an extremely mission-critical foundation of our production infrastructure. Rick will deliver a refresh of our use cases and go deep on the technical challenges we faced during our expansion.
Instaclustr Webinar 50,000 Transactions Per Second with Apache Spark on Apach...Instaclustr
This document describes Instaclustr's implementation of using Apache Spark on Apache Cassandra to monitor over 600 servers running Cassandra and collect metrics over time for tuning, alerting, and automated response systems. Key aspects of the implementation include writing data in 5 minute buckets to Cassandra, using Spark to efficiently roll up the raw data into aggregated metrics on those time intervals, and presenting the data. Optimizations that improved performance included upgrading Cassandra version and leveraging its built-in aggregates in Spark, reducing roll-up job times by 50%.
C* Summit 2013: Cassandra at Instagram by Rick BransonDataStax Academy
Speaker: Rick Branson, Infrastructure Engineer at Instagram
Cassandra is a critical part of Instagram's large scale site infrastructure that supports more than 100 million active users. This talk is a practical deep dive into data models, systems architecture, and challenges encountered during the implementation process.
This document discusses how to monitor Apache Cassandra clusters in real-time using Riemann. It describes how to set up Riemann to receive metrics collected by Cassandra and generate events. Metrics like read/write latencies, request counts, JVM stats and more can be monitored. Events are sent to Riemann which processes them through streams of filters and sends alerts. The metrics can be visualized in real-time graphs in Riemann's web dashboard. Thresholds can also trigger alerts when breached.
Slides from #PromCon2018 Munich.
https://promcon.io/2018-munich/talks/thanos-prometheus-at-scale/
Bartłomiej Płotka
Fabian Reinartz
The Prometheus Monitoring system has been thriving for several years. Along with its powerful data model, operational simplicity and reliability have been a key factor in its success. However, some questions were still largely unaddressed to this day. How can we store historical data at the order of petabytes in a reliable and cost-efficient way? Can we do so without sacrificing responsive query times? And what about a global view of all our metrics and transparent handling of HA setups?
Thanos takes Prometheus' strong foundations and extends it into a clustered, yet coordination free, globally scalable metric system. It retains Prometheus's simple operational model and even simplifies deployments further. Under the hood, Thanos uses highly cost-efficient object storage that's available in virtually all environments today. By building directly on top of the storage format introduced with Prometheus 2.0, Thanos achieves near real-time responsiveness even for cold queries against historical data. All while having virtually no cost overhead beyond that of the underlying object storage.
We will show the theoretical concepts behind Thanos and demonstrate how it seamlessly integrates into existing Prometheus setups.
OpenTSDB is used at Criteo for monitoring their large Hadoop infrastructure which includes over 2500 servers running many different services like HDFS, YARN, HBase, Kafka, and Storm. OpenTSDB was chosen because it can handle the scale of metrics collected, store metrics for long periods of time with fine-grained resolution, and is easily extensible to add new metrics. It uses HBase for storage which is optimized for the time series data stored in OpenTSDB and can scale to meet Criteo's needs of storing billions of data points and handling high query loads.
Speakers: Chris Larsen (Limelight Networks) and Benoit Sigoure (Arista Networks)
The OpenTSDB community continues to grow and with users looking to store massive amounts of time-series data in a scalable manner. In this talk, we will discuss a number of use cases and best practices around naming schemas and HBase configuration. We will also review OpenTSDB 2.0's new features, including the HTTP API, plugins, annotations, millisecond support, and metadata, as well as what's next in the roadmap.
Openstack on Fedora, Fedora on Openstack: An Introduction to cloud IaaSSadique Puthen
Openstack is an open source cloud operating system that provides infrastructure as a service capabilities. It includes components for compute (Nova), storage (Cinder, Swift, Manila), networking (Neutron), orchestration (Heat), metering (Ceilometer), and dashboard (Horizon). The document discusses these components in depth and how they provide infrastructure services. It also covers deployment options like Packstack, TripleO, and Ironic as well as other Openstack projects. The presentation introduces Openstack and its capabilities and components.
The Cassandra architecture shines at ensuring a very high availability of data even while nodes are failing or are overloaded. On the other hand, query latency will often rise during these events, especially on the higher percentiles. Many improvements have been made to reduce this effect over the past years. This talk will focus on one in particular: Speculative Retries. Introduced in Cassandra 2.0 on the server side and in the Java Driver 3.0 on the client side, this strategy remains complex to fully understand and to finely tune. This talk will deep dive into theoretical and practical aspects of Speculative Retries, showing the effect of tuning strategies with ad-hoc benchmarks.
About the Speakers
Michael Figuiere Cloud Platform Engineer, Netflix
Michael is a senior software engineer at Netflix where he works on improving the cloud storage infrastructure. He previously worked at Apple and DataStax where he worked for several years on creating Drivers and Developer Tools for Cassandra. At ease with both enterprise applications and lower level technologies, he specializes in distributed architectures and topics such as databases, search engines, and cloud.
Minh Do Senior Distributed Engineer, Netflix
Minh Do has been working at Netflix for the last several years to run, patch, and troubleshoot Cassandra on both server and client sides, and is also a co-creator of Dynomite project. Prior to Netflix, at Tango, he spearheaded its Big Data pipeline system from the ground using Spark/Hadoop. Before that, at Qualys, he built a distributed queue system that bridges traffics between all major components. He has passion in distributed system, machine learning/deep learning, and data storages.
Cassandra Summit 2014: Reading Cassandra SSTables Directly for Offline Data A...DataStax Academy
Presenter: Ben Vanberg, Senior Software Engineer at FullContact
Here at FullContact we have lots and lots of contact data. In particular we have more than a billion profiles over which we would like to perform ad hoc data analysis. Much of this data resides in Cassandra, and we have many analytics MapReduce jobs that require us to iterate across terabytes of Cassandra data. To solve this problem we've implemented our own splittable input format which allows us to quickly process large SSTables for downstream analytics.
Optimizing Your Cluster with Coordinator Nodes (Eric Lubow, SimpleReach) | Ca...DataStax
With the addition of vnodes (Virtual Nodes), Cassandra users were able to gain a few benefits as a result of streaming when it came to bootstrapping and decommissioning nodes. On the flip side, having to route requests on larger clusters became a lot more intensive of a workload for all nodes that were then forced to act coordinator nodes. By setting up a tier of proxy nodes, we were able to have our cluster of 50 nodes perform with a 300% improvement on average in a mixed workload environment. This is an explanation of what we did, how we did it, and why it works.
About the Speaker
Eric Lubow CTO, SimpleReach
Eric Lubow is CTO of SimpleReach, where he builds highly-scalable distributed systems for processing analytics data. Eric is also a DataStax MVP for Cassandra, and co-author of Practical Cassandra. In his spare time, Eric is a skydiver, motorcycle rider, mixed martial artist, and dog dad.
The document discusses managing logs for Black Friday in Elasticsearch. It covers the Elastic Stack components including Beats, Logstash, Elasticsearch and Kibana. It then discusses monitoring architectures, techniques for optimally sizing Elasticsearch clusters and shards, optimizing bulk indexing size, and distributing load across nodes. The presentation aims to provide guidance on log management strategies for handling high volume traffic periods like Black Friday.
Orchestrating Cassandra with Kubernetes: Challenges and OpportunitiesRaghavendra Prabhu
This is a talk about orchestration of Cassandra with cassandra operator, kubernetes and Yelp PaaSTA (https://github.com/Yelp/paasta).
The talk was presented at Computer Laboratory, University of Cambridge as part of the Engineering, Science and Technology Event (https://www.careers.cam.ac.uk/recruiting/event2Tech.asp) in November 2019.
This summary provides an overview of the key points from the document in 3 sentences:
The document outlines the agenda for Season 3 Episode 1 of the Netflix OSS podcast, which includes lightning talks on 8 new projects including Atlas, Prana, Raigad, Genie 2, Inviso, Dynomite, Nicobar, and MSL. Representatives from Netflix, IBM Watson, Nike Digital, and Pivotal then each provide a 3-5 minute presentation on their featured project. The presentations describe the motivation, features and benefits of each project for observability, integration with the Netflix ecosystem, automation of Elasticsearch deployments, job scheduling, dynamic scripting for Java, message security, and developing microservices
This document summarizes a presentation about monitoring Cassandra systems. It discusses gathering metrics from Cassandra using JMX and nodetool, including thread pool statistics, latency histograms, and metric types. It also provides an overview of the Cassandra read/write process involving memtables and SSTables.
Orchestrating Cassandra with Kubernetes Operator and PaaSTARaghavendra Prabhu
Video URL: https://youtu.be/GjI6MUz7AyE
This is the slide deck of the Percona Live Online 2020 talk given by me in May 2020: https://www.percona.com/resources/videos/orchestrating-cassandra-kubernetes-operator-and-yelp-paasta-percona-live-online
The talk delves into the architecture of our Cassandra Kubernetes Operator and the multi-region multi-AZ clusters it manages, and strategies we have in place for safe rollouts and zero-downtime migration.
Scala like distributed collections - dumping time-series data with apache sparkDemi Ben-Ari
Spark RDDs are almost identical to Scala collection, just in a distributed manner, all of the transformations and actions are derived from the Scala collections API.
As Martin Odersky mentioned, “Spark - The Ultimate Scala Collections” is the right way to look at RDDs. But with that great distributed power comes a great many data problems: at first you’ll start tackling the concept of partitioning, then the actual data becomes the next thing to worry about.
In the talk we’ll go through an overview on Spark's architecture, and see how similar RDDs are to the Scala collections API. We'll then shift to the world of problems that you’ll be facing when using Spark for processing a vast volume of time-series data with multiple data stores (S3, MongoDB, Apache Cassandra, MySQL).
When you start tackling many scale and performance problems, many questions arise:
> How to handle missing data?
> Should the system handle both serving and backend processes, or should we separate them out?
> Which solution is cheaper?
> How do we get the best performance for money spent?
In the talk we will tell the tale of all of the transformations we’ve made to our data and review the multiple data persistency layers... and I’ll try my best NOT to answer the question “which persistency layer is the best?” but I do promise to share our pains and lessons learned!
Presenter: Chris Lohfink, Engineer at Pythian
This session will cover a walk-through to provide an understanding of key metrics critical to operating a Cassandra cluster effectively. Without context to the metrics, we just have pretty graphs. With context, we have a powerful tool to determine problems before they happen and to debug production issues more quickly.
Towards a ZooKeeper-less Pulsar, etcd, etcd, etcd. - Pulsar Summit SF 2022StreamNative
This document summarizes Matteo Merli's talk on moving Apache Pulsar to a ZooKeeper-less metadata model. It discusses how Pulsar currently uses ZooKeeper for metadata storage but faces scalability issues. The talk outlines PIP-45, a plan to introduce a pluggable metadata backend into Pulsar to replace the direct ZooKeeper usage. This would allow alternative storage options like Etcd and improve scalability. It also discusses successes already achieved in Pulsar 2.10 by abstracting the metadata access and future goals around scaling to support millions of topics.
This summary provides an overview of the lightning talks presented at the NetflixOSS Open House:
- Jordan Zimmerman from Netflix presented on several NetflixOSS projects he works on including Curator, a Java library that makes using ZooKeeper easier, and Blitz4j, an asynchronous logging library that improves performance over Log4j.
- Additional talks covered Eureka, a REST service for discovering middle-tier services; Ribbon for load balancing between middle-tier instances; Archaius for dynamic configuration; Astyanax for interacting with Cassandra; and various other NetflixOSS projects.
- The talks highlighted the motivation for these projects including addressing challenges of scaling for Netflix's large data
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Ben...Codemotion Tel Aviv
Demi Ben-Ari gave a presentation about dumping time series data using Apache Spark. The presentation covered an overview of Spark, the structure of maritime location and metadata being collected over time, and the initial challenges of missing data and late arrivals causing holes in the data. It described the evolution of solutions from MongoDB to Cassandra and optimizations made to improve write speeds from 40 minutes to under 5 minutes. The fastest solution was to write the raw data to S3 and have a separate process aggregate it into Cassandra for serving.
This document discusses optimizing performance for high-load projects. It summarizes the delivery loads and technologies used for several projects including mGage, mobclix and XXXX. It then discusses optimizations made to improve performance, including using Solr for search, Redis for real-time data, Hadoop for reporting, and various Java optimizations in moving to Java 7. Specific optimizations discussed include reducing garbage collection, improving random number generation, and minimizing I/O operations.
Large volume data analysis on the Typesafe Reactive Platform - Big Data Scala...Martin Zapletal
The document discusses distributed machine learning and data processing. It covers several topics including reasons for using distributed machine learning, different distributed computing architectures and primitives, distributed data stores and analytics tools like Spark, streaming architectures like Lambda and Kappa, and challenges around distributed state management and fault tolerance. It provides examples of failures in distributed databases and suggestions to choose the appropriate tools based on the use case and understand their internals.
S3, Cassandra or Outer Space? Dumping Time Series Data using Spark - Demi Be...Codemotion
The document discusses time series data processing using Apache Spark. It begins with an introduction of the speaker and an overview of Spark. It then describes the structure and flow of maritime location data. The challenges of processing this streaming time series data are discussed, including delays in data arrival that can cause holes in computations. The document outlines the evolution of their solution from using MongoDB to using Apache Spark with Cassandra and S3 for improved performance and scalability. It concludes with lessons about understanding data characteristics and choosing the right persistence layers.
Recently, the interest in highly scalable stream processing engines has risen, thus many projects have appeared. Apache Samza is a distributed stream-processing framework that uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, and resource management. It is one of the most popular stream processing engines out there used by many high-profile companies. On the other hand, we have Amazon Kinesis that is a fully managed service for real-time processing of streaming data which allows users to scale the amount of data ingested by Kinesis without worrying about the infrastructure details. This presentation gives a brief introduction about the very popular Samza-Kafka integration, then focuses on the new Samza-Kinesis integration, and explains users the new opportunities they have due to the new Samza-Kinesis integration.
Scalable complex event processing on samza @UBERShuyi Chen
The Marketplace data team at Uber has built a scalable complex event processing platform to solve many challenging real time data needs for various Uber products. This platform has been in production for almost a year and it has proven to be very flexible to solve many use cases. In this talk, we will share in detail the design and architecture of the platform, and how we employ Samza, Kafka, and Siddhi at scale.
This slides was presented at Stream Processing Meetup @ LinkedIn on June 15 2016.
Clusternaut: Orchestrating Percona XtraDB Cluster with Kubernetes.Raghavendra Prabhu
The talk presented at MySQL & Friends devroom at FOSDEM 2016 in Brussels: https://fosdem.org/2016/schedule/event/clusternaut/
Devroom: https://fosdem.org/2016/schedule/track/mysql_and_friends/
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kevin Lynch
In this presentation I talk about our motivation to converting our microservices to run on Kubernetes. I discuss many of the technical challenges we encountered along the way, including networking issues, Java issues, monitoring and alerting, and managing all of our resources!
Cassandra Tools and Distributed Administration (Jeffrey Berger, Knewton) | C*...DataStax
At Knewton we operate across five different VPCs a total of 29 clusters, each ranging from 3 nodes to 24 nodes. For a team of three to maintain this is not herculean, however good tools to diagnose issues and gather information in a distributed manner are vital to moving quickly and minimizing engineering time spent.
The database team at Knewton has been successfully using a combination of Ansible and custom open sourced tools to maintain and improve the Cassandra deployment at Knewton. I will be talking about several of these tools and giving examples of how we are using them. Specifically I will discuss the cassandra-tracing tool, which analyzes the contents of the system_traces keyspace, and the cassandra-stat tool, which gives real-time output of the operations of a cassandra cluster. Distributed administration with ad-hoc Ansible will also be covered and I will walk through examples of using these commands to identify and remediate clusterwide issues.
About the Speaker
Jeffrey Berger Lead Database Engineer, Knewton
Dr. Jeffrey Berger is currently the lead database engineer at Knewton, an education tech startup in NYC. He joined the tech scene in NYC in 2013 and spent two years working with MongoDB, becoming a certified MongoDB administrator and a MongoDB Master. He received his Cassandra Administrator certification at Cassandra Summit 2015. He holds a Ph.D. in Theoretical Physics from Penn State and spent several years working on high energy nuclear interactions.
Data processing platforms architectures with Spark, Mesos, Akka, Cassandra an...Anton Kirillov
This talk is about architecture designs for data processing platforms based on SMACK stack which stands for Spark, Mesos, Akka, Cassandra and Kafka. The main topics of the talk are:
- SMACK stack overview
- storage layer layout
- fixing NoSQL limitations (joins and group by)
- cluster resource management and dynamic allocation
- reliable scheduling and execution at scale
- different options for getting the data into your system
- preparing for failures with proper backup and patching strategies
Similar to Taskerman: A Distributed Cluster Task Manager (20)
This talk was given at Cassandra London meetup: https://www.meetup.com/Cassandra-London/events/267271963/ . The talk is about orchestration of Cassandra with our Kubernetes Operator and Yelp PaaSTA. We also outline some of the opportunities and challenges associated with this architecture.
Youtube link: https://www.youtube.com/watch?v=JqAILFkkibA
This talk is about orchestration of Cassandra on Kubernetes with Cassandra Operator and Yelp's Platform-as-a-Service: PaaSTA. The talk focusses specifically on the internals of cassandra operator and its core reconcile loop for reconciliation of cluster state and on-disk configuration.
This is a talk about safe and high velocity automation on AWS (Amazon Web Services) with AWS Systems Manager, and is applicable for use cases such as reliability engineering and deployment automation.
Talk given on state of NUMA with Java databases such as Cassandra and how it can improved / ameliorated, and compared with traditional storage engines.
Clusternaut: Orchestrating Percona XtraDB Cluster with KubernetesRaghavendra Prabhu
Raghavendra Prabhu presented on orchestrating Percona XtraDB Cluster (PXC) with Kubernetes. Some key points:
- Kubernetes provides horizontal scaling, self-healing, automated rollouts/rollbacks, service discovery, storage orchestration and more.
- In Kubernetes, PXC nodes would be deployed as pods with a replication controller to maintain a set number of pods. Services provide load balancing to the pods.
- Demonstrated deploying a basic PXC cluster on Kubernetes, including creating a network, cluster, service, replicating pods from a template, and exposing ports.
- Challenges include load balancing for state transfers between nodes and ensuring nodes are
Gone are those days when companies used to be strictly colocated in a single office. Distributed workplaces are gradually becoming the norm than an exception. So, it is essential that we talk more about it and discuss it.
So, this talk is essentially about:
a) Productivity and working from home.
b) Scheduling flexibility.
c) Challenges in communication and ways to overcome them.
d) Ways of getting such a job and Open Source.
e) Measuring work and micro-management
f) Feeling of detachment and workarounds for it.
To sum up, I will make this talk a very informative and entertaining one, as a lightning talk ought to be.
Securing databases with systemd for containers and services Raghavendra Prabhu
Data is the most valuable entity associated with a system, particularly when it is a sensitive one. Not only are there threats associated with physical access
to the box, but also ones where logical access suffices - sql injections etc.
Vulnerabilities like shellshock and heartbleed have also shown that an exploit in one component can also be used to access others through buffer overflows, memory overruns etc. and/or impact the immunity of system severely.
This is where "Principle of least privilege" comes into play. Wikipedia defines it as "a particular abstraction layer of a computing environment, every module (such as a process, a user or a program depending on the subject) must be able to access only the information and resources that are necessary for its legitimate purpose".
Dock'em: Distributed Systems Testing with NetEm and Docker Raghavendra Prabhu
This talk is about distributed systems testing of Galera with NetEm and Docker!
Video of the talk: https://www.youtube.com/watch?v=YBuuvhSO38s&list=PLctlsn9Gs8wbx47tuhxuNytdrsDf_LWI2&index=1
Playlist: https://www.youtube.com/playlist?list=PLctlsn9Gs8wbx47tuhxuNytdrsDf_LWI2
Galera with Docker: How Synchronous Replication and Linux Containers mesh tog...Raghavendra Prabhu
How Galera (Synchronous replication plugin for Percona XtraDB Cluster) can be used with Docker (or linux containers in general) to 'mesh' well.
Video of the talk: https://www.youtube.com/watch?v=3A8EF549Q3Y&list=PLctlsn9Gs8wbx47tuhxuNytdrsDf_LWI2&index=2
Playlist: http://www.youtube.com/playlist?list=PLctlsn9Gs8wbx47tuhxuNytdrsDf_LWI2
Jutsu or Dô: Open documentation: continuous process than a body Raghavendra Prabhu
The document discusses various factors to consider for effective documentation of open source projects. It emphasizes that lucid documentation can help with rapid community growth, attracting more contributors, enhancing code quality, and aiding bug fixes. Conversely, poor documentation can repel users, lead to less understood code, slow project growth, and cause spurious bug reports. Some highlighted factors include keeping documentation up-to-date, using version control, integrating feedback, examples to aid learning, and considering different user types like end users, developers and architects.
Corpus collapsum: Partition tolerance of Galera in a noisy high load environmentRaghavendra Prabhu
This is the talk given at Highload++ 2014 in Moscow, Russia. The topic was partition tolerance testing of Galera in a noisy high load environment with NetEm and Docker.
Corpus collapsum: Partition tolerance of Galera put to testRaghavendra Prabhu
This is the talk given at RICON 2014 (ricon.io) on partition tolerance testing of Galera with docker and netem.
Video: https://www.youtube.com/watch?v=xRD6A8TY_Uw
Link to the talk: http://ricon.io/event-details/index.html#corpus-collapsum
Acidic clusters - Review of contemporary ACID-compliant databases with synchr...Raghavendra Prabhu
This talk reviews database clusters of our time which employ synchronous replication while being ACID compliant. ACID compliance implies ability to support transactions across nodes. As part of this talk, PXC (Percona XtraDB Cluster)/Galera, Google F1 based on Spanner/CFS and MySQL Cluster will be considered. Primary objective here is to expound features of
each in order to highlight differentiating factors and commonality between them.
Running virtualized Galera instances for fun and profitRaghavendra Prabhu
The document discusses running virtualized Galera instances for high availability and discusses how Galera and virtualization can work together. It covers how Galera works with synchronous replication, popular virtualization solutions like KVM and containers, deploying Galera in virtualized environments including initialization, operations, storage, and networking considerations, and concludes by taking questions.
ACIDic Clusters: Review of current relation databases with synchronous replic...Raghavendra Prabhu
These are the slides from the talk given at Percona Live 2014 MySQL Conference and Expo (PLMCE): http://www.percona.com/live/mysql-conference-2014/sessions/acidic-clusters-review-current-relational-databases-synchronous-replication
Percona XtraDB Cluster before every release: Glimpse into CI testingRaghavendra Prabhu
This document discusses the continuous integration testing process used by Percona for releases of Percona XtraDB Cluster (PXC). It describes how Jenkins is used to automatically run a suite of tests on multiple platforms after every code change, including unit, performance, replication, and end-to-end clustering tests. These automated tests help find bugs early and ensure PXC works as intended as a clustered database system before each release. The document also outlines areas for further improving the testing approach over time.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...XfilesPro
Wondering how X-Sign gained popularity in a quick time span? This eSign functionality of XfilesPro DocuPrime has many advancements to offer for Salesforce users. Explore them now!
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
When it is all about ERP solutions, companies typically meet their needs with common ERP solutions like SAP, Oracle, and Microsoft Dynamics. These big players have demonstrated that ERP systems can be either simple or highly comprehensive. This remains true today, but there are new factors to consider, including a promising new contender in the market that’s Odoo. This blog compares Odoo ERP with traditional ERP systems and explains why many companies now see Odoo ERP as the best choice.
What are ERP Systems?
An ERP, or Enterprise Resource Planning, system provides your company with valuable information to help you make better decisions and boost your ROI. You should choose an ERP system based on your company’s specific needs. For instance, if you run a manufacturing or retail business, you will need an ERP system that efficiently manages inventory. A consulting firm, on the other hand, would benefit from an ERP system that enhances daily operations. Similarly, eCommerce stores would select an ERP system tailored to their needs.
Because different businesses have different requirements, ERP system functionalities can vary. Among the various ERP systems available, Odoo ERP is considered one of the best in the ERp market with more than 12 million global users today.
Odoo is an open-source ERP system initially designed for small to medium-sized businesses but now suitable for a wide range of companies. Odoo offers a scalable and configurable point-of-sale management solution and allows you to create customised modules for specific industries. Odoo is gaining more popularity because it is built in a way that allows easy customisation, has a user-friendly interface, and is affordable. Here, you will cover the main differences and get to know why Odoo is gaining attention despite the many other ERP systems available in the market.
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
SMS API Integration in Saudi Arabia| Best SMS API ServiceYara Milbes
Discover the benefits and implementation of SMS API integration in the UAE and Middle East. This comprehensive guide covers the importance of SMS messaging APIs, the advantages of bulk SMS APIs, and real-world case studies. Learn how CEQUENS, a leader in communication solutions, can help your business enhance customer engagement and streamline operations with innovative CPaaS, reliable SMS APIs, and omnichannel solutions, including WhatsApp Business. Perfect for businesses seeking to optimize their communication strategies in the digital age.
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
6. ● Several TB in Cassandra clusters with tens of nodes each
● Close to a million messages/second in streaming pipeline
● Several TB in Elasticsearch with several hundred nodes in
each
● Many PB archived to S3 every month
● Multi-AZ Multi-Region
● And growing…
Distributed Systems
7.
8. “Need to run logical backup on a fleet without disruption
to ingress traffic”
“Run anti-entropy repair on Cassandra cluster without
spiking read latency”
“Reboot 1000 instances without taking a millennia but not
bringing down site either”
“Upgrade an Elasticsearch cluster from m3.medium to
m3.xlarge safely without downtime”
17. ● Schedulable
● Reusable
● Auditability
○ Not Ad-hoc
○ More Declarative, Less Imperative
■ Configuration Management
● Maintainability
● Observability
● Resilience
Desirable
18. ● Paramount*
● Serialized execution
○ ‘m’ out of ‘n’
○ Disjoint jobs.
● Avoid cascade
● Privilege escalation
● Pull-based
* Unless oncall is automated too.
Safety
19.
20.
21. ● Network is reliable
● Latency is zero
● Bandwidth is infinite
● Network is secure
● One administrator
● Transport cost is zero
● Network is homogenous
● Topology doesn't change
Fallacies of Distributed System
22. Quotes
There are 2 hard problems in computer science: cache
invalidation, naming things, and off-by-1 errors.
@secretGeek
There are only two hard problems in distributed
systems: 2. Exactly-once delivery 1. Guaranteed order
of messages 2. Exactly-once delivery @mathiasverraes
35. ● The executor of Taskerman
● Dequeue task and executes
○ Pre-defined reviewed code.
● Cron-ed on node
● Zookeeper for coordination
● Task deleted upon success
○ Crash safety
● Dead letter queue
TaskRunner
36. class TestTaskRunner(TaskRunner):
def __init__(self, task,..):
# State mgmt and datastore specific
def pre_check(self):
# Is the task safe to execute on this cluster
def execute_action(self):
# Actual execution of task:action
def post_check(self):
# cluster good after execution or is it on fire
46. ● Heartbeat ping
○ End-to-end monitoring
● Dead Letter Queue
○ Retry-based
○ Recycle bin of failed tasks.
○ Hooks into human side of
monitoring
● Status logging
Failure detection
47.
48. ● End-to-end logging
○ Un/structured
● Metrics
○ Counters
○ Queue lengths
● Aggregation and dashboards
● Staleness checks
● Dead Letter Queue
● Multi-modal Alerting
Monitoring
49. ● Restarts
● Reboots
● Scale Up
● Instance updates
● Kafka config reload
● Failure injection
● Backup and restore
● Search indexing
● .. and many more.
Use cases