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Hi, I'm just starting with trying to build web applications and I'm not really familiar with how to store data to make sure something like this scales well.

Consider this pseudo-data, in JSON: { unique id, [array of child ids], [auxiliary data] }

Right now, every request I get requires me to get auxiliary info given an ID. About 3/4 of the time I need to update some of the auxiliary data. About half of the time, I need to query again for the auxiliary data of one of the "child" id's from the array of ids. This makes me want to just use a relational database and generate another query based on the child IDs if I need to traverse the natural graph structure of the data (do very "shallow" searches).

I'm wondering how well this would continue to work if I suddenly decided to do a lot more "depth-first" query patterns (that is, every query would likely be followed by a query to it's child, which has an unpredictable ID), and whether specialized graph databases (not SQL) would give me more scalability in this case. I don't actually know much about how they work but I imagine if there's any reason they exist it's for stuff like this.

Can anyone point me in the right direction? If a single request generates a chain of sequential SELECTs to traverse a graph am I doing it wrong?

submitted by ReallyGoodAdvice
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Irrational Games is an award winning video game developer located in Quincy, MA and founded in 1997 by Ken Levine, Jonathan Chey and Robert Fermier. The team made its name with the much loved first person shooter System Shock 2. This was followed by two Freedom Force games, SWAT 4 and Tribes: Vengeance. In 2005 Irrational Games was acquired by Take-Two Interactive. The team was renamed 2K Boston in 2007, just in time to coincide with the release of the critically acclaimed BioShock, which went o

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New submitter rescrv writes "Key-value stores (like Cassandra, Redis and DynamoDB) have been replacing traditional databases in many demanding web applications (e.g. Twitter, Google, Facebook, LinkedIn, and others). But for the most part, the differences between existing NoSQL systems come down to the choice of well-studied implementation techniques; in particular, they all provide a similar API that achieves high performance and scalability by limiting applications to simple operations like GET and PUT.

HyperDex, a new key-value store developed at Cornell, stands out in the NoSQL spectrum with its unique design. HyperDex employs a unique multi-dimensional hash function to enable efficient search operations — that is, objects may be retrieved without using the key (PDF) under which they are stored. Other systems employ indexing techniques to enable search, or enumerate all objects in the system. In contrast, HyperDex's design enables applications to retrieve search results directly from servers in the system. The results are impressive. Preliminary benchmark results on the project website show that HyperDex provides significant performance improvements over Cassandra and MongoDB. With its unique design, and impressive performance, it seems fittng to ask: Is HyperDex the start of NoSQL 2.0?"


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