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Search found 8 results on 1 pages for 'hypertable'.

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• How to design an exception logging table using HyperTable and access it via the Java client?

  - by ikevinjp

  If I have the following table schema to log an exception (in standard SQL schema): Table: ExceptionLog Columns: ID (Long), ExceptionClass (String), ExceptionMessage (String), Host (String), Port (Integer), HttpHeader (String), HttpPostBody (String), HttpMethod (String) How would I design the same thing in HyperTable (specifically, what is the best approach for efficiency)? And, how would I code it using the HyperTable Java client?

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• bigtable vs cassandra vs simpledb vs dynamo vs couchdb vs hypertable vs riak vs hbase, what do they

  - by Mickey Shine

  Sorry if this question is somewhat subjective. I am new to 'could store', 'distributed store' or some concepts like this. I really wonder what do they have in common and want to get an overview on all of them. What do I need to prepare if I want to write a product similar to this?

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• How to read a simple file to fill a HyperTable with column qualifiers?

  - by Wajih

  I had been looking at the wiki/docs to see for a sample which teaches how to load column qualifiers from a file. Is there any such sample. Could you guide me on the following simple table? Specifically the field FromID which should contain, say FromID: person1 FromID: person2 create table stats (ID1,ID2,To,FromID, QUALIFIER INDEX FromID) My TSV file looks like this #SeQ ID1 ID2 To FromID 01099 3 4 me ---What would the entries be here?? Thank you for the guidance, Wajih

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• Non-Relational Database Design

  - by Ian Varley

  I'm interested in hearing about design strategies you have used with non-relational "nosql" databases - that is, the (mostly new) class of data stores that don't use traditional relational design or SQL (such as Hypertable, CouchDB, SimpleDB, Google App Engine datastore, Voldemort, Cassandra, SQL Data Services, etc.). They're also often referred to as "key/value stores", and at base they act like giant distributed persistent hash tables. Specifically, I want to learn about the differences in conceptual data design with these new databases. What's easier, what's harder, what can't be done at all? Have you come up with alternate designs that work much better in the non-relational world? Have you hit your head against anything that seems impossible? Have you bridged the gap with any design patterns, e.g. to translate from one to the other? Do you even do explicit data models at all now (e.g. in UML) or have you chucked them entirely in favor of semi-structured / document-oriented data blobs? Do you miss any of the major extra services that RDBMSes provide, like relational integrity, arbitrarily complex transaction support, triggers, etc? I come from a SQL relational DB background, so normalization is in my blood. That said, I get the advantages of non-relational databases for simplicity and scaling, and my gut tells me that there has to be a richer overlap of design capabilities. What have you done? FYI, there have been StackOverflow discussions on similar topics here: the next generation of databases changing schemas to work with Google App Engine choosing a document-oriented database

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• Key Value Database For Windows?

  - by Axl

  Other than MongoDB and Memcached, what key-value stores run on Windows? Most of the ones I've seen seem to only run on Linux (Hypertable, Redis, Lightcloud). Related links: http://stackoverflow.com/questions/639545/is-there-a-business-proven-cloud-store-keyvalue-database-open-source http://www.metabrew.com/article/anti-rdbms-a-list-of-distributed-key-value-stores/

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• Free data warehouse - Infobright, Hadoop/Hive or what ?

  - by peperg

  I need to store large amount of small data objects (millions of rows per month). Once they're saved they wont change. I need to : store them securely use them to analysis (mostly time-oriented) retrieve some raw data occasionally It would be nice if it could be used with JasperReports or BIRT My first shot was Infobright Community - just a column-oriented, read-only storing mechanism for MySQL On the other hand, people says that NoSQL approach could be better. Hadoop+Hive looks promissing, but the documentation looks poor and the version number is less than 1.0 . I heard about Hypertable, Pentaho, MongoDB .... Do you have any recommendations ? (Yes, I found some topics here, but it was year or two ago)

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• 'e-Commerce' scalable database model

  - by Ruben Trancoso

  I would like to understand database scalability so I've just heard a talk about Habits of Highly Scalable Web Applications http://techportal.ibuildings.com/2010/03/02/habits-of-highly-scalable-web-applications/ On it, the presenter mainly talk about relational database scalability. I also have read something about MapReduce and Column oriented tables, big tables, hypertable etc... trying to understand which are the most up to date methods to scale web application data. But the second group, to me, is being hard to understand where it fits. It serves as transactional, reliable data store? or not, its just for large access and processing and to handle fine graned operations we will ever need to rely on RDBMSs? Could someone give a comprehensive landscape for those new technologies and how to use it?

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• What scalability problems have you solved using a NoSQL data store?

  - by knorv

  NoSQL refers to non-relational data stores that break with the history of relational databases and ACID guarantees. Popular open source NoSQL data stores include: Cassandra (tabular, written in Java, used by Facebook, Twitter, Digg, Rackspace, Mahalo and Reddit) CouchDB (document, written in Erlang, used by Engine Yard and BBC) Dynomite (key-value, written in C++, used by Powerset) HBase (key-value, written in Java, used by Bing) Hypertable (tabular, written in C++, used by Baidu) Kai (key-value, written in Erlang) MemcacheDB (key-value, written in C, used by Reddit) MongoDB (document, written in C++, used by Sourceforge, Github, Electronic Arts and NY Times) Neo4j (graph, written in Java, used by Swedish Universities) Project Voldemort (key-value, written in Java, used by LinkedIn) Redis (key-value, written in C, used by Engine Yard, Github and Craigslist) Riak (key-value, written in Erlang, used by Comcast and Mochi Media) Ringo (key-value, written in Erlang, used by Nokia) Scalaris (key-value, written in Erlang, used by OnScale) ThruDB (document, written in C++, used by JunkDepot.com) Tokyo Cabinet/Tokyo Tyrant (key-value, written in C, used by Mixi.jp (Japanese social networking site)) I'd like to know about specific problems you - the SO reader - have solved using data stores and what NoSQL data store you used. Questions: What scalability problems have you used NoSQL data stores to solve? What NoSQL data store did you use? What database did you use before switching to a NoSQL data store? I'm looking for first-hand experiences, so please do not answer unless you have that.

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