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Metrics 2.0 & Graph-Explorer
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Most metrics systems link timeseries to a string key, some add a few tags. They often lack information, use inconsistent formats and terminology, and are poorly organized. As the amount of people and software generating, processing, storing and visualizing metrics grows, this approach becomes very cumbersome and there is a lot to be gained from taking a step back and re-thinking metric identifiers and metadata. Metrics 2.0 is a set of conventions around metrics: With barely any extra work metrics become self-describing and standardized. Compatibility between tools increases dramatically, dashboards can automatically convert information needs into graphs, graph renderers can present data more usefully, anomaly detection and aggregators can work more autonomously and avoid common mistakes. Result: less micromanaging of software and configuration, quicker results, more clarity. Less frustration and room for errors. This talk will also cover the tools that turn this concept into production-ready reality: Graph-Explorer is an application that integrates with Graphite. Enter an expression that represents an information need and it generates the corresponding graphs or alerting rules, automatically applying unit conversion, aggregation, processing, etc. Statsdaemon is an aggregation daemon like Etsy's Statsd that expresses performed aggregations and statistical operations by updating the metrics tags, making sure that the metric metadata always corresponds to the data. Dieter Plaetinck is a systems-gone-backend engineer at Vimeo.
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The Weather of the Century Part 2: High Performance
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These are the slides from the Denver/Boulder Spark meet-up on February 24th, 2016. (deck build animations are all broken here... sorry!) This talk provides an evaluation of existing machine learning pipelines in the eyes of different key stakeholders in the data science ecosystem. Focus is be placed upon the entire process from data to product (and keeping everyone in-between happy). Ultimately I explore how to utilize Spotify’s Luigi pipeline tool in combination with Spark to produce batch processing machine learning pipelines that have operational insights and redundancy built in.
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There is a common belief that in order to solve more [advanced] alerting cases and get more complete coverage, we need complex, often math-heavy solutions based on machine learning or stream processing. This talk sets context and pro's/cons for such approaches, and provides anecdotal examples from the industry, nuancing the applicability of these methods. We then explore how we can get dramatically better alerting, as well as make our lives a lot easier by optimizing workflow and machine-human interaction through an alerting IDE (exemplified by bosun), basic logic, basic math and metric metadata, even for solving complicated alerting problems such as detecting faults in seasonal timeseries data. https://www.usenix.org/conference/srecon16europe/program/presentation/plaetinck
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As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape
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Artificial Intelligence Chap.5 : Uncertainty
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Metrics 2.0 & Graph-Explorer
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Metrics 2.0 & GraphExplorer
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Credit: user niteroi @ panoramio.com
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vimeo.com/43800150
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5.
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“Dieter” ?
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Peter → Deter
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what.is.a.metric
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stats.timers.dfs5.proxy server.object.GET.200.timing .upper_90
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O(X*Y*Z) X = # apps Y = # people Z = # aggregators
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stats.timers.dfs5.proxyserver.object.GET.200.timing.upper_90 { “server”: “dfvimeodfsproxy5”, “http_method”: “GET”, “http_code”: “200”, “unit”: “ms”, “target_type”: “gauge”, “stat”: “upper_90”, “swift_type”: “object” “plugin”: “swift_proxy_server” } https://github.com/vimeo/graphexplorer/wiki
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● b: bit ● B: byte ● Err: errors ● Warn: warnings ● Conn: connections ● Event: events (tcp events etc) ● Ino: inodes ● Jiff: jiffies (i.e. for cpu usage) ● Job: job (as in job queue) ● File: (not 'F' that's farad) ● Load: cpu load ● Metric: a metric line like in the statsd or graphite protocol ● Msg: message (like in message queues) ● Page: page (as in memory segment) ● Pckt: network packet ● Process ● Req: http requests, database queries, etc ● Sock: sockets ● Thread ● Ticket: upload tickets, kerberos tickets, ..
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Carbontagger: ... service=foo.instance=host.target_type=gauge.type=calculation .unit=B 123 1234567890 … Statsdaemon: ..unit=B..unit=B... → unit=B/s ..unit=ms..unit=ms.. → unit=ms stat=mean
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GraphExplorer queries 101 Proxyserver swift server:regex upper_90 unit=ms from <datetime> to <datetime> avg over <timespec>
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Stack .. http_method:(PUT|GET) swift_type=object avg by http_code,server
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transcode unit=jobs/s avg over <time> from <datetime> to <datetime>
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Note: data is obfuscated
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!queue sum by zone:apsoutheast|euwest|us east|uswest|saeast|vimeodf|vimeolv group by state
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Note: data is obfuscated
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Group by zone
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Note: data is obfuscated
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{ server=dfvimeodfs1 plugin=diskspace mountpoint=_srv_node_dfs5 unit=B type=used target_type=gauge }
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server:dfvimeodfs unit=GB type=free srv node
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unit=GB/d group by mountpoint
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unit=Mb/s network dfvimeorpc sum by server
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unit=MB
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Dashboard definition queries = [ 'cpu usage sum by core', 'mem unit=B !total group by type:swap', 'stack network unit=b/s', 'unit=B (free|used) group by =mountpoint' ]
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Conclusion ● Changing information needs (esp. for troubleshooting) ● Complicated information needs → changing & complicated graphs & alerts → PAIN ● ● Selfdescribing metrics ● Standardized metrics ● Native metrics 2.0 ● Structuring metrics → BREEZE
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Conclusion ● metrics can be a lot more useful ● Feedback ● ● GraphExplorer, carbontagger, statsdaemon, ... Standardisation & native metrics 2.0 ?
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