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If you foresee that moving to Cassandra is definitely in your future, it will be easier to do while your dataset is still small and manageable. Also, as you learn and get a feel for Cassandra, a small dataset is a better one to make mistakes on (and thus, easier to correct them). That way your data model is solid by the time your dataset gets big, and ...


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Option One, Make a new table referencing the id of the Parent/Child Relationship Pros If you need to obfuscate the data, or generate a GUID, or a custom id for an external department working with the data, this might be a valid option to consider. Cons It's not useful for any other situation that this first person can think of. Option Two, Use the ...


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You have to choose one of the terminal stations as origin and store the distances from origin to the each station on the route: +--------------+----------------------+ | Station Name | Distance from origin | +--------------+----------------------+ | Station A | 0 | | Station B | 10 | | Station C | ...


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Neo4j is a database that sounds a fit for your needs. It is graph based database, there are many drivers for it in many languages and it is built in Java. In this database, you would specify the relationships within each node, rather than creating tables for joins, like in mySQL.


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Computers can't operate the real sets. In fact - nobody can. We don't know how to fetch only required items that satisfied the given conditions from the bag without iterations. Formal relational model is an abstraction. RDBMSes are just resemble it but they are built on the completely different basis. They uses lists, they iterate and they uses rows of ...


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The major differences are related to duplicate entries in both data and metadata, and the existence of NULL values. SQL implementations deviate from the relational model for practical reasons, for the most part. Read up on the topic in SQL and Relational Theory by C.J. Date or (Kudos to Bill Karwin for this title by the same author) read pages 2 and 3 in ...


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I tested few databases for the similar scenario (ingesting logs), and MongoDB seems to be quite useful. I used following collection: db.createCollection("apache", {capped: true, size: 10000000000}); It seems that once the collection is capped it is not growing any more. Now there is a tricky part - retrieving the data. If you need to query periods of ...


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How unique are the DataID's? Are there just a handful of DataID's for the 20 million entries per hour or are DataID's more numerous? But if your most frequent query is based on a narrow time frame first and then specific DataID's then this create statement should give you decent performance. You will need to change the data types to what makes sense for ...


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In addition to what Jynus said: be sure your table is physically clustered on Date first. This will make range scans very efficient, so aggregation up to weeks or months will be fast. Even if you choose to instantiate these week- or month-level totals in summary tables, clustering by date will help by making the updates very quick. This kind of situation ...



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