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ERDDAP:
Heavy Loads, Grids, Clusters, Federations,
and Cloud Computing

ERDDAP is a web application and a web service that aggregates scientific data from diverse local and remote sources and offers a simple, consistent way to download subsets of the data in common file formats and make graphs and maps. This web page discusses issues related to heavy ERDDAP usage loads and explores possibilities for dealing with extremely heavy loads via grids, clusters, federations, and cloud computing.

DISCLAIMER - The contents of this web page are my (Bob Simons) personal opinions and do not necessarily reflect any position of the Government or the National Oceanic and Atmospheric Administration. The calculations are simplistic, but I think the conclusions are correct. Did I use faulty logic or make a mistake in my calculations? If so, the fault is mine alone. Please send an email with the correction to bob dot simons at noaa dot gov. The original version was written in June 2009. There have been no significant changes. This was last updated 2018-02-05.

Heavy Loads / Constraints

With heavy use, a standalone ERDDAP will be constrained (from most to least likely) by:
  1. A remote data source's bandwidth - Even with an efficient connection (e.g., via OPeNDAP), unless a remote data source has a very high bandwidth Internet connection, ERDDAP's responses will be constrained by how fast ERDDAP can get data from the data source. A solution is to copy the dataset onto ERDDAP's hard drive, perhaps with EDDGridCopy or EDDTableCopy.
  2. ERDDAP's server's bandwidth - Unless ERDDAP's server has a very high bandwidth Internet connection, ERDDAP's responses will be constrained by how fast ERDDAP can get data from the data sources and how fast ERDDAP can return data to the clients. The only solution is to get a faster Internet connection.
  3. Memory - If there are many simultaneous requests, ERDDAP can run out of memory and temporarily refuse new requests. (ERDDAP has a couple of mechanisms to avoid this and to minimize the consequences if it does happen.) So the more memory in the server the better. On a 32-bit server, 4+ GB is really good, 2 GB is okay, less is not recommended. On a 64-bit server, you can almost entirely avoid the problem but getting lots of memory. See the -Xmx and -Xms settings for ERDDAP/Tomcat. An ERDDAP getting heavy usage on a computer with a 64-bit server with 8GB of memory and -Xmx set to 4000M is rarely, if ever, constrained by memory.
  4. Hard drive bandwidth - Accessing data stored on the server's hard drive is vastly faster than accessing remote data. Even so, if the ERDDAP server has a very high bandwidth Internet connection, it is possible that accessing data on the hard drive will be a bottleneck. A partial solution is to use faster (e.g., 10,000 RPM) magnetic hard drives or SSD drives (if it makes sense cost-wise). Another solution is to store different datasets on different drives, so that the cumulative hard drive bandwidth is much higher.
  5. Too many files in a cache directory - ERDDAP caches all images, but only caches the data for certain types of data requests. It is possible for the cache directory for a dataset to have a large number of files temporarily. This will slow down requests to see if a file is in the cache (really!). <cacheMinutes> in setup.xml lets you set how long a file can be in the cache before it is deleted. Setting a smaller number would minimize this problem.
  6. CPU - Only two things take a lot of CPU time:
    • NetCDF 4 and HDF 5 now support internal compression of data. Decompressing a large compressed NetCDF 4 / HDF 5 data file can take 10 or more seconds. So multiple simultaneous requests to datasets with data stored in compressed files can put a severe strain on any server. If this is a problem, the solution is to store popular datasets in uncompressed files, or get a server with a CPU with more cores.
    • Making graphs (including maps): roughly 0.2 - 1 second per graph. So if there were many simultaneous unique requests for graphs (WMS clients often make 6 simultaneous requests!), there could be a CPU limitation. When multiple users are running WMS clients, this becomes a problem.
       

Grids, Clusters, and Federations

Under very heavy use, a single standalone ERDDAP will run into one or more of the constraints listed above and even the suggested solutions will be insufficient. For such situations, ERDDAP has features that make it easy to construct scalable grids (also called clusters or federations) of ERDDAPs which allow the system to handle very heavy use (e.g., for a large data center).

I'm using grid (external link) as a general term to indicate a type of computer cluster (external link) where all of the parts may or may not be physically located in one facility and may or may not be centrally administered. An advantage of co-located, centrally owned and administered grids (clusters) is that they benefit from economies of scale (especially the human workload) and simplify making the parts of the system work well together. An advantage of non-co-located grids, non-centrally owned and administered (federations) is that they distribute the human work load and the cost, and may provide some additional fault tolerance. The solution I propose below works well for all grid topographies.

The basic idea of designing a scalable system is to identify the potential bottlenecks and then design the system so that parts of the system can be replicated as needed to alleviate the bottlenecks. Ideally, each replicated part increases the capacity of that part of the system linearly (efficiency of scaling). The system isn't scalable unless there is a scalable solution for every bottleneck. Scalability (external link) is different from efficiency (how quickly a task can be done -- efficiency of the parts). Scalability allows the system to grow to handle any level of demand. Efficiency (of scaling and of the parts) determines how may servers, etc., will be needed to meet a given level of demand. Efficiency is very important, but always has limits. Scalability is the only practical solution to building a system that can handle very heavy use. Ideally, the system will be scalable and efficient.

The goals of this design are:

Our recommendations are:
grid/cluster diagram

The parts of the grid are:

A) For every remote data source that has a high-bandwidth OPeNDAP server, you can connect directly to the remote server. If the remote server is an ERDDAP, use EDDGridFromErddap or EDDTableFromERDDAP to serve the data in the Composite ERDDAP. If the remote server is some other type of DAP server, e.g., THREDDS, Hyrax, or GrADS, use EDDGridFromDap.

B) For every ERDDAP-able data source (a data source from which ERDDAP can read data) that has a high-bandwidth server, set up another ERDDAP in the grid which is responsible for serving the data from this data source.

C) For every ERDDAP-able data source that has a low-bandwidth server (or is a slow service for other reasons), consider setting up another ERDDAP and storing a copy of the dataset on that ERDDAP's hard drives, perhaps with EDDGridCopy and/or EDDTableCopy. If several such ERDDAPs aren't getting many requests for data, you can consolidate them into one ERDDAP.
C servers must be publicly accessible.

D) The composite ERDDAP is a regular ERDDAP except that it just serves data from other ERDDAPs.

Datasets In Very High Demand - In the really unusual case that one of the A, B, or C ERDDAPs can't keep up with the requests because of bandwidth or hard drive limitations, it makes sense to copy the data (again) on to another server+hardDrive+ERDDAP, perhaps with EDDGridCopy and/or EDDTableCopy. While it may seem ideal to have the original dataset and the copied dataset appear seamlessly as one dataset in the composite ERDDAP, this is difficult because the two datasets will be in slightly different states at different times (notably, after the original gets new data, but before the copied dataset gets its copy). Therefore, I recommend that the datasets be given slightly different titles (e.g., "... (copy #1)" and "... (copy #2)", or perhaps "(mirror #n)" or "(server #n)") and appear as separate datasets in the composite ERDDAP. Users are used to seeing lists of mirror sites (external link) at popular file download sites, so this shouldn't surprise or disappoint them. Because of bandwidth limitations at a given site, it may make sense to have the mirror located at another site. If the mirror copy is at a different data center, accessed just by that data center's composite ERDDAP, the different titles (e.g., "mirror #1) aren't necessary.

RAIDs vs. Regular Hard Drives - If a large dataset or a group of datasets are not heavily used, it may make sense to store the data on a RAID since it offers fault tolerance and since you don't need the processing power or bandwidth of another server. But if a dataset is heavily used, it may make more sense to copy the data on another server + ERDDAP + hard drive (similar to what Google does (external link)) rather than to use one server and a RAID to store multiple datasets since you get to use both server+hardDrive+ERDDAPs in the grid until one of them fails.

Failures - What happens if...

Simple, Scalable - This system is easy to set up and administer, and easily extensible when any part of it becomes over-burdened. The only real limitations for a given data center are the data center's bandwidth and the cost of the system.

Bandwidth - Note the approximate bandwidth of commonly used components of the system:

ComponentApproximate Bandwidth (GBytes/s)
DDR memory2.5
SSD drive1
SATA hard drive0.3
Gigabit Ethernet0.1
OC-120.06
OC-30.015
T10.0002

So, one SATA hard drive (0.3GB/s) on one server with one ERDDAP can probably saturate a Gigabit Ethernet LAN (0.1GB/s). And one Gigabit Ethernet LAN (0.1GB/s) can probably saturate an OC-12 Internet connection (0.06GB/s). And at least one source lists OC-12 lines costing about $100,000 per month. (Yes, these calculations are based on pushing the system to its limits, which is not good because it leads to very sluggish responses. But these calculations are useful for planning and for balancing parts of the system.) Clearly, a suitably fast Internet connection for your data center is by far the most expensive part of the system. You can easily and relatively cheaply build a grid with a dozen servers running a dozen ERDDAPs which is capable of pumping out lots of data quickly, but a suitably fast Internet connection will be very, very expensive. The partial solutions are: Note that Cloud Computing and web hosting services offer all the Internet bandwidth you need, but don't solve the price problem.

For general information on designing scalable, high capacity, fault-tolerant systems, see Michael T. Nygard's book Release It (external link).

[These are my opinions. Yes, the calculations are simplistic, but I think the conclusions are correct. Did I use faulty logic or make a mistake in my calculations? If so, the fault is mine alone. Please send an email with the correction to bob dot simons at noaa dot gov.]
 

Cloud Computing

Several companies offer cloud computing services (e.g., Amazon Web Services (external link) and Google Cloud Platform (external link)). Web hosting companies (external link) have offered simpler services since the mid-1990's, but the "cloud" services have greatly expanded the flexibility of the systems and the range of services offered. Since the ERDDAP grid just consists of ERDDAPs and since ERDDAPs are Java web applications that can run in Tomcat (the most common application server) or other application servers, it should be relatively easy to set up an ERDDAP grid on a cloud service or web hosting site. The advantages of these services are: The disadvantages of these services are:

Thanks - Many thanks to Matthew Arrott and his group in the original OOI effort for their work on putting ERDDAP in the cloud and the resulting discussions.


Contact

The contents of this web page are my (Bob Simons) personal opinions and do not necessarily reflect any position of the Government or the National Oceanic and Atmospheric Administration. The calculations are simplistic, but I think the conclusions are correct. Did I use faulty logic or make a mistake in my calculations? If so, the fault is mine alone. Please send an email with the correction to bob dot simons at noaa dot gov.

Questions, comments, suggestions? Please send an email to bob dot simons at noaa dot gov and include the ERDDAP URL directly related to your question or comment.
 


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