CQL In Cassandra 1.0 (and beyond)

This document discusses metrics monitoring systems. It provides an overview of the Graphite monitoring system architecture, including the carbon-cache and carbon-relay components. It then evaluates various open source alternatives for handling high volumes of metrics data, finding that a combination of go-carbon and carbon-c-relay can process over 1.4 million requests per second. The document also discusses tagging metrics with metadata and time series databases that support tags.

Kafka monitoring and metrics With Docker, Grafana, Prometheus, JMX and JConsole By Touraj Ebrahimi Senior Java Developer and Java Architect github: toraj58 bitbucket: toraj58 twitter: @toraj58 https://www.youtube.com/channel/UCcLcw6sTk_8G6EgfBr0E5uA

This document discusses Graphite and options for optimizing its performance for high volumes of metrics data. It summarizes the default Graphite architecture using Carbon and Whisper and different approaches for scaling it up including using go-carbon, carbon-c-relay, and evaluating alternative time series databases like Influx and OpenTSDB. Various techniques for optimizing whisper and cache configurations, I/O performance, and system parameters are also explored. Overall the best performing combination found was go-carbon with carbon-c-relay to handle over 1 million requests per second.

While delivering VoIP solutions to customers for more than ten years, at sipgate we have gained experience in monitoring our VoIP setup. The talk will give an insight on how to monitor Asterisk, Kamailio, Yate and other vital parts of our setup through standard checks and own scripts. We will not only show how to monitor standard SIP, but also how to detect bottlenecks and misfunctions.

Tips, tricks and strategies we use at EverythingMe to scale and keep our servers always running, no matter what

This document proposes an architecture for distributed indexing, storage, and real-time analysis of logs. It discusses challenges of scaling log collection and analysis across hundreds of servers generating terabytes of data daily. The proposed architecture uses multicast messaging and sharding to distribute indexing and querying across clusters of servers for scalability. It emphasizes low overhead indexing and real-time aggregation of results.

Kotlin Coroutines is a new killer feature in Kotlin language. What are they, how do we use them, and how do they connect to Reactive programming (Rx)

The document provides information on application performance tuning education. It discusses key performance metrics like TPS and considerations for CPU usage, memory usage, garbage collection. It then summarizes Java/Tomcat performance tuning factors and garbage collection options. The last part discusses Java profiling and troubleshooting tools like JDK tools, HPROF, jhat, jmap, jstack, jstat and jvisualvm. It also provides an example Tomcat shell script configuration for setting JVM options and using profiling agents.

Perl provides tools like perldoc, cpan, and Perl::Tidy to help developers work more efficiently. One-liners allow running Perl commands and programs directly from the command line. ExtUtils::Command provides functions that emulate common shell commands to make Perl scripts more portable. Perl::Tidy can reformat code to make it more readable.

This document discusses tuning Solr for log search and analysis. It provides the results of baseline tests on Solr performance and capacity indexing 10 million logs. Various configuration changes are then tested, such as using time-based collections, DocValues, commit settings, and hardware optimizations. Using tools like Apache Flume to preprocess logs before indexing into Solr is also recommended for improved throughput. Overall, the document emphasizes that software and hardware optimizations can significantly improve Solr performance and capacity when indexing logs.

This document discusses various tools and techniques for profiling Ruby and Rails applications to optimize performance. It covers benchmarking basics using the Benchmark module and benchmark-ips gem. It also discusses profiling memory and GC using the GC module and memory_profiler gem. For CPU profiling, it recommends the ruby-prof gem. It provides an overview of profiling web apps using New Relic and rack-mini-profiler. Finally, it outlines some Ruby and Rails performance patterns and additional resources.

This document provides an overview and introduction to Cassandra, an open source distributed database management system designed to handle large amounts of data across many commodity servers. It discusses Cassandra's origins from influential papers on Bigtable and Dynamo, its properties including flexibility, scalability and high availability. The document also covers Cassandra's data model using keyspaces and column families, its consistency options, API including Thrift and language drivers, and provides examples of usage for an address book app and storing timeseries data.

This document discusses using Gluster object storage with OpenStack Swift. Gluster-Swift mounts the Swift storage using FUSE and allows Swift to interface with Gluster backends. This avoids reimplementing the Swift object API. Gluster-Swift overrides Swift's distribution and replication to use the Gluster backend. The Swift API is implemented using FUSE operations on the Gluster volume. Future work includes upgrading Gluster-Swift, packaging, optimizations, and potentially developing a native Gluster object interface.

1. Spark Streaming uses Kafka receivers to process data from Kafka in batches at regular intervals. The receivers divide the streams into blocks and write them to Spark's block manager. 2. When processing batches, Spark retrieves the blocks from the block manager and runs jobs on the RDDs created from the blocks. 3. Reliable receivers are needed to handle failures of receivers or the driver, to prevent data loss. The Spark package provides a low-level Kafka receiver implementation that reliably handles offsets and failures.

This document discusses PHP environments and tools. It provides an overview of PHP versions and improvements over time. It also summarizes Laravel, a popular PHP framework, and common tools used in PHP development like Composer and Valet. Production environments, security, optimization, monitoring, and background jobs are also covered at a high level.

The Prometheus monitoring system collects and stores time series data to give valuable insights over hosts, containers, and applications. Its storage engine was designed to be multiple orders of magnitude faster and more space efficient than, say, RRD or SQL storage. However, with the rise of orchestration systems such as Docker Swarm and Kubernetes, and their extensive use of techniques like rolling updates and auto-scaling, environments are becoming increasingly dynamic. This increases the strain on metrics collection systems. To deal with the challenges, a new storage engine has been developed from scratch, bringing a sharp increase in performance and enabling new features. This talk will describe this new storage engine, its architecture, its data structures, and explain why and how it is well suited to gracefully handle high turnover rates of monitoring targets and provide consistent query performance.

This document discusses time series data storage and querying in Prometheus. It describes how Prometheus stores time series data as chunks on disk in a key-value store format, with compression to reduce storage needs. It also explains how Prometheus handles ingesting new time series data through appending to in-memory chunks before writing to disk, and how it handles querying time series data through iterators over chunk files on disk.

This document discusses integrating the Python driver for Cassandra into Python applications. It covers connecting to Cassandra, executing queries, prepared statements, asynchronous queries, object mapping with cqlengine, and best practices for application development including using virtual environments. The presentation aims to make working with Cassandra from Python straightforward and high performing.