I am looking for a convenient way to store and to query huge amount of meteorological data (few TB). More information about the type of data in the middle of the question.
Previously I was looking in the direction of MongoDB (I was using it for many of my own previous projects and feel comfortable dealing with it), but recently I found out about HDF5 data format. Reading about it, I found some similarities with Mongo:
HDF5 simplifies the file structure to include only two major types of object: Datasets, which are multidimensional arrays of a homogenous type Groups, which are container structures which can hold datasets and other groups This results in a truly hierarchical, filesystem-like data format. Metadata is stored in the form of user-defined, named attributes attached to groups and datasets.
Which looks like arrays and embedded objects in Mongo and also it supports indices for querying the data.
Because it uses B-trees to index table objects, HDF5 works well for time series data such as stock price series, network monitoring data, and 3D meteorological data.
The data:
Specific region is divided into smaller squares. On the intersection of each one of the the sensor is located (a dot).
This sensor collects the following information every X minutes:
- solar luminosity
- wind location and speed
- humidity
- and so on (this information is mostly the same, sometimes a sensor does not collect all the information)
It also collects this for different height (0m, 10m, 25m). Not always the height will be the same. Also each sensor has some sort of metainformation:
- name
- lat, lng
- is it in water, and many others
Giving this, I do not expect the size of one element to be bigger than 1Mb. Also I have enough storage at one place to save all the data (so as far as I understood no sharding is required)
Operations with the data. There are several ways I am going to interact with a data:
convert as store big amount of it: Few TB of data will be given to me as some point of time in netcdf format and I will need to store them (and it is relatively easy to convert it HDF5). Then, periodically smaller parts of data (1 Gb per week) will be provided and I have to add them to the storage. Just to highlight: I have enough storage to save all this data on one machine.
query the data. Often there is a need to query the data in a real-time. The most of often queries are: tell me the temperature of sensors from the specific region for a specific time, show me the data from a specific sensor for specific time, show me the wind for some region for a given time-range. Aggregated queries (what is the average temperature over the last two months) are highly unlikely. Here I think that Mongo is nicely suitable, but hdf5+pytables is an alternative.
perform some statistical analysis. Currently I do not know what exactly it would be, but I know that this should not be in a real time. So I was thinking that using hadoop with mongo might be a nice idea but hdf5 with R is a reasonable alternative.
I know that the questions about better approach are not encouraged, but I am looking for an advice of experienced users. If you have any questions, I would be glad to answer them and will appreciate your help.
P.S I reviewed some interesting discussions, similar to mine: hdf-forum, searching in hdf5, storing meteorological data
It's a difficult question and I am not sure if I can give a definite answer but I have experience with both HDF5/pyTables and some NoSQL databases.
Here are some thoughts.
- HDF5 per se has no notion of index. It's only a hierarchical storage format that is well suited for multidimensional numeric data. It's possible to extend on top of HDF5 to implement an index (i.e. PyTables, HDF5 FastQuery) for the data.
- HDF5 (unless you are using the MPI version) does not support concurrent write access (read access is possible).
- HDF5 supports compression filters which can - unlike popular belief - make data access actually faster (however you have to think about proper chunk size which depends on the way you access the data).
- HDF5 is no database. MongoDB has ACID properties, HDF5 doesn't (might be important).
- There is a package (SciHadoop) that combines Hadoop and HDF5.
- HDF5 makes it relatively easy to do out core computation (i.e. if the data is too big to fit into memory).
- PyTables supports some fast "in kernel" computations directly in HDF5 using numexpr
I think your data generally is a good fit for storing in HDF5. You can also do statistical analysis either in R or via Numpy/Scipy.
But you can also think about a hybdrid aproach. Store the raw bulk data in HDF5 and use MongoDB for the meta-data or for caching specific values that are often used.
You can try SciDB if loading NetCDF/HDF5 into this array database is not a problem for you. Note that if your dataset is extremely large, the data loading phase will be very time consuming. I'm afraid this is a problem for all the databases. Anyway, SciDB also provides an R package, which should be able to support the analysis you need.
Alternatively, if you want to perform queries without transforming HDF5 into something else, you can use the product here: http://www.cse.ohio-state.edu/~wayi/papers/HDF5_SQL.pdf Moreover, if you want to perform a selection query efficiently, you should use index; if you want to perform aggregation query in real time (in seconds), you can consider approximate aggregation. Our group has developed some products to support those functions.
In terms of statistical analysis, I think the answer depends on the complexity of your analysis. If all you need is to compute something like entropy or correlation coefficient, we have products to do it in real time. If the analysis is very complex and ad-hoc, you may consider SciHadoop or SciMATE, which can process scientific data in the MapReduce framework. However, I am not sure if SciHadoop currently can support HDF5 directly.
来源：https://stackoverflow.com/questions/16907195/what-is-a-better-approach-of-storing-and-querying-a-big-dataset-of-meteorologica