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manage new post logout login blog of paul ingles google bigquery: copying data between projects and locations i’ve worked on a tool called big replicate that we’ve open-sourced: it helps copy, and synchronise, data between different datasets in projects in google bigquery . i’m a huge fan of google’s bigquery product: a large-scale, and affordable, hosted data-warehouse. data is stored in tables, with sets of tables stored within a dataset (all of which are part of a google cloud project). datasets can be stored in a number of different locations: the us, eu and asia. we use bigquery most often with our google analytics data. google provide a daily export of all hit data as part of their paid-for version of the product. but this data is automatically placed in a dataset based in the us. if we want to connect it to our crm data, for instance, that’s hosted within the eu then we need to copy data into the eu also. we’ve written a tool that automatically synchronises data from one dataset into another. it works in such a way that it’s possible to copy data between both datasets within the same project, or datasets in entirely different projects. further, datasets can be in different locations (allowing data to be copied from the us to the eu, for instance). the code is available on github: https://github.com/uswitch/big-replicate , with binaries also available . for example, to synchronise any google analytics session tables: the tool will find all tables that exist in the source dataset and not yet in the destination dataset, orders them lexicographically in reverse order by name (so table foo_20160712 is prioritised before foo_20160711 ). the top --number tables are copied. we use this to help migrate very large datasets over time, where we generally only need the more recent data immediately. upvote upvoted 0 tweet 1 response // posted multiplexing work reliably and efficiently with state machines and core.async this all started with a tweet and brief exchange with a friend and ex-colleague: i *love* csp and state machines. have a performant + reliable means of working through a bunch of ops that are multiplexed v pleasantly — paul ingles (@) march 30, 2015 this post describes some work we did recently that i’m pretty happy with: we model the execution of independent pieces of work as state machines which are executed concurrently by multiple core.async processes with state communicated over channels. modeling with state machines helps flatten call chain complexity and makes retrying/recovering from error states trivial: we just try to apply the same transition to the same state again. in short, we've improved both throughput and reliability. context specifically our problem was: connect to a reporting api and download a report transform the report, converting some values between units, currencies etc. write the report out to s3 (ultimately to be ingested into our redshift cluster) it’s a pretty simple problem but when we’re downloading thousands of reports every day its likely that we’ll come across intermittent problems; mostly network errors connecting to the apis or we’ll be rate throttled etc. we started simple, making the most of the environment our system ran in. our original code was similar to this: the reporting api lets us download aggregate information for a day and client. ensuring processes completed was the responsibility of a supervisor process . although this was beautifully simple for the incremental work it was incredibly inefficient when running large imports: our unit of work was all steps needed to download, process and upload a report. if any step failed we could only retry the whole. even worse, if we were processing hundreds or thousands of reports together any failure would terminate and prevent all subsequent reports. we could unpick progress and change some command-line options to avoid doing too much again but it's painful. handling errors was slow and painful. if our download request was rate limited we'd have to back-off; operations were globally serial though so any delay sleeps everything. state machines, core.async and concurrent execution instead of weaving function invocations together we can model the problem as a set of independent state machines- each moving independently. our transitions are pretty similar to the list we mentioned at the beginning: :downloadable -> :uploadable -> :completed . each report (a combination of client and day) will progress from :downloadable to :completed . the local order of these operations is important (we can’t upload the report before we’ve downloaded it) but the global order isn’t important- it doesn’t matter whose report we process first. it's also important to note that our operations are idempotent- it also doesn't matter if we download/try to download the same report multiple times, likewise with the upload. at each step our code will use the machine’s :state to determine which transition to apply. if we encounter an error whilst performing a transition we attach :error to the state with the exception, letting the machine retry the operation. our final code looks pretty close to the following: we create the states-ch channel to communicate each state of each machine (i.e. our unit of work). processes are started to progress each of the state machines. once a machine’s :state is :completed the final state is put on the completed channel (this helps us know when all work is finished). states are read from the states-ch channel and the appropriate operation performed. the result of each operation is the new state . we use thread to perform the operation and return a channel we can read the result from. if an operation causes an exception to be raised it’s caught and we associate the exception value to the state map. after a small delay we put the state map back into the states channel for the operation to be attempted again. modeling the problem with state machines and implementing it with core.async gives a few nice properties: operations are transparent. it’s easy to see what’s going on at any point in time. failure is isolated and easily retryable: all values needed to perform an operation are held in the state maps so its really just a case of applying (step state) again. we use core.async’s timeout channel to defer the retry operation, letting us switch to a different op first. overall throughput is increased. if we need to defer an operation we proceed with something else. even on my 4-core laptop it results in ~6x greater throughput. in short, we’re able to process many more reports concurrently than we were able to with our initial ‘dumb’ implementation and the code, i think, is vastly more readable than the nested error handling we used to have. tags statemachine csp core.async clojure upvote upvoted 0 tweet 3 responses // posted syslogger: forward syslog to apache kafka our team rewrote a small but key component within our data infrastructure earlier this year: a daemon to forward log data to apache kafka . last week i made the repository public and thought it would be worth mentioning. our log data can be extremely valuable: helping us understand how users interact with our products and letting us measure the service we provide. lots of services and daemons produce logs that we can usefully look at together to better understand the whole: nginx, varnish, our application services and more. the problems with log files most of our applications and services would write their logs to files on disk. it’s convenient (to a point) when you can just tail a file to see what’s happening. it’s ok when you’re administering a handful of machines with perhaps a few processes but it breaks when we start talking about the composition of systems. this is for a few reasons (nothing new of course but worth repeating): aggregating log files across your servers is important. no person/process is an island after all. by extension its necessary to replicate log files to a centralis

URL analysis for oobaloo.co.uk

http://oobaloo.co.uk/tag/bandits
http://oobaloo.co.uk/tag/neo4j
http://oobaloo.co.uk/tag/hdfs
http://oobaloo.co.uk/tag/statemachine
http://oobaloo.co.uk/tag/clojure
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http://oobaloo.co.uk/amazon-redshift-plus-r-analytics-flow
http://oobaloo.co.uk/tag/csp
http://oobaloo.co.uk/multiplexing-work-reliably-and-efficiently-with-state-machines-and-core-dot-async
http://oobaloo.co.uk/tag/golang
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http://oobaloo.co.uk/kafka-for-uswitchs-event-pipeline
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Whois Information

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Domain name:
oobaloo.co.uk

Data validation:
Nominet was able to match the registrant's name and address against a 3rd party data source on 10-Dec-2012

Registrar:
eNom LLC [Tag = ENOM]
URL: http://www.enom.com

Relevant dates:
Registered on: 04-Jan-2002
Expiry date: 04-Jan-2019
Last updated: 06-Dec-2017

Registration status:
Registered until expiry date.

Name servers:
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ns2.dnsimple.com
ns3.dnsimple.com
ns4.dnsimple.com

WHOIS lookup made at 14:15:11 27-Jul-2018

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  REFERRER http://www.nominet.org.uk

  REGISTRAR Nominet UK

SERVERS

  SERVER co.uk.whois-servers.net

  ARGS oobaloo.co.uk

  PORT 43

  TYPE domain

DOMAIN

SPONSOR
eNom LLC [Tag = ENOM]
URL: http://www.enom.com
Relevant dates:

  CREATED 04-Jan-2002

  CHANGED 06-Dec-2017

STATUS
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NSERVER

  NS1.DNSIMPLE.COM 162.159.24.4

  NS2.DNSIMPLE.COM 162.159.25.4

  NS3.DNSIMPLE.COM 162.159.26.4

  NS4.DNSIMPLE.COM 162.159.27.4

  NAME oobaloo.co.uk

DISCLAIMER
This WHOIS information is provided for free by Nominet UK the central registry
for .uk domain names. This information and the .uk WHOIS are:
Copyright Nominet UK 1996 - 2018.
You may not access the .uk WHOIS or use any data from it except as permitted
by the terms of use available in full at https://www.nominet.uk/whoisterms,
which includes restrictions on: (A) use of the data for advertising, or its
repackaging, recompilation, redistribution or reuse (B) obscuring, removing
or hiding any or all of this notice and (C) exceeding query rate or volume
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