7 ms·

Could you be more specific about the IoT DB issue? What would a credible solution do? Thanks in advance.

jandrewrogers 10y ago
An IoT database requires the intersection of three software capabilities in a single platform, which have varying degrees of accessibility (the technology exists in principle) and none are available in open source currently. - An exabyte-scale storage engine. Nothing too exotic here technically and a few companies have built them, but the design needs to address continuous data corruption, continuous hardware failure, geo-federation, etc. - A real-time database kernel that supports very high throughput for mixed workloads. A production kernel of this type doesn't currently exist though several people working in closed source databases understand the necessary computer science in principle; the academic literature is far behind the state-of-the-art. The ability to gracefully shift load and transients between servers under full load with many millions of writes per second is not trivial. - Native discrete topology operators. Necessary for geospatial analytics, sensor coverages, etc. If you can do it natively in the database kernel, it makes the second requirement easier to achieve since you don't need secondary indexing generally. Any solution even halfway toward the general solution would be viable. The value possible if you have such a system is hard to overestimate. Companies have paid half a million dollars for the output of a single analytic query on tens of trillions of IoT records; the differentiator was that it was possible to execute such a query at all. It is extremely high-end and polymathic computer science, but serious valuable if you can make a credible dent in it. And unlike some advanced topics in computer science, there are no epic unsolved theoretical problems you have to solve, though some relevant computer science may be unpublished.
- bostik 10y ago
  A friend is working on an IoT problem with two of your three capabilities: storage and analytics. He has voiced similar concerns. (To be fair, he gripes constantly that nothing exists which would work for him, within the budgetary and timing constraints.) His company provides sensor systems for civil engineering projects. A single large bridge can have sensor packs every 50m or so - per beam. The amount of sensor data coming in for a single municipality or region is already staggering. Vibration and stress analytics are required on a daily basis. The final requirement, one which you didn't mention, is that this setup should be fairly low maintenance. If you need a team of rocket scientists to operate and just keep it from falling over, the cost structure will be unsustainable. A service that provided all this in a platform with sane APIs and good BI integration should be making tons of money.
  - aorloff 10y ago
    > budgetary and timing constraints ain't that the truth
  - z3t4 10y ago
    Instead of storing the data and then analyzing it, make it into a stream! eg: collect | analyze | report During development or if you want to store the raw data you can split the stream. eg: collect | tee >(store) >(anotherAnalyzer) | analyze | report
- dcposch 10y ago
  What are one or two concrete things you'd actually use this for?
- shortoncash 10y ago
  Maybe a general solution just isn't cost effective and may never be cost effective, regardless of how much we bring down actual costs for the resources involved in solving data-intensive problems. The (possible) reason? The performance advantage you gain from tailoring specifics to structural relationships within exceptionally large datasets may just be too large to trade off for some "generalized" solution. I'm just speculating, of course. I was sort of entertaining your idea and going back and forth between "a sounds-good fantasy" and "No, that would be awesome, why does this not exist?"
- espeed 10y ago
  Hi Andrew - What's the status with SpaceCurve? http://spacecurve.com http://spacecurve.com
- btown 10y ago
  The first two challenges are quite familiar in the financial industry, and kx, with its kdb+ time-series database, is arguably the industry leader. Most people in the open source sector don't pay it much attention, since its proprietary nature and terse APL-inspired syntax [0] lend themselves towards "I'll make my career in this, become a wizard, get Wall Street money, and not be very incentivized to compete with myself by sharing knowledge." To the third question, I'm not sure to what extent it treats topology natively, but kx is certainly positioning themselves as an IoT solution provider [1]. Of course, the cost is likely prohibitive for smaller IoT startups, and more innovation would certainly be good from a supply-demand perspective, but from a technical perspective I'm not sure this is an unsolved problem. [0] https://a.kx.com/q/d/q.htm https://a.kx.com/q/d/q.htm [1] https://kx.com/solutions/utilities/ https://kx.com/solutions/utilities/
  - jandrewrogers 10y ago
    KX is a leader in what it does but it doesn't really fit the canonical IoT use case, either from a workload or analytics perspective. The challenge is that IoT isn't just temporal, most of the interesting relationships are spatial as well since you are analyzing relationships across sensor and telemetry streams, and the analytics are real-time mixed workloads with ridiculous working set sizes. It isn't their market. They rock the time-series market though. A good example of an IoT data analytics problem is analyzing a petabyte of drone sensor data, which on a large drone amounts to a few flights worth. Typical raw sources tend to be some combination of hyper-spectral imaging/video and LIDAR. Or RF probability functions e.g. mobile. Or a combination of all of the above because you are fusing multiple sources to reduce the uncertainty for your analysis. FWIW, the "tens of trillions" of IoT records I mentioned was a real-world example from one of the most famous financial companies. It was a spatial analytic on a polygon model, and a classic IoT data model. If KX solved that particular analysis problem, they would have used it.
  - monodeldiablo 10y ago
    kdb is awesome, but very tailored to one-dimensional (time-ordered) data. I've built a couple multi-dimensional array database engines in my time and -- trust me -- it's a really difficult problem to solve. That said, I have a design kicking around for one that meets most of the OP's demands, so if anybody wants to join me... There's been an explosion in raster data in just the last 5 years. Cheap satellites, cheap drones, and cheap platforms have really turned the firehose to "high". And the resolution on scientific data -- climate and weather model output, astronomy data, particle physics data, seismic data, etc. -- just keeps going up. This is an area of massive growth for which no existing solution is quite adequate. If I had the time and/or the cash, I'd be diving in head-first.
- aorloff 10y ago
  Flurry (now part of Yahoo) has experience with IoT storage at that scale (or near it). The "T" just happens to be your phone. Data processing (and staying in business) at that scale is a matter of great teams and solid architecture. This won't be a "service" easily sold to a general audience. The data processing could become a commodity but the architecture won't be -- its highly tied to your specific industry.
- cachemiss 10y ago
  I'd also add, there's a component of streaming analytics that isn't solved either. One of the points I've tried to make at various companies (we've worked at the same one before) is that streaming solutions and batch solutions need to be fused into a single execution engine. A streaming system on its own (operating on temporal windows) is not nearly as useful as on that can be joined to a storage engine with data at rest. It also needs to be disk based, so windows can be large, which most people do not want to take on. It also needs to be extremely parallel, and efficient. Thousands of requests a second per server is not even in the right ball-park (which is lots of current execution engines now). Operating at line rate is generally table stakes IMO. The operations on the stream should be parallelized automatically, up to petabytes a day of input. Humans don't have the necessary context to do the partitioning up front, especially with streams that change. The issue is (and I've tried to come up with designs to address this, though not in practice), is that co-locating the data at rest, with data that is moving through the system is a tricky problem, especially with complicated joins. They can be the same engine (and should), but traditional database engines tend to have a problem with streaming queries, since they are just repeatedly executing a query against every new record. They are expressible, just not efficient. There is room to innovate in this space, but most people building these engines either solve the parallelism problem naively, or not at all. There's also the problem of driving this computation to the edge, which is also something I have a solution for in a way that no one is doing, but have not yet met a company willing to take this level of effort on. All the points you make about the kernel are apt, as are the points about the distribution algorithms. Also, the protocols used aren't nash safe, so at scale most of these systems become an operational juggling act under pressure. All streaming systems that I know of do not know enough about the underlying data to gracefully rebalance and co-locate, since they all tend to embody the map/reduce paradigm, which is oblivious to underlying data distribution, at least in current practice. There is available computer science to solve all these issues, I think some of the spatial algorithms out there can also be applied to the streaming space, especially in join evaluation.
  - notforgot 10y ago
    You shared a lot of deep insight here. Thanks. > something I have a solution for in a way that no one is doing Is the general direction for this something you can share? 30 years of database literature accumulated a lot of knowledge. It's be a bold claim to say there's something powerful yet non-obvious.

An IoT database requires the intersection of three software capabilities in a single platform, which have varying degrees of accessibility (the technology exists in principle) and none are available in open source currently. - An exabyte-scale storage engine. Nothing too exotic here technically and a few companies have built them, but the design needs to address continuous data corruption, continuous hardware failure, geo-federation, etc. - A real-time database kernel that supports very high throughput for mixed workloads. A production kernel of this type doesn't currently exist though several people working in closed source databases understand the necessary computer science in principle; the academic literature is far behind the state-of-the-art. The ability to gracefully shift load and transients between servers under full load with many millions of writes per second is not trivial. - Native discrete topology operators. Necessary for geospatial analytics, sensor coverages, etc. If you can do it natively in the database kernel, it makes the second requirement easier to achieve since you don't need secondary indexing generally. Any solution even halfway toward the general solution would be viable. The value possible if you have such a system is hard to overestimate. Companies have paid half a million dollars for the output of a single analytic query on tens of trillions of IoT records; the differentiator was that it was possible to execute such a query at all. It is extremely high-end and polymathic computer science, but serious valuable if you can make a credible dent in it. And unlike some advanced topics in computer science, there are no epic unsolved theoretical problems you have to solve, though some relevant computer science may be unpublished.

bostik 10y ago
A friend is working on an IoT problem with two of your three capabilities: storage and analytics. He has voiced similar concerns. (To be fair, he gripes constantly that nothing exists which would work for him, within the budgetary and timing constraints.) His company provides sensor systems for civil engineering projects. A single large bridge can have sensor packs every 50m or so - per beam. The amount of sensor data coming in for a single municipality or region is already staggering. Vibration and stress analytics are required on a daily basis. The final requirement, one which you didn't mention, is that this setup should be fairly low maintenance. If you need a team of rocket scientists to operate and just keep it from falling over, the cost structure will be unsustainable. A service that provided all this in a platform with sane APIs and good BI integration should be making tons of money.
- aorloff 10y ago
  > budgetary and timing constraints ain't that the truth
- z3t4 10y ago
  Instead of storing the data and then analyzing it, make it into a stream! eg: collect | analyze | report During development or if you want to store the raw data you can split the stream. eg: collect | tee >(store) >(anotherAnalyzer) | analyze | report
dcposch 10y ago
What are one or two concrete things you'd actually use this for?
shortoncash 10y ago
Maybe a general solution just isn't cost effective and may never be cost effective, regardless of how much we bring down actual costs for the resources involved in solving data-intensive problems. The (possible) reason? The performance advantage you gain from tailoring specifics to structural relationships within exceptionally large datasets may just be too large to trade off for some "generalized" solution. I'm just speculating, of course. I was sort of entertaining your idea and going back and forth between "a sounds-good fantasy" and "No, that would be awesome, why does this not exist?"
espeed 10y ago
Hi Andrew - What's the status with SpaceCurve? http://spacecurve.com http://spacecurve.com
btown 10y ago
The first two challenges are quite familiar in the financial industry, and kx, with its kdb+ time-series database, is arguably the industry leader. Most people in the open source sector don't pay it much attention, since its proprietary nature and terse APL-inspired syntax [0] lend themselves towards "I'll make my career in this, become a wizard, get Wall Street money, and not be very incentivized to compete with myself by sharing knowledge." To the third question, I'm not sure to what extent it treats topology natively, but kx is certainly positioning themselves as an IoT solution provider [1]. Of course, the cost is likely prohibitive for smaller IoT startups, and more innovation would certainly be good from a supply-demand perspective, but from a technical perspective I'm not sure this is an unsolved problem. [0] https://a.kx.com/q/d/q.htm https://a.kx.com/q/d/q.htm [1] https://kx.com/solutions/utilities/ https://kx.com/solutions/utilities/
- jandrewrogers 10y ago
  KX is a leader in what it does but it doesn't really fit the canonical IoT use case, either from a workload or analytics perspective. The challenge is that IoT isn't just temporal, most of the interesting relationships are spatial as well since you are analyzing relationships across sensor and telemetry streams, and the analytics are real-time mixed workloads with ridiculous working set sizes. It isn't their market. They rock the time-series market though. A good example of an IoT data analytics problem is analyzing a petabyte of drone sensor data, which on a large drone amounts to a few flights worth. Typical raw sources tend to be some combination of hyper-spectral imaging/video and LIDAR. Or RF probability functions e.g. mobile. Or a combination of all of the above because you are fusing multiple sources to reduce the uncertainty for your analysis. FWIW, the "tens of trillions" of IoT records I mentioned was a real-world example from one of the most famous financial companies. It was a spatial analytic on a polygon model, and a classic IoT data model. If KX solved that particular analysis problem, they would have used it.
- monodeldiablo 10y ago
  kdb is awesome, but very tailored to one-dimensional (time-ordered) data. I've built a couple multi-dimensional array database engines in my time and -- trust me -- it's a really difficult problem to solve. That said, I have a design kicking around for one that meets most of the OP's demands, so if anybody wants to join me... There's been an explosion in raster data in just the last 5 years. Cheap satellites, cheap drones, and cheap platforms have really turned the firehose to "high". And the resolution on scientific data -- climate and weather model output, astronomy data, particle physics data, seismic data, etc. -- just keeps going up. This is an area of massive growth for which no existing solution is quite adequate. If I had the time and/or the cash, I'd be diving in head-first.
aorloff 10y ago
Flurry (now part of Yahoo) has experience with IoT storage at that scale (or near it). The "T" just happens to be your phone. Data processing (and staying in business) at that scale is a matter of great teams and solid architecture. This won't be a "service" easily sold to a general audience. The data processing could become a commodity but the architecture won't be -- its highly tied to your specific industry.
cachemiss 10y ago
I'd also add, there's a component of streaming analytics that isn't solved either. One of the points I've tried to make at various companies (we've worked at the same one before) is that streaming solutions and batch solutions need to be fused into a single execution engine. A streaming system on its own (operating on temporal windows) is not nearly as useful as on that can be joined to a storage engine with data at rest. It also needs to be disk based, so windows can be large, which most people do not want to take on. It also needs to be extremely parallel, and efficient. Thousands of requests a second per server is not even in the right ball-park (which is lots of current execution engines now). Operating at line rate is generally table stakes IMO. The operations on the stream should be parallelized automatically, up to petabytes a day of input. Humans don't have the necessary context to do the partitioning up front, especially with streams that change. The issue is (and I've tried to come up with designs to address this, though not in practice), is that co-locating the data at rest, with data that is moving through the system is a tricky problem, especially with complicated joins. They can be the same engine (and should), but traditional database engines tend to have a problem with streaming queries, since they are just repeatedly executing a query against every new record. They are expressible, just not efficient. There is room to innovate in this space, but most people building these engines either solve the parallelism problem naively, or not at all. There's also the problem of driving this computation to the edge, which is also something I have a solution for in a way that no one is doing, but have not yet met a company willing to take this level of effort on. All the points you make about the kernel are apt, as are the points about the distribution algorithms. Also, the protocols used aren't nash safe, so at scale most of these systems become an operational juggling act under pressure. All streaming systems that I know of do not know enough about the underlying data to gracefully rebalance and co-locate, since they all tend to embody the map/reduce paradigm, which is oblivious to underlying data distribution, at least in current practice. There is available computer science to solve all these issues, I think some of the spatial algorithms out there can also be applied to the streaming space, especially in join evaluation.
- notforgot 10y ago
  You shared a lot of deep insight here. Thanks. > something I have a solution for in a way that no one is doing Is the general direction for this something you can share? 30 years of database literature accumulated a lot of knowledge. It's be a bold claim to say there's something powerful yet non-obvious.