Hw09 Monitoring Best Practices
•Download as PPT, PDF•
2 likes•907 views
The document discusses developing a comprehensive monitoring approach for Hadoop clusters. It recommends starting with basic monitoring of nodes using Nagios and Cacti for metrics like CPU usage, disk usage, and network traffic. It then suggests adding Hadoop-specific checks like monitoring DataNodes and graphing NameNode operations using JMX. Finally, it proposes setting alarms based on JMX metrics and regularly reviewing filesystem growth and utilization.
Report
Share
Report
Share
![Relevant Hadoop Information ,[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
PostgreSQL Terminology
In the “Sharing is caring” spirit, we came up with a series of internal talks called, By Showmaxers, for Showmaxers, and we recently started making them public. There are already talks about Networks, and Android app building, available.
Our latest talk focuses on PostgreSQL Terminology, and is led by Angus Dippenaar. He worked on Showmax projects from South Africa, and moved to work with us in Prague, Czech Republic.
The talk was meant to fill some holes in our knowledge of PostgreSQL. So, it guides you through the basic PostgreSQL terminology you need to understand when reading the official documentation and blogs.
You may learn what all these POstgreSQL terms mean:
Command, query, local or global object, non-schema local objects, relation, tablespace, database, database cluster, instance and its processes like postmaster or backend; session, connection, heap, file segment, table, TOAST, tuple, view, materialized (view), transaction, commit, rollback, index, write-ahead log, WAL record, WAL file, checkpoint, Multi-version concurrency control (MVCC), dead tuples (dead rows), or transaction exhaustion.
The terminology is followed by a demonstration of transaction exhaustion.
Get the complete explanation and see the demonstration of the transaction exhaustion and of tuple freezing in the talk on YouTube: https://youtu.be/E-RkI3Ws7gM.
Using Apache Spark to Solve Sessionization Problem in Batch and Streaming
This document discusses sessionization techniques using Apache Spark batch and streaming processing. It describes using Spark to join previous session data with new log data to generate user sessions in batch mode. For streaming, it covers using watermarks and stateful processing to continuously generate sessions from streaming data. Key aspects covered include checkpointing to provide fault tolerance, configuring the state store, and techniques for reprocessing data in batch and streaming contexts.
PGConf APAC 2018 - Patroni: Kubernetes-native PostgreSQL companion
Speaker: Alexander Kukushkin
Kubernetes is a solid leader among different cloud orchestration engines and its adoption rate is growing on a daily basis. Naturally people want to run both their applications and databases on the same infrastructure.
There are a lot of ways to deploy and run PostgreSQL on Kubernetes, but most of them are not cloud-native. Around one year ago Zalando started to run HA setup of PostgreSQL on Kubernetes managed by Patroni. Those experiments were quite successful and produced a Helm chart for Patroni. That chart was useful, albeit a single problem: Patroni depended on Etcd, ZooKeeper or Consul.
Few people look forward to deploy two applications instead of one and support them later on. In this talk I would like to introduce Kubernetes-native Patroni. I will explain how Patroni uses Kubernetes API to run a leader election and store the cluster state. I’m going to live-demo a deployment of HA PostgreSQL cluster on Minikube and share our own experience of running more than 130 clusters on Kubernetes.
Patroni is a Python open-source project developed by Zalando in cooperation with other contributors on GitHub: https://github.com/zalando/patroni
Sessionization with Spark streaming
The document discusses sessionization with Spark streaming to analyze user sessions from a constant stream of page visit data. Key points include:
- Streaming page visit data presents challenges like joining new visits to ongoing sessions and handling variable data volumes and long user sessions.
- The proposed solution uses Spark streaming to join a checkpoint of incomplete sessions with new visit data to calculate session metrics in real-time.
- Important aspects are controlling data ingress size and partitioning to optimize performance of operations like joins and using custom formats to handle output to multiple sinks.
Deep dive into PostgreSQL statistics.
This document discusses using PostgreSQL statistics to optimize performance. It describes various statistics sources like pg_stat_database, pg_stat_bgwriter, and pg_stat_replication that provide information on operations, caching, and replication lag. It also provides examples of using these sources to identify issues like long transactions, temporary file growth, and replication delays.
Move Over, Rsync
This document summarizes a presentation about the advantages of using ZFS for data replication compared to rsync. It provides several examples showing ZFS performing full dataset replications between 289x and 1,148x faster than rsync due to its use of copy-on-write snapshots and incremental block-level replication. It also briefly explains how copy-on-write snapshots and incremental replication work at a high-level in ZFS to provide such performance advantages over rsync. Additional context is provided about data services that offer ZFS-based replication for $60 or less per terabyte per month.
InfluxDB IOx Tech Talks: Intro to the InfluxDB IOx Read Buffer - A Read-Optim...
InfluxDB IOx Tech Talks: Intro to the InfluxDB IOx Read Buffer - A Read-Optimized In-Memory Query Execution Engine
Recommended
PostgreSQL Terminology
In the “Sharing is caring” spirit, we came up with a series of internal talks called, By Showmaxers, for Showmaxers, and we recently started making them public. There are already talks about Networks, and Android app building, available.
Our latest talk focuses on PostgreSQL Terminology, and is led by Angus Dippenaar. He worked on Showmax projects from South Africa, and moved to work with us in Prague, Czech Republic.
The talk was meant to fill some holes in our knowledge of PostgreSQL. So, it guides you through the basic PostgreSQL terminology you need to understand when reading the official documentation and blogs.
You may learn what all these POstgreSQL terms mean:
Command, query, local or global object, non-schema local objects, relation, tablespace, database, database cluster, instance and its processes like postmaster or backend; session, connection, heap, file segment, table, TOAST, tuple, view, materialized (view), transaction, commit, rollback, index, write-ahead log, WAL record, WAL file, checkpoint, Multi-version concurrency control (MVCC), dead tuples (dead rows), or transaction exhaustion.
The terminology is followed by a demonstration of transaction exhaustion.
Get the complete explanation and see the demonstration of the transaction exhaustion and of tuple freezing in the talk on YouTube: https://youtu.be/E-RkI3Ws7gM.
Using Apache Spark to Solve Sessionization Problem in Batch and Streaming
This document discusses sessionization techniques using Apache Spark batch and streaming processing. It describes using Spark to join previous session data with new log data to generate user sessions in batch mode. For streaming, it covers using watermarks and stateful processing to continuously generate sessions from streaming data. Key aspects covered include checkpointing to provide fault tolerance, configuring the state store, and techniques for reprocessing data in batch and streaming contexts.
PGConf APAC 2018 - Patroni: Kubernetes-native PostgreSQL companion
Speaker: Alexander Kukushkin
Kubernetes is a solid leader among different cloud orchestration engines and its adoption rate is growing on a daily basis. Naturally people want to run both their applications and databases on the same infrastructure.
There are a lot of ways to deploy and run PostgreSQL on Kubernetes, but most of them are not cloud-native. Around one year ago Zalando started to run HA setup of PostgreSQL on Kubernetes managed by Patroni. Those experiments were quite successful and produced a Helm chart for Patroni. That chart was useful, albeit a single problem: Patroni depended on Etcd, ZooKeeper or Consul.
Few people look forward to deploy two applications instead of one and support them later on. In this talk I would like to introduce Kubernetes-native Patroni. I will explain how Patroni uses Kubernetes API to run a leader election and store the cluster state. I’m going to live-demo a deployment of HA PostgreSQL cluster on Minikube and share our own experience of running more than 130 clusters on Kubernetes.
Patroni is a Python open-source project developed by Zalando in cooperation with other contributors on GitHub: https://github.com/zalando/patroni
Sessionization with Spark streaming
The document discusses sessionization with Spark streaming to analyze user sessions from a constant stream of page visit data. Key points include:
- Streaming page visit data presents challenges like joining new visits to ongoing sessions and handling variable data volumes and long user sessions.
- The proposed solution uses Spark streaming to join a checkpoint of incomplete sessions with new visit data to calculate session metrics in real-time.
- Important aspects are controlling data ingress size and partitioning to optimize performance of operations like joins and using custom formats to handle output to multiple sinks.
Deep dive into PostgreSQL statistics.
This document discusses using PostgreSQL statistics to optimize performance. It describes various statistics sources like pg_stat_database, pg_stat_bgwriter, and pg_stat_replication that provide information on operations, caching, and replication lag. It also provides examples of using these sources to identify issues like long transactions, temporary file growth, and replication delays.
Move Over, Rsync
This document summarizes a presentation about the advantages of using ZFS for data replication compared to rsync. It provides several examples showing ZFS performing full dataset replications between 289x and 1,148x faster than rsync due to its use of copy-on-write snapshots and incremental block-level replication. It also briefly explains how copy-on-write snapshots and incremental replication work at a high-level in ZFS to provide such performance advantages over rsync. Additional context is provided about data services that offer ZFS-based replication for $60 or less per terabyte per month.
InfluxDB IOx Tech Talks: Intro to the InfluxDB IOx Read Buffer - A Read-Optim...
InfluxDB IOx Tech Talks: Intro to the InfluxDB IOx Read Buffer - A Read-Optimized In-Memory Query Execution Engine
Monitoring with Ganglia
June 24, 2014. At Velocity 2014, Fastly engineer Vladimir Vuksan gave an intro to Ganglia concepts (grid, clusters, hosts) as well as an installation of a sample monitoring grid. He also goes through the following commonly used visualization tools and how they may aid in detecting issues, identifying causes, and taking corrective action:
- Cluster/Grid Views
- Aggregate graphs
- Compare Hosts
- Custom graph functionality
- Views
- Interactive graphs
- Trending
- Nagios/Alerting system integration
- How to add metrics to Ganglia
- Different export formats such as JSON, CSV, and XML
Kernel Recipes 2017: Using Linux perf at Netflix
This document discusses using the Linux perf profiling tool at Netflix. It begins with an overview of why Netflix needs Linux profiling to understand CPU usage quickly and completely. It then provides an introduction to the perf tool, covering its basic workflow and commands. The document discusses profiling CPU usage with perf, including potential issues like JIT runtimes and missing symbols. It provides several examples of perf commands for listing, counting, and recording events. The overall summary is that perf allows Netflix to quickly and accurately profile CPU usage across the entire software stack, from applications to libraries to the kernel, to optimize performance.
Monitoring MySQL with OpenTSDB
A monitoring system is arguably the most crucial system to have in place when administering and tweaking the performance of any database system. DBAs also find themselves with a variety of monitoring systems and plugins to use; ranging from small scripts in cron to complex data collection systems. In this talk, I’ll discuss how Box made a shift from the Cacti monitoring system and other various shell scripts to OpenTSDB and the changes made to our servers and daily interaction with monitoring to increase our agility in identifying and addressing changes in database behavior.
Advanced Apache Cassandra Operations with JMX
Nodetool is a command line interface for managing a Cassandra node. It provides commands for node administration, cluster inspection, table operations and more. The nodetool info command displays node-specific information such as status, load, memory usage and cache details. The nodetool compactionstats command shows compaction status including active tasks and progress. The nodetool tablestats command displays statistics for a specific table including read/write counts, space usage, cache usage and latency.
Profiling the logwriter and database writer
The document discusses the behavior of the Oracle log writer (LGWR) process under different conditions. In idle mode, LGWR sleeps for 3 seconds at a time on a semaphore without writing to the redo log buffer. When a transaction is committed, LGWR may write the committed redo entries to disk either before or after the foreground process waits on a "log file sync" event, depending on whether LGWR has already flushed the data. The document also compares the "post-wait" and "polling" modes used for the log file sync wait.
Troubleshooting PostgreSQL with pgCenter
PgCenter is a tool for monitoring and troubleshooting PostgreSQL. It provides a graphical interface to view key performance metrics and statuses. Some of its main features include displaying server health, load, memory and disk usage, statement performance, replication status and more. It aims to help PostgreSQL administrators quickly check the health of their databases and identify potential problems.
opentsdb in a real enviroment
This document discusses the architecture of OpenTSDB and provides instructions for setting up and using OpenTSDB to collect CPU metrics from a host. It describes:
1) The main components of OpenTSDB including the TSD, HBase, and Hadoop.
2) How to start and stop OpenTSDB by starting/stopping TSD, HBase, and Hadoop.
3) A sample scenario of collecting CPU load average metrics from a host and inserting them into OpenTSDB using a script to post metrics to the TSD socket.
Apache Spark Structured Streaming + Apache Kafka = ♡
The slides I prepared for https://www.meetup.com/Paris-Apache-Kafka-Meetup/events/268164461/ about Apache Kafka integration in Apache Spark Structured Streaming.
HBaseCon 2013: OpenTSDB at Box
This document discusses Box's use of OpenTSDB to store and query time series metrics data. It describes how OpenTSDB provides a scalable and easy way to collect, store, and query large amounts of metrics data compared to previous solutions. It includes examples of using OpenTSDB, such as a script to collect MySQL metrics and adding it as a cron job, and examples of querying the data through the OpenTSDB API and web interface. It also provides some statistics about Box's OpenTSDB deployment and next steps.
Deep dive into PostgreSQL statistics.
This document discusses PostgreSQL statistics and how to use them effectively. It provides an overview of various PostgreSQL statistics sources like views, functions and third-party tools. It then demonstrates how to analyze specific statistics like those for databases, tables, indexes, replication and query activity to identify anomalies, optimize performance and troubleshoot issues.
Performance is a feature! - London .NET User Group
Starting with the premise that "Performance is a Feature", this session will look at how to measure, what to measure and how get the best performance from your .NET code.
We will look at real-world examples from the Roslyn code-base and StackOverflow (the product), including how the .NET Garbage Collector needs to be tamed!
Apache Spark in your likeness - low and high level customization
User-defined features and session extensions in Apache Spark allow for high-level and low-level customization. High-level customization includes user-defined types (UDT), user-defined functions (UDF), and user-defined aggregate functions (UDAF). Low-level customization involves extensions to the analyzer rules, optimizations, physical execution, and more. The document provides examples of UDT, UDF, and UDAF and discusses how they allow incorporating custom logic into Spark applications similar to stored procedures in databases.
Ganglia Monitoring Tool
The document discusses Ganglia, an open-source monitoring system for clusters and grids. It provides an overview of Ganglia's architecture and components, including Gmond for metric gathering, Gmetad for aggregation, and an Apache web frontend for presentation. Configuration details are also covered for Gmond, Gmetad, and the underlying Round Robin Database storage.
Am I reading GC logs Correctly?
The document provides an overview of how to read and understand garbage collection (GC) log lines from different Java vendors and JVM versions. It begins by explaining the parts of a basic GC log line for the OpenJDK GC log format. It then discusses GC log lines for G1 GC and CMS GC in more detail. Finally, it shares examples of GC log formats from IBM JVMs and different levels of information provided. The document aims to help readers learn to correctly interpret GC logs and analyze GC behavior.
Adding replication protocol support for psycopg2
This document discusses adding logical replication protocol support to the psycopg2 library to allow Python applications to consume real-time replication streams from PostgreSQL. It provides code examples for connecting to replication slots, consuming change streams and messages, and stopping replication. Docker images are also available to simplify testing logical replication. Physical replication is also supported through a separate connection class. Asynchronous replication connections and keepalive messages are demonstrated as well.
Ceph issue 해결 사례
[Open Infrastructure & Cloud Native Days Korea 2019]
커뮤니티 버전의 OpenStack 과 Ceph를 활용하여 대고객서비스를 구축한 사례를 공유합니다. 유연성을 확보한 기업용 클라우드 서비스 구축 사례와 높은 수준의 보안을 요구하는 거래소 서비스를 구축, 운영한 사례를 소개합니다. 또한 이 프로젝트에 사용된 기술 스택 및 장애 해결사례와 최적화 방안을 소개합니다. 오픈스택은 역시 오픈소스컨설팅입니다.
#openstack #ceph #openinfraday #cloudnative #opensourceconsulting
Advanced troubleshooting linux performance
Small presentation about monitoring stack performance (for example on linux) to see it`s behavior and troubleshoot operational issues.
PostgreSQL WAL for DBAs
Devrim Gunduz gives a presentation on Write-Ahead Logging (WAL) in PostgreSQL. WAL logs all transactions to files called write-ahead logs (WAL files) before changes are written to data files. This allows for crash recovery by replaying WAL files. WAL files are used for replication, backup, and point-in-time recovery (PITR) by replaying WAL files to restore the database to a previous state. Checkpoints write all dirty shared buffers to disk and update the pg_control file with the checkpoint location.
Troubleshooting PostgreSQL Streaming Replication
This document provides an overview of troubleshooting streaming replication in PostgreSQL. It begins with introductions to write-ahead logging and replication internals. Common troubleshooting tools are then described, including built-in views and functions as well as third-party tools. Finally, specific troubleshooting cases are discussed such as replication lag, WAL bloat, recovery conflicts, and high CPU recovery usage. Throughout, examples are provided of how to detect and diagnose issues using the various tools.
Managing PostgreSQL with PgCenter
This document provides an overview of pgCenter, a tool for managing and monitoring PostgreSQL databases. It describes pgCenter's interface which displays system metrics, PostgreSQL statistics and additional information. The interface shows values for items like CPU and memory usage, database connections, autovacuum operations, and query information. PgCenter provides a quick way to view real-time PostgreSQL and server performance metrics.
Hw09 Data Processing In The Enterprise
This document discusses JPMorgan Chase's consideration of using Hadoop in the enterprise. It outlines the potential for Hadoop to reduce costs through lower hardware expenses and more efficient use of resources. Hadoop could also enable new types of data analysis and disrupt existing technologies. The document then describes JPMorgan Chase's active proof-of-concept projects evaluating Hadoop and how it positions Hadoop relative to traditional data warehousing. It concludes by identifying additional features needed to better support enterprise use of Hadoop.
Hw09 Hadoop Applications At Yahoo!
Yahoo! is the largest contributor, tester, and user of Hadoop. They operate 4000 node Hadoop clusters and release the Yahoo! Distribution of Hadoop as open source. Yahoo! contributes all their Hadoop development work back to the Apache Foundation and uses Hadoop for large-scale data processing and analytics across petabytes of data.
More Related Content
What's hot
Monitoring with Ganglia
June 24, 2014. At Velocity 2014, Fastly engineer Vladimir Vuksan gave an intro to Ganglia concepts (grid, clusters, hosts) as well as an installation of a sample monitoring grid. He also goes through the following commonly used visualization tools and how they may aid in detecting issues, identifying causes, and taking corrective action:
- Cluster/Grid Views
- Aggregate graphs
- Compare Hosts
- Custom graph functionality
- Views
- Interactive graphs
- Trending
- Nagios/Alerting system integration
- How to add metrics to Ganglia
- Different export formats such as JSON, CSV, and XML
Kernel Recipes 2017: Using Linux perf at Netflix
This document discusses using the Linux perf profiling tool at Netflix. It begins with an overview of why Netflix needs Linux profiling to understand CPU usage quickly and completely. It then provides an introduction to the perf tool, covering its basic workflow and commands. The document discusses profiling CPU usage with perf, including potential issues like JIT runtimes and missing symbols. It provides several examples of perf commands for listing, counting, and recording events. The overall summary is that perf allows Netflix to quickly and accurately profile CPU usage across the entire software stack, from applications to libraries to the kernel, to optimize performance.
Monitoring MySQL with OpenTSDB
A monitoring system is arguably the most crucial system to have in place when administering and tweaking the performance of any database system. DBAs also find themselves with a variety of monitoring systems and plugins to use; ranging from small scripts in cron to complex data collection systems. In this talk, I’ll discuss how Box made a shift from the Cacti monitoring system and other various shell scripts to OpenTSDB and the changes made to our servers and daily interaction with monitoring to increase our agility in identifying and addressing changes in database behavior.
Advanced Apache Cassandra Operations with JMX
Nodetool is a command line interface for managing a Cassandra node. It provides commands for node administration, cluster inspection, table operations and more. The nodetool info command displays node-specific information such as status, load, memory usage and cache details. The nodetool compactionstats command shows compaction status including active tasks and progress. The nodetool tablestats command displays statistics for a specific table including read/write counts, space usage, cache usage and latency.
Profiling the logwriter and database writer
The document discusses the behavior of the Oracle log writer (LGWR) process under different conditions. In idle mode, LGWR sleeps for 3 seconds at a time on a semaphore without writing to the redo log buffer. When a transaction is committed, LGWR may write the committed redo entries to disk either before or after the foreground process waits on a "log file sync" event, depending on whether LGWR has already flushed the data. The document also compares the "post-wait" and "polling" modes used for the log file sync wait.
Troubleshooting PostgreSQL with pgCenter
PgCenter is a tool for monitoring and troubleshooting PostgreSQL. It provides a graphical interface to view key performance metrics and statuses. Some of its main features include displaying server health, load, memory and disk usage, statement performance, replication status and more. It aims to help PostgreSQL administrators quickly check the health of their databases and identify potential problems.
opentsdb in a real enviroment
This document discusses the architecture of OpenTSDB and provides instructions for setting up and using OpenTSDB to collect CPU metrics from a host. It describes:
1) The main components of OpenTSDB including the TSD, HBase, and Hadoop.
2) How to start and stop OpenTSDB by starting/stopping TSD, HBase, and Hadoop.
3) A sample scenario of collecting CPU load average metrics from a host and inserting them into OpenTSDB using a script to post metrics to the TSD socket.
Apache Spark Structured Streaming + Apache Kafka = ♡
The slides I prepared for https://www.meetup.com/Paris-Apache-Kafka-Meetup/events/268164461/ about Apache Kafka integration in Apache Spark Structured Streaming.
HBaseCon 2013: OpenTSDB at Box
This document discusses Box's use of OpenTSDB to store and query time series metrics data. It describes how OpenTSDB provides a scalable and easy way to collect, store, and query large amounts of metrics data compared to previous solutions. It includes examples of using OpenTSDB, such as a script to collect MySQL metrics and adding it as a cron job, and examples of querying the data through the OpenTSDB API and web interface. It also provides some statistics about Box's OpenTSDB deployment and next steps.
Deep dive into PostgreSQL statistics.
This document discusses PostgreSQL statistics and how to use them effectively. It provides an overview of various PostgreSQL statistics sources like views, functions and third-party tools. It then demonstrates how to analyze specific statistics like those for databases, tables, indexes, replication and query activity to identify anomalies, optimize performance and troubleshoot issues.
Performance is a feature! - London .NET User Group
Starting with the premise that "Performance is a Feature", this session will look at how to measure, what to measure and how get the best performance from your .NET code.
We will look at real-world examples from the Roslyn code-base and StackOverflow (the product), including how the .NET Garbage Collector needs to be tamed!
Apache Spark in your likeness - low and high level customization
User-defined features and session extensions in Apache Spark allow for high-level and low-level customization. High-level customization includes user-defined types (UDT), user-defined functions (UDF), and user-defined aggregate functions (UDAF). Low-level customization involves extensions to the analyzer rules, optimizations, physical execution, and more. The document provides examples of UDT, UDF, and UDAF and discusses how they allow incorporating custom logic into Spark applications similar to stored procedures in databases.
Ganglia Monitoring Tool
The document discusses Ganglia, an open-source monitoring system for clusters and grids. It provides an overview of Ganglia's architecture and components, including Gmond for metric gathering, Gmetad for aggregation, and an Apache web frontend for presentation. Configuration details are also covered for Gmond, Gmetad, and the underlying Round Robin Database storage.
Am I reading GC logs Correctly?
The document provides an overview of how to read and understand garbage collection (GC) log lines from different Java vendors and JVM versions. It begins by explaining the parts of a basic GC log line for the OpenJDK GC log format. It then discusses GC log lines for G1 GC and CMS GC in more detail. Finally, it shares examples of GC log formats from IBM JVMs and different levels of information provided. The document aims to help readers learn to correctly interpret GC logs and analyze GC behavior.
Adding replication protocol support for psycopg2
This document discusses adding logical replication protocol support to the psycopg2 library to allow Python applications to consume real-time replication streams from PostgreSQL. It provides code examples for connecting to replication slots, consuming change streams and messages, and stopping replication. Docker images are also available to simplify testing logical replication. Physical replication is also supported through a separate connection class. Asynchronous replication connections and keepalive messages are demonstrated as well.
Ceph issue 해결 사례
[Open Infrastructure & Cloud Native Days Korea 2019]
커뮤니티 버전의 OpenStack 과 Ceph를 활용하여 대고객서비스를 구축한 사례를 공유합니다. 유연성을 확보한 기업용 클라우드 서비스 구축 사례와 높은 수준의 보안을 요구하는 거래소 서비스를 구축, 운영한 사례를 소개합니다. 또한 이 프로젝트에 사용된 기술 스택 및 장애 해결사례와 최적화 방안을 소개합니다. 오픈스택은 역시 오픈소스컨설팅입니다.
#openstack #ceph #openinfraday #cloudnative #opensourceconsulting
Advanced troubleshooting linux performance
Small presentation about monitoring stack performance (for example on linux) to see it`s behavior and troubleshoot operational issues.
PostgreSQL WAL for DBAs
Devrim Gunduz gives a presentation on Write-Ahead Logging (WAL) in PostgreSQL. WAL logs all transactions to files called write-ahead logs (WAL files) before changes are written to data files. This allows for crash recovery by replaying WAL files. WAL files are used for replication, backup, and point-in-time recovery (PITR) by replaying WAL files to restore the database to a previous state. Checkpoints write all dirty shared buffers to disk and update the pg_control file with the checkpoint location.
Troubleshooting PostgreSQL Streaming Replication
This document provides an overview of troubleshooting streaming replication in PostgreSQL. It begins with introductions to write-ahead logging and replication internals. Common troubleshooting tools are then described, including built-in views and functions as well as third-party tools. Finally, specific troubleshooting cases are discussed such as replication lag, WAL bloat, recovery conflicts, and high CPU recovery usage. Throughout, examples are provided of how to detect and diagnose issues using the various tools.
Managing PostgreSQL with PgCenter
This document provides an overview of pgCenter, a tool for managing and monitoring PostgreSQL databases. It describes pgCenter's interface which displays system metrics, PostgreSQL statistics and additional information. The interface shows values for items like CPU and memory usage, database connections, autovacuum operations, and query information. PgCenter provides a quick way to view real-time PostgreSQL and server performance metrics.
What's hot (20)
Apache Spark Structured Streaming + Apache Kafka = ♡
Apache Spark Structured Streaming + Apache Kafka = ♡
Performance is a feature! - London .NET User Group
Performance is a feature! - London .NET User Group
Apache Spark in your likeness - low and high level customization
Apache Spark in your likeness - low and high level customization
Viewers also liked
Hw09 Data Processing In The Enterprise
This document discusses JPMorgan Chase's consideration of using Hadoop in the enterprise. It outlines the potential for Hadoop to reduce costs through lower hardware expenses and more efficient use of resources. Hadoop could also enable new types of data analysis and disrupt existing technologies. The document then describes JPMorgan Chase's active proof-of-concept projects evaluating Hadoop and how it positions Hadoop relative to traditional data warehousing. It concludes by identifying additional features needed to better support enterprise use of Hadoop.
Hw09 Hadoop Applications At Yahoo!
Yahoo! is the largest contributor, tester, and user of Hadoop. They operate 4000 node Hadoop clusters and release the Yahoo! Distribution of Hadoop as open source. Yahoo! contributes all their Hadoop development work back to the Apache Foundation and uses Hadoop for large-scale data processing and analytics across petabytes of data.
Hw09 Building Data Intensive Apps A Closer Look At Trending Topics.Org
The document describes TrendingTopics.org, a website that detects trending topics using Wikipedia page view data analyzed with Hadoop on Amazon EC2. It loads over 1TB of Wikipedia page view logs into Hadoop on EC2, uses Hive and Python to perform daily timelines and trend detection, and hosts the results on a Ruby on Rails front end also running on EC2. All the code is open source and hosted on Github.
Hw09 Large Scale Transaction Analysis
1. Visa operates the world's largest payment network, VisaNet, which processes over 130 million authorizations per day across 28 million merchant locations.
2. Hadoop offers the potential to analyze Visa's large volumes of transaction data faster than currently possible. Visa is researching how to configure Hadoop solutions and integrate them securely within its existing financial systems.
3. Key questions include how to securely load data into Hadoop clusters, integrate security features like hardware security modules, and ensure solutions meet Visa's requirements of being fast, secure, flexible, reliable and scalable for enterprise use.
ZooKeeper Futures
Henry Robinson gave a presentation on upcoming features for ZooKeeper. He discussed observers, which allow non-voting servers to scale client connections without impacting performance. He also covered dynamic ensembles, which would allow changing the ZooKeeper cluster membership without downtime. Finally, he announced that Cloudera's Distribution for Hadoop will include ZooKeeper packages to integrate it more fully.
Hw09 Optimizing Hadoop Deployments
This document discusses optimizing Hadoop workloads through hardware and software configuration. Key recommendations include using dual-socket servers with the latest Intel Xeon processors for better performance and scalability. Sufficient memory, SSDs, and an optimized Linux distribution can also improve throughput and reduce costs. Proper configuration of Hadoop masters, slaves, and middleware helps ensure workload demands are met efficiently.
Comprehensive Security for the Enterprise III: Protecting Data at Rest and In...
This webinar discusses how you can use Navigator capabilities such as Encrypt and Key Trustee to secure data and enable compliance. Additionally, we will discuss our joint work with Intel on Project Rhino (an initiative to improve data security in Hadoop). We also hear from a security architect at a financial services company that is using encryption and key management to meet financial regulatory requirements.
Viewers also liked (7)
Hw09 Building Data Intensive Apps A Closer Look At Trending Topics.Org
Hw09 Building Data Intensive Apps A Closer Look At Trending Topics.Org
Comprehensive Security for the Enterprise III: Protecting Data at Rest and In...
Comprehensive Security for the Enterprise III: Protecting Data at Rest and In...
Similar to Hw09 Monitoring Best Practices
Nagios Conference 2011 - Daniel Wittenberg - Scaling Nagios At A Giant Insur...
Daniel Wittenburg' presentation on a reference story for a German Health Insurance Company. The presentation was given during the Nagios World Conference North America held Sept 27-29th, 2011 in Saint Paul, MN. For more information on the conference (including photos and videos), visit: http://go.nagios.com/nwcna
Deployment with Fabric
This document discusses using Fabric for Python application deployment and configuration management. It provides an overview of Fabric basics like tasks, roles, and environments. It also describes using Fabric for common operations like code deployment, database migrations, and managing server growth. Key advantages of Fabric include its simple task-based interface and ability to control multiple servers simultaneously. The document provides an example of using Fabric for a full deployment process including pushing code, running migrations, and restarting processes.
Dynamic Hadoop Clusters
The document discusses managing Hadoop clusters dynamically using configuration management tools like SmartFrog. It describes how SmartFrog can be used to model Hadoop clusters, deploy the clusters from the model, manipulate the Hadoop filesystem and submit jobs. Issues discussed include Hadoop configuration, performance on virtual machines, and dynamic network discovery. The document calls for others to help build out the SmartFrog Hadoop tools.
Introduction to PowerShell
This document contains biographical information about Boulos Dib, an independent consultant specializing in software development. It provides details about Dib's early experience with personal computers and programming languages. It also lists upcoming presentations by Dib on LightSwitch and Silverlight at the NYC Code Camp in October 2011. The document concludes with an overview of PowerShell scripting.
Nagios Conference 2012 - Dan Wittenberg - Case Study: Scaling Nagios Core at ...
Dan Wittenberg's presentation on using Nagios at a Fortune 50 Company
The presentation was given during the Nagios World Conference North America held Sept 25-28th, 2012 in Saint Paul, MN. For more information on the conference (including photos and videos), visit: http://go.nagios.com/nwcna
Django deployment with PaaS
This presentation was given at the Boston Django meetup on November 16, and surveyed several leading PaaS providers including Stackato, Dotcloud, OpenShift and Heroku.
For each PaaS provider, I documented the steps necessary to deploy Mezzanine, a popular Django-based CMS and blogging platform.
At the end of the presentation, I do a wrap-up of the different providers and provide a comparison matrix showing which providers have which features. This matrix is likely to go out-of-date quickly because these providers are adding new features all the time.
Hw09 Production Deep Dive With High Availability
ContextWeb is an online advertisement company that processes large volumes of log data using Hadoop. They process up to 120GB of raw log files per day. Their Hadoop cluster consists of 40 nodes and processes around 2000 MapReduce jobs per day. They developed techniques for partitioning data by date/time and using file revisions to allow incremental processing while ensuring data consistency and freshness of reports.
Eagle from eBay at China Hadoop Summit 2015
The document summarizes Hadoop, a full-stack real-time monitoring framework for eBay's Hadoop clusters. It discusses eBay's large-scale Hadoop environment with over 10 clusters, 10,000 nodes, and 50,000 jobs/day. It then introduces Eagle, the uniform monitoring framework, which consists of the Eagle framework and Eagle apps. The framework provides scalable real-time monitoring capabilities and the apps provide domain-specific monitoring for Hadoop, Spark, HBase etc. It highlights two Eagle apps: JPA for job performance monitoring and DAM for security monitoring.
Planning for-high-performance-web-application
Planning for performance is a document about optimizing web applications for performance. It discusses basic practices like using source code management, continuous integration tools, and issue trackers. It also covers hardware platforms, software platforms, system essentials like monitoring and security tools, and optimizations like caching, SQL tuning, and scaling through load balancing, databases, file systems, and more. The overall document provides an overview of many aspects of planning for and ensuring high performance of web applications.
Osd ctw spark
Spark is a fast and general engine for large-scale data processing. It runs on Hadoop clusters through YARN and Mesos, and can also run standalone. Spark is up to 100x faster than Hadoop for certain applications because it keeps data in memory rather than disk, and it supports iterative algorithms through its Resilient Distributed Dataset (RDD) abstraction. The presenter provides a demo of Spark's word count algorithm in Scala, Java, and Python to illustrate how easy it is to use Spark across languages.
Caching and tuning fun for high scalability
Caching has been a 'hot' topic for a few years. But caching takes more than merely taking data and putting it in a cache : the right caching techniques can improve performance and reduce load significantly. But we'll also look at some major pitfalls, showing that caching the wrong way can bring down your site. If you're looking for a clear explanation about various caching techniques and tools like Memcached, Nginx and Varnish, as well as ways to deploy them in an efficient way, this talk is for you.
Going live with BommandBox and docker Into The Box 2018
This document discusses using CommandBox and Docker to deploy real projects. It covers background on the development workflow and environments, benefits of Docker and CommandBox, code cleanup tools like CFLint and git hooks, serving apps with CommandBox, server monitoring with Prometheus, dynamic configuration, caching, session storage, logging with Elasticsearch and Kibana, load balancing with Kubernetes, data changes, scheduled tasks, and canary/blue-green deployments. The overall message is that CommandBox and tools can provide structure and simplify transitions to help teams succeed in deploying applications.
Into The Box 2018 Going live with commandbox and docker
This document discusses using CommandBox and Docker to deploy real projects. It covers background on the development workflow and environments, benefits of Docker and CommandBox, code cleanup tools like CFLint and git hooks, serving apps with CommandBox, server monitoring with Prometheus, dynamic configuration, caching, session storage, logging with Elasticsearch and Kibana, load balancing with Kubernetes, data changes, scheduled tasks, and canary/blue-green deployments. The overall message is that CommandBox and tools can provide structure and simplify transitions to help teams succeed in deploying applications.
Basics of big data analytics hadoop
The document provides an overview of big data, analytics, Hadoop, and related concepts. It discusses what big data is and the challenges it poses. It then describes Hadoop as an open-source platform for distributed storage and processing of large datasets across clusters of commodity hardware. Key components of Hadoop introduced include HDFS for storage, MapReduce for parallel processing, and various other tools. A word count example demonstrates how MapReduce works. Common use cases and companies using Hadoop are also listed.
FP - Découverte de Play Framework Scala
♥ Play Framework is an open-source web application framework for Java and Scala that follows the model-view-controller (MVC) architectural pattern. It supports dependency injection, routing, and asynchronous programming. Some key features include routing, controllers, database access using Slick, evolutions for database schema changes, and support for functional programming concepts like Option, Either, and Future.
Monitoring MySQL with DTrace/SystemTap
DTrace and SystemTap are dynamic tracing frameworks available for Solaris and Linux respectively. This session will give an overview of the static DTrace probes available in both Drizzle and MySQL and show numerous examples of scripts that utilize these probes. Mixing dynamic and static probes will also be discussed.
DrupalCampLA 2011: Drupal backend-performance
The DrupalCampLA 2011 presentation on backend performance. The slides go over optimizations that can be done through the LAMP (or now VAN LAMMP stack for even more performance) to get everything up and running.
Deep learning - the conf br 2018
This is the slides of my presentation at "the conf br 2018" called "Deep Learning - Segmentation, Identification and Classification"
Introduction to Stacki at Atlanta Meetup February 2016
An introduction to Stacki-the fastest bare metal Linux server provisioning tool from the Stacki Atlanta kickoff meetup on 2/23/16 at the Microsoft Innovation Center. Greg Bruno is the VP Engineering at StackIQ.
Beyond Cookies, Persistent Storage For Web Applications Web Directions North ...
This document discusses persistent storage options for web applications beyond cookies. It describes name/value storage, databases, static files, and how HTML5 specifications like localStorage, sessionStorage, databases, and the application cache provide similar persistent storage capabilities to older technologies like Gears and Flash. Use cases, code examples, and browser support are provided for each HTML5 storage mechanism.
Similar to Hw09 Monitoring Best Practices (20)
Nagios Conference 2011 - Daniel Wittenberg - Scaling Nagios At A Giant Insur...
Nagios Conference 2011 - Daniel Wittenberg - Scaling Nagios At A Giant Insur...
Nagios Conference 2012 - Dan Wittenberg - Case Study: Scaling Nagios Core at ...
Nagios Conference 2012 - Dan Wittenberg - Case Study: Scaling Nagios Core at ...
Going live with BommandBox and docker Into The Box 2018
Going live with BommandBox and docker Into The Box 2018
Into The Box 2018 Going live with commandbox and docker
Into The Box 2018 Going live with commandbox and docker
Introduction to Stacki at Atlanta Meetup February 2016
Introduction to Stacki at Atlanta Meetup February 2016
Beyond Cookies, Persistent Storage For Web Applications Web Directions North ...
Beyond Cookies, Persistent Storage For Web Applications Web Directions North ...
More from Cloudera, Inc.
Partner Briefing_January 25 (FINAL).pptx
The document discusses using Cloudera DataFlow to address challenges with collecting, processing, and analyzing log data across many systems and devices. It provides an example use case of logging modernization to reduce costs and enable security solutions by filtering noise from logs. The presentation shows how DataFlow can extract relevant events from large volumes of raw log data and normalize the data to make security threats and anomalies easier to detect across many machines.
Cloudera Data Impact Awards 2021 - Finalists
The document outlines the 2021 finalists for the annual Data Impact Awards program, which recognizes organizations using Cloudera's platform and the impactful applications they have developed. It provides details on the challenges, solutions, and outcomes for each finalist project in the categories of Data Lifecycle Connection, Cloud Innovation, Data for Enterprise AI, Security & Governance Leadership, Industry Transformation, People First, and Data for Good. There are multiple finalists highlighted in each category demonstrating innovative uses of data and analytics.
2020 Cloudera Data Impact Awards Finalists
Cloudera is proud to present the 2020 Data Impact Awards Finalists. This annual program recognizes organizations running the Cloudera platform for the applications they've built and the impact their data projects have on their organizations, their industries, and the world. Nominations were evaluated by a panel of independent thought-leaders and expert industry analysts, who then selected the finalists and winners. Winners exemplify the most-cutting edge data projects and represent innovation and leadership in their respective industries.
Edc event vienna presentation 1 oct 2019
The document outlines the agenda for Cloudera's Enterprise Data Cloud event in Vienna. It includes welcome remarks, keynotes on Cloudera's vision and customer success stories. There will be presentations on the new Cloudera Data Platform and customer case studies, followed by closing remarks. The schedule includes sessions on Cloudera's approach to data warehousing, machine learning, streaming and multi-cloud capabilities.
Machine Learning with Limited Labeled Data 4/3/19
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
Introducing Cloudera DataFlow (CDF) 2.13.19
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
-Powerful data ingestion powered by Apache NiFi
-Edge data collection by Apache MiNiFi
-IoT-scale streaming data processing with Apache Kafka
-Enterprise services to offer unified security and governance from edge-to-enterprise
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Join Cloudera as we outline how we use Cloudera technology to strengthen sales engagement, minimize marketing waste, and empower line of business leaders to drive successful outcomes.
Leveraging the cloud for analytics and machine learning 1.29.19
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Leveraging the Cloud for Big Data Analytics 12.11.18
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Modern Data Warehouse Fundamentals Part 3
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Modern Data Warehouse Fundamentals Part 2
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Modern Data Warehouse Fundamentals Part 1
The document discusses the benefits and trends of modernizing a data warehouse. It outlines how a modern data warehouse can provide deeper business insights at extreme speed and scale while controlling resources and costs. Examples are provided of companies that have improved fraud detection, customer retention, and machine performance by implementing a modern data warehouse that can handle large volumes and varieties of data from many sources.
Extending Cloudera SDX beyond the Platform
Cloudera SDX is by no means no restricted to just the platform; it extends well beyond. In this webinar, we show you how Bardess Group’s Zero2Hero solution leverages the shared data experience to coordinate Cloudera, Trifacta, and Qlik to deliver complete customer insight.
Federated Learning: ML with Privacy on the Edge 11.15.18
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
Analyst Webinar: Doing a 180 on Customer 360
451 Research Analyst Sheryl Kingstone, and Cloudera’s Steve Totman recently discussed how a growing number of organizations are replacing legacy Customer 360 systems with Customer Insights Platforms.
Build a modern platform for anti-money laundering 9.19.18
In this webinar, you will learn how Cloudera and BAH riskCanvas can help you build a modern AML platform that reduces false positive rates, investigation costs, technology sprawl, and regulatory risk.
Introducing the data science sandbox as a service 8.30.18
How can companies integrate data science into their businesses more effectively? Watch this recorded webinar and demonstration to hear more about operationalizing data science with Cloudera Data Science Workbench on Cazena’s fully-managed cloud platform.
More from Cloudera, Inc. (20)
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Leveraging the cloud for analytics and machine learning 1.29.19
Leveraging the cloud for analytics and machine learning 1.29.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Leveraging the Cloud for Big Data Analytics 12.11.18
Leveraging the Cloud for Big Data Analytics 12.11.18
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18
Build a modern platform for anti-money laundering 9.19.18
Build a modern platform for anti-money laundering 9.19.18
Introducing the data science sandbox as a service 8.30.18
Introducing the data science sandbox as a service 8.30.18
Recently uploaded
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications.
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsCosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Recently uploaded (20)
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Hw09 Monitoring Best Practices
- 1. How to monitor the $H!T out of Hadoop Developing a comprehensive open approach to monitoring hadoop clusters
- 7. Hadoop JMX
- 17. Disk Utilization
- 27. JMX Graphing
- 28. JMX Graphing
- 29. JMX Graphing