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Проектирование крупномасштабных приложений сбора данных (Josh Berkus)
 

Проектирование крупномасштабных приложений сбора данных (Josh Berkus)

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    Проектирование крупномасштабных приложений сбора данных (Josh Berkus) Проектирование крупномасштабных приложений сбора данных (Josh Berkus) Presentation Transcript

    • FirehoseEngineeringdesigninghigh-volumedata collectionsystems Josh Berkus HiLoad, Moscow October 2011
    • Firehose Database Applications (FDA)(1) very high volume of data input from many producers(2) continous processing of incoming data
    • Mozilla Socorro
    • Upwind
    • Fraud Detection System
    • Firehose Challenges
    • 1. Volume● 100s to 1000s facts/second● GB/hour
    • 1. Volume● spikes in volume● multiple uncoorindated sources
    • 1. Volumevolume always grows over time
    • 2. Constant flowsince data arrives 24/7 …while the user interface can bedown, data collection can neverbe down
    • 2. Constant flow ● cant stop receiving to processETL ● data can arrive out of order
    • 3. Database size● terabytes to petabytes ● lots of hardware ● single-node DBMSes arent enough ● difficult backups, redundancy, migration ● analytics are resource-consumptive
    • 3. Database size● database growth ● size grows quickly ● need to expand storage ● estimate target data size ● create data ageing policies
    • 3. Database size “We will decide on a dataretention policy when we run out of disk space.” – every business user everywhere
    • 4.
    • many components = many failures
    • 4. Component failure● all components fail ● or need scheduled downtime ● including the network● collection must continue● collection & processing must recover
    • solving firehose problems
    • socorro project
    • http://crash-stats.mozilla.com
    • Mozilla Socorro webserverscollectors reports processors
    • socorro data volume● 3000 crashes/minute ● avg. size 150K● 40TB accumulated raw data ● 500GB accumulated metadata / reports
    • dealing with volume load balancers collectors
    • dealing with volumemonitor processors
    • dealing with size data 40TB expandible metadata views 500GB fixed size
    • dealing with component failureLots of hardware ... ● 30 Hbase ● 3 ES servers nodes ● 6 collectors ● 2 PostgreSQL ● 12 processors servers ● 8 middleware & ● 6 load web servers balancers … lots of failures
    • load balancing & redundancy load balancers collectors
    • elastic connections● components queue their data ● retain it if other nodes are down● components resume work automatically ● when other nodes come back up
    • elastic connections crash mover collectorreciever local file queue
    • server management● puppet ● controls configuration of all servers ● makes sure servers recover ● allows rapid deployment of replacement nodes
    • Upwind
    • Upwind ● speed ● wind speed ● heat ● vibration ● noise ● direction
    • Upwind 1. maximize power generation 2. make sure turbine isnt damaged
    • dealing with volumeeach turbine: 90 to 700 facts/secondwindmills per farm: up to 100number of farms: 40+est. total: 300,000 facts/second (will grow)
    • dealing with volume local historian analytic reports storage database local historian analytic reports storage database
    • dealing with volume local historian analytic storage database master database local historian analytic storage database
    • multi-tenant partitioning● partition the whole application ● each customer gets their own toolchain● allows scaling with the number of customers ● lowers efficiency ● more efficient with virtualization
    • dealing with: constant flow and size minute hours days months yearshistorian buffer table table table table minute hours days months historian buffer table table table minute hours days historian buffer table table
    • time-based rollups● continuously accumulate levels of rollup ● each is based on the level below it ● data is always appended, never updated ● small windows == small resources
    • time-based rollups● allows: ● very rapid summary reports for different windows ● retaining different summaries for different levels of time ● batch/out-of-order processing ● summarization in parallel
    • firehose tips
    • data collection must be: ● continuous ● parallel ● fault-tolerant
    • data processing must be: ● continuous ● parallel ● fault-tolerant
    • every component must be able to fail● including the network● without too much data loss● other components must continue
    • 5 tools to use1. queueing software2. buffering techniques3. materialized views4. configuration management5. comprehensive monitoring
    • 4 donts1. use cutting-edge technology2. use untested hardware3. run components to capacity4. do hot patching
    • firehose mastered?
    • Contact● Josh Berkus: josh@pgexperts.com ● blog: blogs.ittoolbox.com/database/soup● PostgreSQL: www.postgresql.org ● pgexperts: www.pgexperts.com● Upcoming Events ● PostgreSQL Europe: http://2011.pgconf.eu/ ● PostgreSQL Italy: http://2011.pgday.it/ The text and diagrams in this talk is copyright 2011 Josh Berkus and is licensed under the creative commons attribution license. Title slide image is licensed from iStockPhoto and may not be reproduced or redistributed. Socorro images are copyright 2011 Mozilla Inc.
    • Firehose Engineering designing high-volume data collection systems Josh Berkus HiLoad, Moscow October 2011 1I created this talk because, for some reason, Ive been working on a bunch of the same kind of data systems lately.
    • Firehose Database Applications (FDA) (1) very high volume of data input from many producers (2) continous processing of incoming data 2I call these “Firehose Database Applications”. Theyre characterized by two factors, and have a lot of things in common regardless of which industry theyre used in.1. These applications are focused entirely on the collection of large amounts of data coming in from many high-volume, producers, and2. they process data into some more useful form, such as summaries, continuously 24 hours a day.Some examples ...
    • Mozilla Socorro 3Mozillas Socorro system, which collects firefox & thunderbird crash data from around the world.
    • Upwind 4Upwind, which collects a huge amount of metrics from large wind turbine farms in order to monitor and analyze them.
    • Fraud Detection System 5And numerous other systems, such as a fraud detection system which receives data from over 100 different transaction processing systems and analyzes it to look for patterns which would indicate fraud.
    • Firehose Challenges 6regardless of how these firehose systems are used, they share the same four challenges, all of which need to be overcome in some way to get the system running and keep it running.
    • 1. Volume ● 100s to 1000s facts/second ● GB/hour 7The first and most obvious is volume. A firehose system is collecting many, many facts per second, which may add up to gigabytes per hour.
    • 1. Volume ● spikes in volume ● multiple uncoorindated sources 8You also have to plan for unpredicted spikes in volume which may be 10X normal.Also your data sources may come in at different rates, and with interruptions or out of order data.But, the biggest challenge of volume always is ...
    • 1. Volume volume always grows over time 9… there is always, always, always more of it than there used to be.
    • 2. Constant flow since data arrives 24/7 … while the user interface can be down, data collection can never be down 10the second challenge is dealing with the all-day, all- week, all-year nature of the incoming data.for example, it is often permissiable for the user interface or reporting system be out of service, but data collection must go on at all times without interruption. Otherwise, data would be lost.this makes for different downtime plans than standard web applications, who can have “read only downtimes”. Firehose systems cant; they must arrange for alternate storage if the primary collections is down.
    • 2. Constant flow ● cant stop receiving to process ETL ● data can arrive out of order 11it also means that conventional Extract Transform Load (ETL) approaches to data warehousing dont work. You cant wait for the down period to process the data. Instead, data must be processed continuously while still collecting.Data is also never at rest; you must be prepared for interruptions and out-of-order data.
    • 3. Database size ● terabytes to petabytes ● lots of hardware ● single-node DBMSes arent enough ● difficult backups, redundancy, migration ● analytics are resource-consumptive 12another obvious challenge is the sheer size of the data.once you start accumulating data 24x7, you will rapidly find yourself dealing with terabytes or even petabytes of data. This requires you to deal with lots of hardware and large clustered systems or server farms, including clustered or distributed databases.this makes backups and redundancy either very difficult or very expensive.performing analytics on the data and reporting on it becomes very resource-intensive and slow at large data sizes.
    • 3. Database size ● database growth ● size grows quickly ● need to expand storage ● estimate target data size ● create data ageing policies 13the database is also growing rapidly, which means that solutions which work today might not work next month. You need to estimate the target size of your data and engineer for that, not the amount of data you have now.you will also have to create a data ageing or expiration policy so that you can eventually purge data and limit the database to some finite size. this is the hardest thing to do because ...
    • 3. Database size “We will decide on a data retention policy when we run out of disk space.” – every business user everywhere 14… users never ever ever want to “throw away” data. No matter how old it is or how infrequently its accessed.This isnt special to firehose systems except that they accumulate data faster than any other kind of system.
    • 4. 15problem #4 is illustrated by this architecture diagram of the Socorro system. Firehose systems have lots and lots of components, often using dozens of servers and hundreds of application instances. In addition to cost and administrative overhead, what having that much stuff means is ...
    • many components = many failures 16… every day of the week something is going to be crashing on you. You simply cant run a system with 90+ servers and expect them to be up all the time.
    • 4. Component failure ● all components fail ● or need scheduled downtime ● including the network ● collection must continue ● collection & processing must recover 17Even when components dont fail unexpectedly, they have to be taken down for upgrades, maintenance, replacement, or migration to new locations and software. But the data collection and processing has to go on while components are unavailable or offline. Even when processing has to stop, components must recover quickly when the system is ready again, and must do so without losing data.
    • solving firehose problems 18So lets talk about how a couple of different teams solved some of these firehose problems.
    • socorro project 19First were going to talk about Mozillas Socorro system. Ill bet a lot of you have seen this Firefox crash screen. The data it produces gets sent to the socorro system at Mozilla, where its collected and processed.
    • http://crash-stats.mozilla.com 20At the end of the processing we get pretty reports like this, which let the mozilla developers know when releases are stable and ready, and where frequent crashing and hanging issues are. You can visit it right now: Its http://crash-stats.mozilla.com
    • 21Theres quite a rich set of data there collecting everything about every aspect of whats making Firefox and other Mozilla products crash, individually and in the aggregate.
    • Mozilla Socorro webservers collectors reports processors 22this is a simplified architecture diagram showing the very basic data flow of Socorro.1. Crashes come in through the internet,2. are harvested by a cluster of collectors3. raw crashes are stored in Hbase4. a cluster of processors pull raw crashes from Hbase process crashes, and write processed crashes and metadata to Hbase and Postgres5. Postgres populates views which are used to support the web interface and reports used internally by mozilla developers.
    • 23a full architecture diagram would look like this one, but even this doesnt show all components.
    • socorro data volume ● 3000 crashes/minute ● avg. size 150K ● 40TB accumulated raw data ● 500GB accumulated metadata / reports 24socorro receives up to 3000 crashes every minute from around the world. each of these crashes comes with 50 to 1000K of binary crash data which needs to be processed.within the target data lifetime of 6-12 months, socorro has accumulated nearly 40 terabytes of raw data and half a terabyte of metadata, views and reports.
    • dealing with volume 25 load balancers collectorshow does Mozilla deal with this large data volume? In one word:parallelism1. crashes come in from the internet2. they hit a set of round-robin load balancers3. the load balancers randomly assign them to one of six crash collectors4. the crash collectors receive the crash information, and then write the raw crash to a 30-server Hbase cluster.
    • dealing with volume 26 monitor processors5. the raw crashes in HBase are pulled out in parallel by twelve processor servers running 10 processor instances each.6. these processors do a lot of work to process the binary crashes and then write the processed crash results to Hbase an the processed crash metadata and summary data to Postgres.7. for scheduling and preventing duplication, the processors are coordinated by a monitor server, which runs a queue and monitoring system backed by Postgres.
    • dealing with size data 40TB expandible metadata views 500GB fixed size 27Why have both Hbase and PostgreSQL? Isnt one database enough?Well, the 30-server Hbase cluster holds 40 terabytes of data. Its good at doing this in a redundant, scalable way and is very fast for pulling individual crashes out of the billion or so which are stored there.However Hbase is very poor at ad-hoc queries, or simple browsing of data over the web. It also often requires downtime for maintenanceSo we use Postgres which holds metadata and holds summary “materialized views” which are used by the web application and the reports to present analytics to users.Postgres cant store the raw data though because it cant easily expand to the sizes needed.
    • dealing with component failure Lots of hardware ... ● 30 Hbase ● 3 ES servers nodes ● 6 collectors ● 2 PostgreSQL ● 12 processors servers ● 8 middleware & ● 6 load web servers balancers … lots of failures 28Of course, with the more than 60 servers were dealing with, something is always going down. Thats simply reality. Some of the worst failures have involved the entire network going out and all components losing communication with each other.This means that we need to be prepared to have failures and to recover from them. How do we do that?
    • load balancing & redundancy 29 load balancers collectorsOne obvious way is to have lots of redundant components for each role in the application. As we already saw, for incoming data there is more than one load balancer and are 6 collectors. This means that we can lose several machines and still be receiving user data, and that in catastrophic failure we only lose an minority of the data.
    • elastic connections ● components queue their data ● retain it if other nodes are down ● components resume work automatically ● when other nodes come back up 30A more important strategy weve learned through hard experience is the requirement to have “elastic connections”. That is, each component is prepared to lose communication with the components on either side of it. They queue data trough several mechanisms, and resume work when the next component in line comes back up.
    • elastic connections crash mover collector reciever local file queue 31For example, lets look at the collectors. They receive data from the web and write to Hbase. But its more complex than that.1. The collectors can lose communication to Hbase for a variety of reasons. It can be down for maintenance, or there can be network issues.2. The crashes are received by receiver processes3. which write them to local file storage in a primitive, filename-based queue.4. A separate “crashmover” process looks for files in the queue5. When Hbase is available, the crashmover saves the files to Hbase.
    • server management ● puppet ● controls configuration of all servers ● makes sure servers recover ● allows rapid deployment of replacement nodes 32Another thing which has been essential to managing component failure is comprehensive server management through Puppet. Having servers under central management means that we can make sure services are up and configured correctly when they are supposed to be. And it also means that if we permanently lose nodes we can replace them quickly with identically configured nodes.
    • Upwind 33lets move on to a different team and different methods of facing firehose challenges. Upwind.
    • Upwind ● speed ● wind speed ● heat ● vibration ● noise ● direction 34power-generating wind turbines have onboard computers which produce a lot of data about the status and operation of the wind turbine Upwind takes that data, collects it, summarizes it and turns it into graphical summary reports for customers who own wind farms.
    • Upwind 1. maximize power generation 2. make sure turbine isnt damaged 35the customers need this data for two reasons:1. wind turbines are very expensive and they need to make sure theyre maximizing power generation from them.2. to intervene and keep the turbines from catching fire or otherwise becoming permanently damaged.
    • dealing with volume each turbine: 90 to 700 facts/second windmills per farm: up to 100 number of farms: 40+ est. total: 300,000 facts/second (will grow) 36turbines come with a lot of different monitors, between 90 and 700 of them depending on the model. each of these monitors produces a reading every second.an individual “wind farm” can have up to 100 turbines, and Upwind expects more than 40 farms to make use of the service once its fully deployed. Plus they want to get more customers.this means an estimated 300,000 metrics per second total by next year.
    • dealing with volume local historian analytic reports storage database local historian analytic reports storage database 37Upwind chose a very different route than Mozilla for dealing with this volume. Lets explain the data flow first:1. Wind farms write metrics to local storage, which can hold data for a couple hours.2. This data is then read by an industry-specific application called the historian which interprets the raw data and stores each second of data.3. that data is then read from the historian by Postgres4. which produces nice summaries and analytics for the reports.As it turns out, data is not shared between wind farms most of the time. So Upwind can scale simply by adding a whole additional service tool chain for each windfarm it brings online.
    • dealing with volume local historian analytic storage database master database local historian analytic storage database 38In the limited cases where we need to do inter-farm reports, we will have a master Postgres node which will use pl/proxy and/or foreign data wrappers to summarize data from multiple wind farms.
    • multi-tenant partitioning ● partition the whole application ● each customer gets their own toolchain ● allows scaling with the number of customers ● lowers efficiency ● more efficient with virtualization 39this is whats known as multi-tenant partitioning, where you give each customer, or “tenant”, a full stack of their own. Its not very resource-efficient, but it does easily scale as you add customers, and eliminates the need to use complex clustered storage or databases.cloud hosting and virtualization is also making this strategy easier to manage.
    • dealing with: constant flow and size minute hours days months years historian buffer table table table table minute hours days months historian buffer table table table minute hours days 40 historian buffer table tableIn order to deal with the constant flow of data and the large database size, we continously produce a series of time-based summaries of the data:1. a buffer holding a several minutes of data. this also accounts for lag and out-of-order data.2. hour summaries 3. day summaries4. month summaries 5. annual summaries
    • time-based rollups ● continuously accumulate levels of rollup ● each is based on the level below it ● data is always appended, never updated ● small windows == small resources 41For efficiency, each level builds on the level beneath it. This means that no analytic operation ever has to work with a very large set, an so can be very fast.Even better, the time-based summaries can be run in parallel, so that we can make maximum use of the processors on the server.
    • time-based rollups ● allows: ● very rapid summary reports for different windows ● retaining different summaries for different levels of time ● batch/out-of-order processing ● summarization in parallel 42this allows us to have a set of “views” which summarize the data at the time windows users look at. This means almost “instant” reports for users.
    • firehose tips 43based on my experiences with several of these firehose projects, heres some general rules for building this kind of an application
    • data collection must be: ● continuous ● parallel ● fault-tolerant 44data collection has to be 24x7. It must be done in parallel by multiple processes/servers, and above all must be fault-tolerant.
    • data processing must be: ● continuous ● parallel ● fault-tolerant 45data processing has to be built the same way.
    • every component must be able to fail ● including the network ● without too much data loss ● other components must continue 46the lesson most people dont seem to be prepared for is that every component must be able to fail without permanently downing the system, or causing unacceptable data loss.
    • 5 tools to use 1. queueing software 2. buffering techniques 3. materialized views 4. configuration management 5. comprehensive monitoring 47heres some classes of tools you should be using in any firehose application.queuing and buffering for fault-tolerancematerialized views to deal with volume and sizeconfiguration management and comprehensive monitoring to keep the system up.
    • 4 donts 1. use cutting-edge technology 2. use untested hardware 3. run components to capacity 4. do hot patching 48some things you will be tempted to do and shouldnt* dont use cutting edge hardware or software because its not reliable and will lead to repeated unacceptable downtimes* dont put hardware into service without testing it first beause it will be unreliable or perform poorly, dragging the system down* never run components or layers of your stack at capacity, because then you have no headroom for when you have a surge in data* and never do hot patching of production services because you cant manage it and it will lead to mistakes and taking the system down.
    • firehose mastered? 49so now that we know how to do it, its time for you to build your own firehose applications. its challenging and fun.
    • Contact ● Josh Berkus: josh@pgexperts.com ● blog: blogs.ittoolbox.com/database/soup ● PostgreSQL: www.postgresql.org ● pgexperts: www.pgexperts.com ● Upcoming Events ● PostgreSQL Europe: http://2011.pgconf.eu/ ● PostgreSQL Italy: http://2011.pgday.it/ The text and diagrams in this talk is copyright 2011 Josh Berkus and is licensed under the creative commons attribution license. Title slide image is licensed from iStockPhoto and may not be reproduced or redistributed. Socorro images are copyright 2011 Mozilla Inc. 50contact and copyright information.Questions?