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"These are the major new features added since Slick 1.0.1: A code generator that reverse-engineers the database schema and generates all code required for working with Slick. New driver architecture to allow support for non-SQL, non-JDBC databases. Table definitions in the Lifted Embedding use a new syntax which is slightly more verbose but also more robust and logical, avoiding several pitfalls from earlier versions. Table definitions (and their * projections) are not restricted to flat tuples of columns anymore. They can use any type that would be valid as the return type of a Query. The old projection concatenation methods ~ and ~: are still supported but not imported by default. In addition to Scala tuples, Slick supports its own HList abstraction for records of arbitrary size. You can also add support for your own record types with only a few lines of code. All record types can be used everywhere (including table definitions and mapped projections) and they can be mixed and nested arbitrarily. Soft inserts are now the default, i.e. AutoInc columns are automatically skipped when inserting with +=, ++=, insert and insertAll. This means that you no longer need separate projections (without the primary key) for inserts. There are separate methods forceInsert and forceInsertAll in JdbcProfile for the old behavior. A new model for pre-compiled queries replaces the old QueryTemplate abstraction. Any query (both, actual collection-valued Query objects and scalar queries) or function from Column types to such a query can now be lifted into a Compiled wrapper. Lifted functions can be applied (without having to recompile the query), and you can use both monadic composition of Compiled values or just get the underlying query and use that for further composition. Pre-compiled queries can now be used for update and delete operations in addition to querying. threadLocalSession has been renamed to dynamicSession and the corresponding methods have distinct names (e.g. w

"I published two such Tables of Elements about a year ago, click here to check them out. This one is a new one, focusing on machine learning libraries (R and Julia). And it is interactive, with access to the various libraries listed in the table, when clicking on an element (only on the original article)."

"HDF (Hierarchical Data Format) technologies are relevant when the data challenges being faced push the limits of what can be addressed by traditional database systems, XML documents, or in-house data formats. Leveraging the powerful HDF products and the expertise of The HDF Group, organizations realize substantial cost savings while solving challenges that seemed intractable using other data management technologies. Many HDF adopters have very large datasets, very fast access requirements, or very complex datasets. Others turn to HDF because it allows them to easily share data across a wide variety of computational platforms using applications written in different programming languages. Some use HDF to take advantage of the many open-source and commercial tools that understand HDF. Similar to XML documents, HDF files are self-describing and allow users to specify complex data relationships and dependencies. In contrast to XML documents, HDF files can contain binary data (in many representations) and allow direct access to parts of the file without first parsing the entire contents. HDF, not surprisingly, allows hierarchical data objects to be expressed in a very natural manner, in contrast to the tables of relational database. Whereas relational databases support tables, HDF supports n-dimensional datasets and each element in the dataset may itself be a complex object. Relational databases offer excellent support for queries based on field matching, but are not well-suited for sequentially processing all records in the database or for subsetting the data based on coordinate-style lookup."

"Apache Phoenix is a SQL skin over HBase delivered as a client-embedded JDBC driver targeting low latency queries over HBase data. Apache Phoenix takes your SQL query, compiles it into a series of HBase scans, and orchestrates the running of those scans to produce regular JDBC result sets. The table metadata is stored in an HBase table and versioned, such that snapshot queries over prior versions will automatically use the correct schema. Direct use of the HBase API, along with coprocessors and custom filters, results in performance on the order of milliseconds for small queries, or seconds for tens of millions of rows. "

"Apache Phoenix is a SQL skin over HBase delivered as a client-embedded JDBC driver targeting low latency queries over HBase data. Apache Phoenix takes your SQL query, compiles it into a series of HBase scans, and orchestrates the running of those scans to produce regular JDBC result sets. The table metadata is stored in an HBase table and versioned, such that snapshot queries over prior versions will automatically use the correct schema. Direct use of the HBase API, along with coprocessors and custom filters, results in performance on the order of milliseconds for small queries, or seconds for tens of millions of rows."

"Internet Routing: It's a program (well, a daemon, as you are going to discover in a moment) which works as a dynamic router in an Internet type network (that is, in a network running either the IPv4 or the IPv6 protocol). Routers are devices which forward packets between interconnected networks in order to allow hosts not connected directly to the same local area network to communicate with each other. They also communicate with the other routers in the Internet to discover the topology of the network which allows them to find optimal (in terms of some metric) rules for forwarding of packets (which are called routing tables) and to adapt themselves to the changing conditions such as outages of network links, building of new connections and so on. Most of these routers are costly dedicated devices running obscure firmware which is hard to configure and not open to any changes (on the other hand, their special hardware design allows them to keep up with lots of high-speed network interfaces, better than general-purpose computer does). Fortunately, most operating systems of the UNIX family allow an ordinary computer to act as a router and forward packets belonging to the other hosts, but only according to a statically configured table."

"HPaste unlocks the rich functionality of HBase for a Scala audience. In so doing, it attempts to achieve the following goals: Provide a strong, clear syntax for querying and filtration Perform as fast as possible while maintaining idiomatic Scala client code -- the abstractions should not show up in a profiler! Re-articulate HBase's data structures rather than force it into an ORM-style atmosphere. A rich set of base classes for writing MapReduce jobs in hadoop against HBase tables. Provide a maximum amount of code re-use between general Hbase client usage, and operation from within a MapReduce job. Use Scala's type system to its advantage--the compiler should verify the integrity of the schema. Be a verbose DSL--minimize boilerplate code, but be human readable!"

"This page contains tables of known HTML rel values from specifications, formats, proposals, brainstorms, and non-trivial POSH usage in the wild. In addition, dropped and rejected values are listed at the end for comprehensiveness. usage: see how is 'rel' used. Regarding rev, see: should 'rev' even be used. This page is also the official rel registry ([1][2]). Add new and proposed rel values to the following section: HTML5 link type extension "

"This document contains current data regarding the (Publication) Status of specifications being developed by the Web Applications Working Group (aka WebApps). The data is provided in several tables: 1) API Specifications; 2) Web Components Specifications; 3) Widget Specifications; 4) Recommendations in Maintenance Mode; and 5) Specs NO Longer Active."

"A go library to improve readability in terminal apps using tabular data"