Hi,
I have been looking at a lot of different alternatives for working with time series data. The likes of kdb and fame are obviously tried, tested and suitable solutions but also with a hefty price tag and closed source. On the open source side I haven't so far found a good holistic approach. What I have found is that HDF5 is a very good and efficient library for storing and retrieving time series data (and numerical datasets in general), but it's sort of a single user/single system setup, but in terms of layers on top of it for replication, network connectivity etc I haven't seen much. Doing my initial reading around riak I was happy to see that there's pluggable backend storage and it provides a lot of the higher level features one would want. So I put the two together and asked myself the question "Would riak with a HDF5 storage backend make a nice system for working with time series data?" I don't know the answer to that question yet and need to do more reading to understand riak but I thought I'd throw the idea out there and see if what comes back. The basic query would typically be "for series identified by name(s) X (,Y,Z), give me the data for the time window between N to M". Regards, Patrik ###################################################################### The information contained in this communication is confidential and intended only for the individual(s) named above. If you are not a named addressee, please notify the sender immediately and delete this email from your system. The views expressed in this email are the views of the author and do not necessarily represent the views of Millennium Capital Partners LLP (MCP LLP) or any of its affiliates. Outgoing and incoming electronic communications of MCP LLP and its affiliates, including telephone communications, may be electronically archived and subject to review and/or disclosure to someone other than the recipient. MCP LLP is authorised and regulated by the Financial Services Authority. ######################################################################
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