It has recently been brought to attention that the MySQL man pages have been relicensed. The change was made rather silently going from MySQL 5.5.30 to MySQL 5.5.31. This affects all pages in the man/ directory of the source code.

You can tell the changes have come during this short timeframe (5.5.30->5.5.31). The old manual pages were released under the following license:

This documentation is free software; you can redistribute it and/or modify it only under the terms of the GNU General Public License as published by the Free Software Foundation; version 2 of the License.

The new man pages (following 5.5.31 and greater – still valid for 5.5.32) are released under the following license:

This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited.

This is clearly not very friendly of MySQL at Oracle.

More information on the official website: http://www.unqlite.org/

When an unregistered user edits Wikipedia, he or she is identified by his or her IP address. These IP addresses are translated to users’ approximate geographic location. Edits by registered users do not have associated IP information, so the map actually represents only a small portion of the total edit activity on Wikipedia.

Built using d3, DataMaps, freegeoip.net, and the Wikimedia RecentChanges IRC feed, broadcast through wikimon. Sourceavailable on github.

Built by Stephen LaPorte and Mahmoud Hashemi.

GOOG:US  858.800 USD13.080 1.55%

Google glass is getting hyped and trashed all at the same time and it’s not even here yet. Meanwhile, Android’s marketplace dominance and Google’s nicely executed moves to mobile ads are contributing to the valuation. And of course, Microsoft is suffering as their tablet/smartphone offerings flounder and the PC business that they dominate shrinks.

It’s an interesting thought, thinking through what company could be the next one to reach the quarter-trillion valuation mark, which is the valuation that both Google and Microsoft just recently shot past. In the far future, could (or perhaps not too far into the future, as in later this year?), Oracle and Cisco and Intel reach that plateau? They each have been worth that much before.

Perhaps you’ve heard about the next generation of databases roughly classified as NoSQL databases? These databases are generally much better than RDBMS at scaling, performance, and ease-of-development (e.g. in NoSQL the object-relational impedance mismatch usually disappears). Unfortunately, many talks on NoSQL are very academic and general. Not this one. This session will introduce the ideas around the so-called NoSQL movement, and we’ll learn how to leverage MongoDB (a popular open source NoSQL db) to build .NET applications using LINQ as the data access language. We’ll build out a .NET application using LINQ and MongoDB in a series of interactive demos using Visual Studio 2012 and C#.

1) Strange issues with Lua scripting.

2) Not reconfigured reappearing master using Sentinel.

3) Server continusly trying to save on save error.

(This version of Redis may also help with AOF and slow / busy disks and latency issues.)

* [FIX] Throttle BGSAVE attempt on saving error.
* [FIX] redis-cli: raise error on bad command line switch.
* [FIX] Redis/Jemalloc Gitignore were too aggressive.
* [FIX] Test: fix RDB test checking file permissions.
* [FIX] Sentinel: always redirect on master->slave transition.
* [FIX] Lua updated to version 5.1.5. Fixes rare scripting issues.
* [NEW] AOF: improved latency figures with slow/busy disks.
* [NEW] Sentinel: turn old master into a slave when it comes back.
* [NEW] More explicit panic message on out of memory.
* [NEW] redis-cli: --latency-history mode implemented.

Download: http://redis.io/download

More details available here:

http://www.chris-granger.com/2013/04/28/light-table-040/

https://code.google.com/p/leveldb/

LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.

Features

• Keys and values are arbitrary byte arrays.
• Data is stored sorted by key.
• Callers can provide a custom comparison function to override the sort order.
• The basic operations are Put(key,value), Get(key), Delete(key).
• Multiple changes can be made in one atomic batch.
• Users can create a transient snapshot to get a consistent view of data.
• Forward and backward iteration is supported over the data.
• Data is automatically compressed using the Snappy compression library.
• External activity (file system operations etc.) is relayed through a virtual interface so users can customize the operating system interactions.
• Detailed documentation about how to use the library is included with the source code.

Limitations

• This is not a SQL database. It does not have a relational data model, it does not support SQL queries, and it has no support for indexes.
• Only a single process (possibly multi-threaded) can access a particular database at a time.
• There is no client-server support builtin to the library. An application that needs such support will have to wrap their own server around the library.

Performance

Here is a performance report (with explanations) from the run of the included db_bench program. The results are somewhat noisy, but should be enough to get a ballpark performance estimate.

Setup

We use a database with a million entries. Each entry has a 16 byte key, and a 100 byte value. Values used by the benchmark compress to about half their original size.

   LevelDB:    version 1.1
   Date:       Sun May  1 12:11:26 2011
   CPU:        4 x Intel(R) Core(TM)2 Quad CPU    Q6600  @ 2.40GHz
   CPUCache:   4096 KB
   Keys:       16 bytes each
   Values:     100 bytes each (50 bytes after compression)
   Entries:    1000000
   Raw Size:   110.6 MB (estimated)
   File Size:  62.9 MB (estimated)

Write performance

The “fill” benchmarks create a brand new database, in either sequential, or random order. The “fillsync” benchmark flushes data from the operating system to the disk after every operation; the other write operations leave the data sitting in the operating system buffer cache for a while. The “overwrite” benchmark does random writes that update existing keys in the database.

   fillseq      :       1.765 micros/op;   62.7 MB/s     
   fillsync     :     268.409 micros/op;    0.4 MB/s (10000 ops)
   fillrandom   :       2.460 micros/op;   45.0 MB/s     
   overwrite    :       2.380 micros/op;   46.5 MB/s

Each “op” above corresponds to a write of a single key/value pair. I.e., a random write benchmark goes at approximately 400,000 writes per second.

Each “fillsync” operation costs much less (0.3 millisecond) than a disk seek (typically 10 milliseconds). We suspect that this is because the hard disk itself is buffering the update in its memory and responding before the data has been written to the platter. This may or may not be safe based on whether or not the hard disk has enough power to save its memory in the event of a power failure.

Read performance

We list the performance of reading sequentially in both the forward and reverse direction, and also the performance of a random lookup. Note that the database created by the benchmark is quite small. Therefore the report characterizes the performance of leveldb when the working set fits in memory. The cost of reading a piece of data that is not present in the operating system buffer cache will be dominated by the one or two disk seeks needed to fetch the data from disk. Write performance will be mostly unaffected by whether or not the working set fits in memory.

   readrandom   :      16.677 micros/op;  (approximately 60,000 reads per second)
   readseq      :       0.476 micros/op;  232.3 MB/s    
   readreverse  :       0.724 micros/op;  152.9 MB/s

LevelDB compacts its underlying storage data in the background to improve read performance. The results listed above were done immediately after a lot of random writes. The results after compactions (which are usually triggered automatically) are better.

   readrandom   :      11.602 micros/op;  (approximately 85,000 reads per second)   
   readseq      :       0.423 micros/op;  261.8 MB/s    
   readreverse  :       0.663 micros/op;  166.9 MB/s

Some of the high cost of reads comes from repeated decompression of blocks read from disk. If we supply enough cache to the leveldb so it can hold the uncompressed blocks in memory, the read performance improves again:

   readrandom   :       9.775 micros/op;  (approximately 100,000 reads per second before compaction)
   readrandom   :       5.215 micros/op;  (approximately 190,000 reads per second after compaction)

- An integration with Oracle Coherence has been provided that allows Oracle NoSQL Database to be used as a cache for Oracle Coherence applications, also allowing applications to directly access cached data from Oracle NoSQL Database. Documentation can be foundhttp://bit.ly/14e6jEP.

- Oracle NoSQL Database Enterprise Edition now has support for semantic technologies. Specifically, the Resource Description Framework (RDF), SPARQL query language, and a subset of the Web Ontology Language (OWL) are now supported. These capabilities are referred to as the RDF Graph feature of Oracle NoSQL Database. The RDF Graph feature provides a Java-based interface to store and query semantic data in Oracle NoSQL Database Enterprise Edition. Documentation can be found http://bit.ly/Y7aQX4.

Find the complete list of changes in the change log.

Changelog: http://bit.ly/ZweZDS
Download: http://bit.ly/yLGVg3