Time Series Data Storage in MongoDB

ajackson
@
skylineinnovations.com

Sunday, July 24, 2011

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.

Scaling
Flexibility


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. 8/2009

Project Development
+
Technology


“Project Development”

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.

renewable energy and
efﬁciency installations, for
measurable, guaranteed savings.


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

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
=>
Customer

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.

Acquisition,
Storage,
Search,
Retrieval,
} Designed for these

Analytics. <= Users are here



Acquisition,
Storage,
Search,
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”

Acquisition,
Storage,
Search,
Retrieval,
} Designed for these

Analytics. <= Users are here
Business value is here!



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 series?
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 Count
Tue Mar 9 23:01:44 2010,14.7627064834,53.7822899383,12.1642527206,51.1436001456,6.40476190476,8.9582972583,22.6857033228,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333458
Tue Mar 9 23:02:44 2010,14.958038343,53.764889193,12.1642527206,51.0925345058,6.40476190476,8.85184138407,22.5716100982,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333462
Tue Mar 9 23:03:45 2010,15.1145934976,53.6986641192,12.1642527206,50.8692901812,6.40476190476,8.78519002979,22.5673674246,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333462
Tue Mar 9 23:04:45 2010,15.2512207824,53.5955190752,12.1642527206,50.8293877551,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333468
Tue Mar 9 23:05:45 2010,15.3690229715,53.5534492867,12.1642527206,50.8293877551,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333471
Tue Mar 9 23:06:46 2010,15.5253261193,53.5534492867,12.1642527206,50.8658228816,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333472
Tue Mar 9 23:07:46 2010,15.6676270005,53.5534492867,12.1642527206,50.9177829276,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.293277114,0.0,0.0,0.0,0.0,0.0,333472
Tue Mar 9 23:08:47 2010,15.7915083121,53.4761516976,12.1642527206,50.8398031014,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.1826467404,0.0,0.0,0.0,0.0,0.0,333477
Tue Mar 9 23:09:47 2010,15.9763741003,53.693428918,12.1642527206,50.7859446809,6.40476190476,8.78519002979,22.5461357574,24.0728390462,22.1782915595,0.0,1.0,0.0,0.0,0.0,333581
Tue 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
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)

Time
(t)


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


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

tn tn+1


Finding known interesting ranges (deﬁnitely the most common)

t t’ etc.

Using features to ﬁnd interesting ranges.

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

y

t t’ etc.



y
Thresholds
y’

t t’ etc.



(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!

“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!

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?

"_id" : {
"measures" : {
"total-delta" : -85.78773442284201,
"Energy Sold" : 450087.1186574721,
"Generation" : 57273.159890170136,
"consumed-delta" : 12.569841951556597,
"lbs-sold" : 18848.4,
"Stored Energy" : 1306699.6439737699, “measures”
"Total Usage" : -1605086.7261496289,
"Stratification" : -4.905050370111111,
"gen-delta-roof" : 4.819865854785763,
"lbs-loop" : 6520.1025 },
"day_of_week" : 4,
"month" : 8,
“dimensions”
"install" : {
"panels" : 32,
"name" : "agni-3501",
"num_files" : "3744",
"storage" : 1612,
"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" },
...right?
"year" : 2010,
"day" : 6

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!

"_id" : {
"measures" : {
"total-delta" : -85.78773442284201,
"Energy Sold" : 450087.1186574721,
"Generation" : 57273.159890170136,
"consumed-delta" : 12.569841951556597,
"lbs-sold" : 18848.4,
"Stored Energy" : 1306699.6439737699,
"Total Usage" : -1605086.7261496289,
"Stratification" : -4.905050370111111,
"gen-delta-roof" : 4.819865854785763,
"lbs-loop" : 6520.1025 },
"day_of_week" : 4,
"month" : 8,
"install" : {
"panels" : 32,
"name" : "agni-3501",
"num_files" : "3744",
"storage" : 1612,
"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

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 rows
to 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.

"_id" : {
"measures" : {

Let’s just look at one
"total-delta" : -85.78773442284201,
"Energy Sold" : 450087.1186574721,
"Generation" : 57273.159890170136,
"consumed-delta" : 12.569841951556597,
"lbs-sold" : 18848.4,
"Stored Energy" : 1306699.6439737699,
"Total Usage" : -1605086.7261496289,
"Stratification" : -4.905050370111111,
"gen-delta-roof" : 4.819865854785763,
"lbs-loop" : 6520.1025 },
"day_of_week" : 4,
"month" : 8,
"install" : {
"panels" : 32,
"name" : "agni-3501",
"num_files" : "3744",
"storage" : 1612,
"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

row-major data
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 Count
Tue Mar 9 23:01:44 2010,14.7627064834,53.7822899383,12.1642527206,51.1436001456,6.40476190476,8.9582972583,22.6857033228,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333458
Tue Mar 9 23:02:44 2010,14.958038343,53.764889193,12.1642527206,51.0925345058,6.40476190476,8.85184138407,22.5716100982,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333462
Tue Mar 9 23:03:45 2010,15.1145934976,53.6986641192,12.1642527206,50.8692901812,6.40476190476,8.78519002979,22.5673674246,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333462
Tue Mar 9 23:04:45 2010,15.2512207824,53.5955190752,12.1642527206,50.8293877551,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333468
Tue Mar 9 23:05:45 2010,15.3690229715,53.5534492867,12.1642527206,50.8293877551,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333471
Tue Mar 9 23:06:46 2010,15.5253261193,53.5534492867,12.1642527206,50.8658228816,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.3083978559,0.0,0.0,0.0,0.0,0.0,333472
Tue Mar 9 23:07:46 2010,15.6676270005,53.5534492867,12.1642527206,50.9177829276,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.293277114,0.0,0.0,0.0,0.0,0.0,333472
Tue Mar 9 23:08:47 2010,15.7915083121,53.4761516976,12.1642527206,50.8398031014,6.40476190476,8.78519002979,22.5652456306,24.0728390462,22.1826467404,0.0,0.0,0.0,0.0,0.0,333477
Tue Mar 9 23:09:47 2010,15.9763741003,53.693428918,12.1642527206,50.7859446809,6.40476190476,8.78519002979,22.5461357574,24.0728390462,22.1782915595,0.0,1.0,0.0,0.0,0.0,333581
Tue 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


“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" : {
"measures" : {
"total-delta" : -85.78773442284201,
"Energy Sold" : 450087.1186574721,
"Generation" : 57273.159890170136,
"consumed-delta" : 12.569841951556597,

A formula:

E = ∆t × F
#pseudocode
class 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)


Creating a Cube
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.


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 sensors
maybe some miscellaneous ones
internet backhaul
no 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 analog
3 pulse
ONE MB
no 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 sensors
one pulse
no increase in accuracy
no data backhaul -- at all
...
what’s the price?


$1,000


Hmm...
I can solder, and arduinos are
pretty cheap


It’s on!


arduino + netbook!

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


Time Series Data Storage in MongoDB

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (20)

Similar to Time Series Data Storage in MongoDB

Similar to Time Series Data Storage in MongoDB (20)

Recently uploaded

Recently uploaded (20)

Time Series Data Storage in MongoDB