Time Series Data Storage in MongoDB

transcript

Sunday, July 24, 2011

ajackson@

skylineinnovations.com

a tale of rapid prototyping, data

warehousing, solar power, an architecture

designed for data analysis at “scale”...and arduinos!

So here’s what i’d like to talk about: Who we are, how we got started, and most importantly, how we’ve been able to use MongoDB to help us. We’re not a traditional startup -- and while i know that this is not a “startups” talk, but a Mongo one, i’d like to show how Mongo’s flexible nature really helped us as a business, and how Mongo specifically has been a good choice for us as we build some of our tools. Here are some themes:

Scaling

Mongo has come to have a pretty strong association with the word “scaling.”

Scaling is a word we throw around a lot, and it almost always means “software performance, as inputs grow by orders of magnitude.”

But scaling also means performance as the variety of inputs increases. I’d argue that it’s scaling to go from 10 users to 10,000, and it’s also scaling to go from ten ‘kinds’ of input to a hundred.

There’s another word for this.

ScalingFlexibility

Particularly when you scale in the real world, you start to find that it’s complicated and messy and entropic in ways that software isn’t always equipped to handle. So for us, when we say “mongo helps us scale”, we don’t necessarily mean scaling to petabytes of data. We’ll come back to them as well.

Business-first development

This generally means flexibile, lightweight processes. Things that become fixed & unchangable quickly become obsolete and sad :’(

When Does “Context”

become “Yak Shaving”?

When i read new things or hear about new stuff, I’m always trying to put it in context. So, sometimes i put too much context in my talks :( To avoid it, I sometimes go a little too fast over the context that *is* important. So please stop me to ask questions! Also, the problem domain here is a little different than what we might be used to, so bear with me as we go into plumbing & construction.

Preliminaries

Est. 8/2009Sunday, July 24, 2011

Project Development+

Technology

“Project Development”Sunday, July 24, 2011

finance, develop, and operate renewable energy and efficiency

installations, for measurable, guaranteed savings.

finance, develop, and operate renewable energy

and efficiency installations, for measurable, guaranteed savings.

We’ll pay to put stuff on your roof, and we’ll keep it at its maximally awesome.

finance, develop, and operate renewable energy and

efficiency installations, for measurable, guaranteed savings.

Right now, this means solar thermal, more efficient lighting retrofits, and maybe HVAC.

finance, develop, and operate renewable energy and efficiency installations, for measurable,

guaranteed savings.

So, here’s the interesting part. Since we put stuff on your roof for free, we need to get that money back. What we do is, we’ll charge you for the energy that it saved you, but, here’s the twist. Other companies have done similar things, where they say “we’ll pay for a system/retrofit/whatever, and you’ll agree to pay us an arbitrary number, and we say you’ll get savings, but you won’t actually be able to tell, really.” That always seemed sketchy to us. So, we actually measure the performance of this stuff, collect the data, and guarantee that you save money.

(not webapps)

Topics not covered:

• Why solar thermal?

• Why hasn’t anyone else done this before?

• Pivots? Iterations?

• What’s the market size?

• Funding? Capital structures?

• Wait, how do you guys make money?

Oh, right, this isn’t a startup talk. But feel free to ask me these later!

Solar Thermal in Five Minutes

( mongo next, i promise! )

Municipal =>

Roof=>

Tank=>

CustomerSunday, July 24, 2011

Relevant Data to Track

Temperatures (about a dozen)

Flow Rates(at least two)

Parallel data streams(hopefully many)

e.g., weather data, insolation data. It’d be nice if we didn’t have to collect it all ourselves.

how much data?

20 data points @ 4 bytes

1 minute intervals

at 1000 projects (I wish!)

for 10 years

80 * 60 * 24 * 365 * 10 * 1000 = 400 GB?

...not much, really, “in the raw”

unfortunately, we can’t really store it with maximal efficiency, because of things like timestamps, metadata, etc., but still.

I hope this provides enough context on the business problems we’re trying to solve. It looks like we’ll need a data pipeline, and we’ll need one fast.

We’ve got data that we’ll need to use to build, monitor, and monetize these energy technologies. Having worked at other smart grid companies before, I’ve seen some good data pipelines and some bad data pipelines. I’d like to build a good one. The less stuff i have to build, the better.

As i do some research, i find that a lot of these data pipelines have a few well-defined areas of responsibility.

Acquisition, Storage, Search,

Retrieval, Analytics.

These should be self explanatory. What’s interesting is that not only are most of the end-users of the system analysts, interested in analyzing, but that most systems seem to be designed for the other functionality. More importantly, they’re not very well decoupled: by the time the analysts get to start building tools, the design decisions from the beginning are inextricable from the systems that came before.

Retrieval, Analytics. <= Users are here

} Designed for these

Retrieval, Analytics.

It’s important to remember that, while you can’t get good analytics without the other stuff, the analytics is where almost all of the value is! Search & retrieval are approaching “solved”

Retrieval, Analytics. <= Users are here

Business value is here!

} Designed for these

It’s important to remember that, while you can’t get good analytics without the other stuff, the analytics is where almost all of the value is! Search & retrieval are approaching “solved”

so, here’s how i started thinking about things. This is a design diagram from the early days of the company.

easy, python, no problem. There are some interesting topics here, but they’re not mongoDB related. I was pretty sure i knew how to build this part, and i was pretty sure i knew what the data would look like.

This part was also easy -- e-mail reports, csvs, maybe some fancy graphs, possibly some light webapps for internal use. These would be dictated by business goals first, but the technological questions were straightforward.

Here was the real question.

What would be some use cases of an analyst having a good experience look like? What would they expect the tools to do?

Now we can think about what the data

looks like

So, let’s think about what this data looks like, how it’s structured and what it is. Then, after that, we can look at what the best ways to organize it for future usefulness.

Time,municipal water in T,solar heated water out T,solar tank bottom taped to side,solar tank top taped to side,array in/out,array in/out,tank room ambient t,array supply temperature,array return temperature,solar energy sensor,customer flow meter,customer OIML btu meter,solar collector array flow meter,solar collector array OIML btu meter,Cycle CountTue Mar 9 23:01:44 2010,14.7627064834,53.7822899383,12.1642527206,51.1436001456,6.40476190476,8.9582972583,22.6857033228,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333458Tue Mar 9 23:02:44 2010,14.958038343,53.764889193,12.1642527206,51.0925345058,6.40476190476,8.85184138407,22.5716100982,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333462Tue Mar 9 23:03:45 2010,15.1145934976,53.6986641192,12.1642527206,50.8692901812,6.40476190476,8.78519002979,22.5673674246,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333462Tue Mar 9 23:04:45 2010,15.2512207824,53.5955190752,12.1642527206,50.8293877551,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333468Tue Mar 9 23:05:45 2010,15.3690229715,53.5534492867,12.1642527206,50.8293877551,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333471Tue Mar 9 23:06:46 2010,15.5253261193,53.5534492867,12.1642527206,50.8658228816,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333472Tue Mar 9 23:07:46 2010,15.6676270005,53.5534492867,12.1642527206,50.9177829276,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.293277114,0.0,0.0,0.0,0.0,0.0,333472Tue Mar 9 23:08:47 2010,15.7915083121,53.4761516976,12.1642527206,50.8398031014,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.1826467404,0.0,0.0,0.0,0.0,0.0,333477Tue Mar 9 23:09:47 2010,15.9763741003,53.693428918,12.1642527206,50.7859446809,6.40476190476,8.78519002979,22.5461357574,24.0728390462,22.1782915595,0.0,1.0,0.0,0.0,0.0,333581Tue Mar 9 23:10:47 2010,16.1650984572,54.0547534088,12.1642527206,50.725,6.40476190476,8.78519002979,22.4544906773,24.0728390462,22.1782915595,0.0,0.0,0.0,0.0,0.0,333614

Time series?

TIME SERIES DATA

So what is time series data?

Features, Over Time

multi-dimensional features. What’s fun in a business like this is that we’re not really sure what the features we study will be. -- Flexibility callout

Features, Over Time

Thing(Feature vector, v)

Features, Over Time

Thing(Feature vector, v)

A couple of ideas:sampling rates. “regularity”. “completeness”analog vs. digitalinstantaneous vs. cumulative (tradeoffs)

tn tn+1

Finding known interesting ranges (definitely the most common)

tn tn+1

Finding known interesting ranges (definitely the most common)

t t’ etc.Sunday, July 24, 2011

Using features to find interesting ranges.

These two ways to look for things should inform our design decisions.

Thresholds

(more complicated stuff can be thought of as transformations...)

e.g., frequency analysis, wavelets, whatever.

At this point, I go off and do a bunch of research on existing technologies. I really hate reinventing the wheel, and we really don’t have the manpower.

Time series specific tools

Scientific tools & libraries

Traditional data-warehousing approaches

So, these were some of the options i looked at. I want to quickly point out why i eliminated the first two classes of tools.

Time series specific tools

RRDtool -- Round Robin Database

There’s really surprisingly few of these. One of the best is the RRDtool. It’s pretty sweet, and i highly recommend it. Unfortunately, it’s really designed for applications that are highly regular, and that are already pretty digital, for instance, sampling latencies, or temperatures in a datacenter. It’s not really good for unreliable sensors, nor is it really designed for long term persistance. It also has a really high lock-in, with legacy data formats, etc. Don’t get me wrong, it’s totally rad, but i didn’t think it was for us.

Scientific tools & libraries

e.g., PyTables

Pretty cool, but not many of these were mature & ready for primetime. Some that were, like PyTables, didn’t really match our business use-case.

Traditional data-warehousing approaches

So, these were some of the options i looked at. I want to quickly point out why i eliminated the first two classes of tools. [...]. That leaves us with the traditional approaches. This represents a pretty well established field, but very few of the tools are free, lightweight, and mature.

Enterprise buzzwords(Just google for OLAP)

But the biggest idea i learned is that most data warehousing revolves around the idea of a “fact table”. They call it a “multidimensional OLAP cube”, but basically it exists as a totally denormalized SQL table.

“Measures” and their

“Dimensions”

(or facts)

pretty neat!Sunday, July 24, 2011

“how elegant!”

in practice...

(from “How to Build OLAP Application Using Mondrian + XMLA + SpagoBI”)

to which the only acceptable response is:

ha! Yeah right.

Time series are not relational!Sunday, July 24, 2011

even extracted features are not inherently relational!

Also: you don’t know what you’re looking for, you don’t know when you’ll find it, you won’t know when you’ll have to start looking for something different.Why would you lock yourself into a schema?

We don’t know what we’ll want to know.

We won’t know what we want to know. Not only are we warehousing time-series of multidimensional feature vectors, we don’t even know the dimensions we’ll be interested in yet!

natural fit for documents

This makes a schema-less database a natural fit for these sorts of things. Think about all the alter-table calls i’ve avoided...

"_id" : { "install.name" : "agni-3501", "timestamp" : ISODate("2010-08-06T00:00:00Z"), "frequency" : "daily" }, "measures" : { "total-delta" : -85.78773442284201, "Energy Sold" : 450087.1186574721, "Generation" : 57273.159890170136, "consumed-delta" : 12.569841951556597, "lbs-sold" : 18848.4, "Gallons Loop" : 740.5, "Coincident Usage" : 400, "Stored Energy" : 1306699.6439737699, "Gallons Sold" : 2260, "Energy Delivered" : 360069.6949259777, "Total Usage" : -1605086.7261496289, "Stratification" : -4.905050370111111, "gen-delta-roof" : 4.819865854785763, "lbs-loop" : 6520.1025 }, "day_of_year" : 218, "day_of_week" : 4, "month" : 8, "week_of_year" : 31, "install" : { "panels" : 32, "name" : "agni-3501", "num_files" : "3744", "heater_efficiency" : 0.8, "storage" : 1612, "install_completed" : ISODate("2010-08-06T00:00:00Z"), "logger_type" : "emerald", "_id" : ObjectId("4d2905536edfdb022f000212"), "polysun_proj" : [ 22863.7, 24651.7, 30301.7, 30053.5, 29640.5, 27806.4, 27511, 28563.1, 27840.7, 26470.9, 21718.9, 19145.4 ], "last_seen" : "2011-01-08 05:26:35.352782" }, "year" : 2010, "day" : 6

isn’t this better?

“measures”

“dimensions”

...right?

measures & dimensions. This would be a nice, clean division, except that it isn’t. Frequently we’ll look for measures by other measures -- i.e., each measure serves as a dimension.

...actually, not a good model.

The line gets pretty blurry, in practice. Multi-dimensional vectors mean every measure provides another dimension.Anyway!

How do we build these quickly & efficiently?

the goal: good numbers!

Remember, the goal here is to make it easy for analysts to get comparable numbers, so when i ask for the delivered energy for one system, compared to the delivered energy from another, i can just get the time-series data, without having to worry about if sensors changed, when the network was out, when a logger was replaced with another one, etc.

So, the OLTP layer serving as our inputs essentially serves up timestamped data as CSV series. It doesn’t really provide a lot of intelligence, and is basically the raw numbers

from rowsto columns

So, most of what our pipeline does is turn things from rows to columns, in a flexible, useful way. I’m gonna walk through that process, quickly.

Let’s just look at one

Time,municipal water in T,solar heated water out T,solar tank bottom taped to side,solar tank top taped to side,array in/out,array in/out,tank room ambient t,array supply temperature,array return temperature,solar energy sensor,customer flow meter,customer OIML btu meter,solar collector array flow meter,solar collector array OIML btu meter,Cycle CountTue Mar 9 23:01:44 2010,14.7627064834,53.7822899383,12.1642527206,51.1436001456,6.40476190476,8.9582972583,22.6857033228,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333458Tue Mar 9 23:02:44 2010,14.958038343,53.764889193,12.1642527206,51.0925345058,6.40476190476,8.85184138407,22.5716100982,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333462Tue Mar 9 23:03:45 2010,15.1145934976,53.6986641192,12.1642527206,50.8692901812,6.40476190476,8.78519002979,22.5673674246,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333462Tue Mar 9 23:04:45 2010,15.2512207824,53.5955190752,12.1642527206,50.8293877551,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333468Tue Mar 9 23:05:45 2010,15.3690229715,53.5534492867,12.1642527206,50.8293877551,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333471Tue Mar 9 23:06:46 2010,15.5253261193,53.5534492867,12.1642527206,50.8658228816,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333472Tue Mar 9 23:07:46 2010,15.6676270005,53.5534492867,12.1642527206,50.9177829276,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.293277114,0.0,0.0,0.0,0.0,0.0,333472Tue Mar 9 23:08:47 2010,15.7915083121,53.4761516976,12.1642527206,50.8398031014,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.1826467404,0.0,0.0,0.0,0.0,0.0,333477Tue Mar 9 23:09:47 2010,15.9763741003,53.693428918,12.1642527206,50.7859446809,6.40476190476,8.78519002979,22.5461357574,24.0728390462,22.1782915595,0.0,1.0,0.0,0.0,0.0,333581Tue Mar 9 23:10:47 2010,16.1650984572,54.0547534088,12.1642527206,50.725,6.40476190476,8.78519002979,22.4544906773,24.0728390462,22.1782915595,0.0,0.0,0.0,0.0,0.0,333614

row-major data

“Functional”

class Mass(BasicMeasure): def __init__(self, density, volume): ...

self._result_func = functools.partial( lambda data, density, volume: density * volume(data) density=density, volume=volume)

def __call__(self, data): return self._result_func(data)

quasi-functional classes that describe how to calculate a value from data.

"_id" : { "install.name" : "agni-3501", "timestamp" : ISODate("2010-08-06T00:00:00Z"), "frequency" : "daily" }, "measures" : { "total-delta" : -85.78773442284201, "Energy Sold" : 450087.1186574721, "Generation" : 57273.159890170136, "consumed-delta" : 12.569841951556597,

#pseudocodeclass LoopEnergy(BasicMeasure): def __init__(self, heat_cap, delta, mass): ... def result_func(data): return self.delta(data) * self.mass(data) * self.heat_cap self._result_func = result_func

def __call__(self, data): return self._result_func(data)

A formula:

E = ∆t × F

For each install, for each chunk of data:

apply all known formulas to get values

make some convenience keys (e.g., day_of_year)

stuff it in mongo

Then, map/reduce to whatever dimensionalities you’re interested in: e.g., downsampling.

Creating a Cube

Here’s some pseudocode for how to make a cube of multidimensional data.So, what’s the payoff?

How much water did[x] use, monthly?

> db.facts_monthly.find({"install.name": [foo]}, {"measures.Gallons Sold": 1}).sort({“_id”: 1})

Complicated analytical queries can be boiled down to nearly single line mongo-queries. Here’s some examples:

What were our highest production days?

> db.facts_daily.find({}, {“measures.Energy Sold”: 1}).sort({_measures.Energy Sold”: -1})

How does the distribution of [x] on the weekend compare to its distribution on the weekdays?

> weekends = db.facts_daily.find({"day_of_week": {$in: [5,6]}})> weekdays = db.facts_daily.find({"day_of_week": {$nin: [5,6]}})> do stuff

What’s the production of installs north of a certain latitude, with a certain class of panel, on Tuesdays?

For hours where the average delivered temperature delta was above [x], what was our generation efficiency?

Normalize by number of panels? (map/reduce)

Normalize by distance from equinox? (map/reduce)

...etc.

• Building a cube can be done in parallel

• Map/reduce is an easy way to think about transforms.

• Not maximally efficient, but parallelizes on commodity hardware.

Some advantages.re #3 -- so what? It’s not a webapp.

mongoDB:The future of enterprise

business intelligence.(they just don’t know it yet)

So, here’s my thesis:document-databases are far superior to relational databases for business intelligence cases. Not only that, but mongoDB and some common sense lets you replace multimillion dollar IBM-level enterprise solutions with open-source awesomeness. All this in a rapid, agile way.

Lastly...

Mongo expands in an organization.

it’s cool, don’t fight it. Once we started using it for our analytics, we realized there was a lot of other schema-loose data that we could use it for -- like the definitions of the measures themselves, or the details about an install, etc., etc.

Final Thoughts

Ok, i want to close up with a few jumping-off points.

“Business Intelligence”no longer requires

megabucks

Flexible tools means business responsiveness

should be easy

“Scaling” doesn’t just mean depth-first.

businesses grow deep, in the sense of adding more users, but they also grow broad.

Questions?

Epilogue

Quest for Logging Hardware

This’ll be easy!This is such an obvious and well

explored problem space, i’m sure we’ll be able to find a

solution that matches our needs without breaking the bank!

Shopping List!16 temperature sensors

4 flow sensorsmaybe some miscellaneous ones

internet backhaulno software/data lock in.

Conventions FTW!

And since we’ve walked a couple convention floors and product catalogs from major industrial supply vendors, i’m sure it’s in

here somewhere!

derp derp “internet”?

I’m sure there’s a reason why all of these loggers have to connect

via USB...Pace Scientific XR5:

8 analog3 pulse

ONE MBno internet?

$500?!?

yay windows?...and require proprietary (windows!) software or

subscription plans that route my data through their servers

(basically all of them!)

Maybe the gov’t can help!

Perhaps there’s some kind of standard that the governments

require for solar thermal monitoring systems to be

eligible for incentives or tax credits.

Vive la France!An obscure standard by the

Organisation Internationale de Métrologie Légale

appears! Neat!

A “Certified”Logger

two temperature sensorsone pulse

no increase in accuracyno data backhaul -- at all

...what’s the price?

$1,000

Hmm...I can solder, and arduinos are

pretty cheap

It’s on!

arduino + netbook!Sunday, July 24, 2011

TL; DR: Existing loggers

are terrible.

Also, existing industries aren’t really ready for rapid prototyping and its destructive effects.

• http://www.flickr.com/photos/rknight/4358119571/

• http://4.bp.blogspot.com/_8vNzwxlohg0/TJoUWqsF4LI/AAAAAAAABMg/QaUiKwCEZn8/s320/turtles-all-the-way-down.jpg

• http://www.flickr.com/photos/rhk313/3801302914/

• http://www.flickr.com/photos/benny_lin/481411728/

• http://spagobi.blogspot.com/2010_08_01_archive.html

• http://community.qlikview.com/forums/t/37106.aspx