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Larry Ellison's Other Sporting Passion: Tennis - BusinessWeek

讨论: 无备份的数据库如何应对故障?

近期，在为一个客户的数据库制定紧急预案，这个数据库数据量大约在1.4T左右。由于客户没有额外的存储资源用于备份，所以数据库目前存在了很大的安全风险。虽然存储级别有一定的安全防范，但是如果遇到数据损失、坏块、文件损坏等问题，将无法应对。

Hotsos Symposium 2010 — Battle Against Any Guess Is Won

Video fragments of my session posted at the end — read on. I arrived at Omni Mandalay Hotel on Sunday evening with Dan Norris. I was flying through Chicago and it turned out that Dan was on the same flight and only few rows behind me. Small world. Preparations for the conference were very chaotic on my [...]

Using Map/Reduce for Network Forensics and Troubleshooting

Using Map/Reduce for Network Forensics and Troubleshooting:

Interesting technology stack behind ☞ xtractr — a network packet analysis tool - combining Ferret, SQLLite, V8, jQuery, Flot, Sammy and CouchDB. According to the linked article, xtractr uses a CouchDB inspired MapReduce for performing packet analysis. I’d say that if you could use it, PIG would have been nice too.

One of the huge challenges in packet forensics is that packets have incredibly rich information content and they come at many different layers each of which might be interesting on its own. Now, we didn’t want to build crazy SQL joins (I’m personally JOIN-challenged) across 90,000+ Wireshark fields. So we ended up using Map/Reduce very much inspired by CouchDB.

NoSQL Ecosystem News 2010-03-09

A Redis monitor for ZenPack (Open Source Network Monitoring and Systems Management) ☞
Just a quick reminder that today there will be an ad-hoc NoSQL & beers meetup at Bag O’Nails at 7pm. ☞ See the place on the map.
Found this amazing poster on ☞ DarkGreyIndustries blog and I was wondering if we could try to plot each NoSQL project on it. What do you think?

Credit DarkGreyIndustries blog

CTO of 10gen, MongoDB creators: We are sort of similar to MySQL or PostgreSQL in terms of how ...

Some quotes and comments from ☞ (a quite long) interview with Eliot Horowitz, CTO of 10gen, creators of MongoDB:

I think the first question you have to ask about any database these days is, “What’s the data model?”

The only thing I’d add is: “… and how does that fit my problem?”.

That whole class of problems exists because there’s a very clunky mapping from objects to relational databases. With document databases, that mapping becomes much simpler.

I only partially agree with this. There are some scenarios when mappings seem to be easier with document databases, but for very complex models (read hierarchical, multi-relational) things will remain quite the same — I am saying “quite” because you can still use some short routes, but at the end of the day it will depend also on how you’ll use that data.

I also think […] that the object databases before were actually more closely related to current graph databases than to document databases. The document database is really just taking MySQL, and instead of having a row, you have a document. So I think it’s a much simpler transition and it’s actually much closer to MySQL than a lot of people might think.

I assume that the connection with graph databases is based on the following arguments: the connectivity between objects can be very rich and while all that can be persisted it is not accomplished in a transparent way.

The second part is also worth emphasizing, as it is basically a validation of the schema-less relational database, that FriendFeed and others (see [1] and [2]) are using.

If you look at our road map for this year, there’s no one big feature. I think the only big thing we’re doing right now is getting the auto-sharding to be fully production bulletproof.

I totally agree. That auto-sharding feature has been in alpha for too long.

We are sort of similar to MySQL or PostgreSQL in terms of how you could use us, and people want all the features that they’re used to in MySQL and PostgreSQL. These include things like full-text search, SNMP, and all the assorted add-ons providing special indexing.

Is this the market MongoDB is trying to reach? Is MongoDB trying to become the new MySQL? Definitely interesting.

If there was a feature that would hurt our performance, we would think long and hard about implementing it, and we are definitely more interested in making the basics work than we are in adding more features.

I really appreciate this sort of opinionated approach, even if sometimes you’ll have to tell users a bit more about the tradeoffs.

NoSQL Wants To Be Elastic Caching When It Grows Up... Does It Really?

While I’d probably love having the talent to write such ☞big statements, I’d still prefer to get things right firstly:

Elastic Caching Platforms Are KV Stores On Steroids

[…]

NoSQL Wants To Be Elastic Caching When It Grows Up

[…]

Say “Yes” To Elastic KV Stores In Your Architecture

I confess that I see this whole NoSQL and Elastic caching discussion quite differently, as right now it looks to me that elastic caching is the one missing features (especially persistence) and not the other way around. So maybe, Mike Gaultieri, the author of the above quotes, meant the opposite: elastic caching will become NoSQL when it grows up.

Update: Jeff Darcy (@Obdurodon) ☞ is at it again with a much more detailed comment than I could come up with.

I’m not saying that IMDGs (nb: in memory data grids) aren’t valuable. They can be a very valuable part of an application’s computation or communication model. When it comes to that same application’s storage model, though, IMDGs are irrelevant and shouldn’t be presented as alternatives to various kinds of storage.

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International Women’s Day

If you do not know what International Women’s Day is: http://www.internationalwomensday.com/ Start planning your blog posts for Ada Lovelace day now (March 24th, http://findingada.com/ Ada Lovelace Day is an international day of blogging (videologging, podcasting, comic drawing etc.!) to draw attention to the achievements of women in technology and science.) To that end, I would like [...]

Operations on Graph Databases

The InfoGrid blog has started to publish a series on basic operations with graph databases. While it looks like getting a taste of graph databases was a very good start, it wasn’t meant to introduce the details of working with a graph database, something that people may not be familiar with.

So, here are the first three articles on operations with a graph database:

☞ Nodes
☞ Edges and Traversals
☞ Typing (from free form nodes/edges to “strongly typed” nodes/edges)

I just hope the series will keep going!

Presentation: Overview of HBase at Meetup

Sslides for the Overview of HBase at Meetup presentation.

My notes:

the options slide:
“scaling is built in, but extra indexing is DIY”. We had a post on this subject HBase secondary indexes
open source library for Java beans mapping to HBase tables ☞ meetup.beeno

NoSQL Ecosystem News 2010-03-08

There will be an ad-hoc NoSQL & beers meetup at Bag O’Nails at 7pm. ☞ See the place on the map. It would be nice if you could drop me a note if planning to join us, but you can just show up there!
I am in London this whole week and even if I have a crazy agenda I’d love to get a chance to meet myNoSQL readers and NoSQL users for a NoSQL chat and beer. Ping me!
Redis 1.2.4 is out ☞. It fixes an issue with replication for dump files larger than 2GB.

It is Not about SQL Scalability

An extremely popular discussion last week was Dennis Forbes’ ☞ post on SQL scalability which more or less concluded that SQL is scalable and NoSQL isn’t for everyone.

I have been lucky to catch this post early and post a long comment to it, which ended up suggesting a different conclusion: SQL is scalable. SQL scalability isn’t for everyone. NoSQL isn’t for everyone either. Unfortunately, even if Dennis agreed with this new conclusion, things got quite wrong in the comment thread.

Jeff Darcy (@Obdurodon) ☞ followed up shortly and provided five important corrections:

Vogels and company know more about scalability than just about anyone, and more […] Vogels’s definition of scalability is right or wrong on the merits, not based on who he is or what other opinions you attribute to him.

Equating “highly interrelated” with “relational” doesn’t do justice to either, and characterizing social-media workloads as “largely unrelated islands of data” is just laughable.

Your notions of I/O performance are way off.

Saying that RDBMSes are scalable, because you can (a) throw a ton of money at Oracle/Sybase or (b) do most of the work sharding and replicating something like MySQL, is a bit disingenuous.

Defining scalability in terms of “highest realistic level of usage” and “maintaining acceptable service level” is also a big pile of weasel words.

It doesn’t really matter if you are in the NoSQL camp or not, or if you feel like defending SQL/RDBMS from what some sensationalist publishers call the end of SQL/RDBMS, but you should always try to get things right. While I’ve already said this everywhere, I’ll continue telling it: like NoSQL or not, we are better off having good options around while building the next product!

ACOUG 首次活动接受报名

请发送报名邮件到 enroll@acoug.org 信箱，报名请按以下格式填写。报名请在邮件附件用文本文件(txt格式)以逗号分割符填写，请尽量填写详细，我们将按报名顺序安排活动参与。

On "Is a computer science degree a good goal?"

支付宝技术人的“黄埔军校”

支付宝在培养工程师方面也有自己的特色，在技术部，有一个培训项目“黄埔军校”，通过该军校已经为中国的电子支付产业培养了不少的技术中坚力量，也正是这个“军校”保证了支付宝在快速发展中的技术优势。 “黄埔军校”是面向P5以上的技术人员推出系列课程，而讲师则来自P7及以上的员工包括高级主管以及外部的讲师等，例如支付宝首席架构师–程立，支付宝的“大师”–冯春培等人，都是军校的讲师。军校主要通过“黄埔夜话”（每月一期）和“黄埔进阶”（两周一期）的形式进行技术沟通交流，员工可以根据自己不同的层级选择适合自己学习的套餐，自主制定每年的学习计划。学习内容： 1、黄埔技术进阶培训（专业技术类）：SOA基础, 领域建模, 非功能设计, 分布式系统设计, 性能分析, 项目进度/变更/风险管理…… 2、黄埔夜话（通用技能类）：影响力，沟通技巧，时间管理，网络时代的持续学习…… 我们希望有更多的工程师能一起参与我们的“黄埔军校”，不管是作为讲授者还是学习者。具体可以参见2010年支付宝春季招聘之工程师篇，或者支付宝招聘。 No related posts. No related posts.

NoSQL Week in Review 14

It looks like I’ve made it to the 14th edition of the NoSQL week in review, even if I was a bit sick lately and also missed a reliable internet connection for the last days. But to start of with a good news to compensate, I am quite proud to let you know that myNoSQL is an official media partner for the ☞ NoSQL event organized by 10gen in Boston on March 11th, so I hope I’ll be able to cover the event at least as well as I did for FOSDEM NoSQL event.

This last week seems to have continued to be under the sign of the Twitter interview on Cassandra, so we’ve learned about more Cassandra usecases, plus some fundamental Cassandra partitioning strategies and embedded Cassandra.

Talking about usecases, we’ve also looked at an emerging Redis usecase: queues, compared the some offline and production notes on MongoDB and looked at a very simple generic NoSQL usecase: note taking apps.

We also had a fair share of presentations and videos: Persistent graphs in Python with Neo4j, Intro to MongoDB by Alex Sharp and Relaxing with CouchDB. And there were some other posts that you can check in the NoSQL week in review section.

What’s Hot in the NoSQL World

Unfortunately while it looks like the community found interesting the 6 questions for every NoSQL project we haven’t really got some answers, so here is a I’d like to hear from you, the NoSQL readers what NoSQL projects would you be interested in hearing an answer from?

I am quite happy that you’ve found interesting the interview with Mark about FleetDB, the Clojure implemented schema-free database.

Last, but not least, in case you are planning to use MongoDB, I’d strongly suggest spending some time on offline and production notes on MongoDB as it is a very condensed way to understand quite a few details about it.

New NoSQL Releases

As far as I can tell, this week we only had Mongo 1.3.3 a development release about which you can read more ☞ here.

Update: It looks like there was also a Redis release the other day. Redis 1.2.4 fixed an issue with replication for dump files larger than 2GB.

NoSQL Week in Review

MongoDB and C#
“Even if according to the 10gen survey the number of people using MongoDB from or on a Windows environment is pretty small, I continue to see some articles here and there, so I thought that the Windows MongoDB users will benefit from getting a chance to see what have been written so far. …”

(Tags: mongodb, csharp)
Getting Up to Speed with CouchDB and Java
“If you haven’t done it already, this article will probably get you started in no time. …”

(Tags: couchdb, java, tutorial)
Google’s MapReduce patent - no threat to stuffed elephants
“Unfortunately these are just pure assumptions and even if NoSQL projects argue (and can probably prove it) that their implementations are not tied to the Google’s MapReduce paper, I still don’t see things as clear as they should be with an emerging technology looking for broad adoption. …”

(Tags: mapreduce)
Access Control Lists with Graph Databases
“It looks like MyNoSQL’s initiative to compare same scenarios implemented by some of the graph databases is catching up and after Neo4j blog published an extensive article on ☞ access control lists with Neo4j , the guys from InfoGrid picked up the challenge and provided ☞ their own solution . …”

(Tags: neo4j, infogrid, graph database)
Cassandra Usecases: Survey Results
“A very nice summary of Cassandra usecases …”

(Tags: usecase, cassandra)
FleetDB: An Interview with Mark McGranaghan
“FleetDB is an MIT licensed schema-free database implemented primarily on Clojure that provides a combination of schema-free records, declarative queries, optimizing query planner and a few more interesting features. While not exactly targeting those scenarios that involve tons of data and require massive scalability, FleetDB seems to be a nice tool to have around when prototyping your next app. Mark McGranaghan, the project creator, has been kind enough to answer a couple of questions for us. …”

(Tags: document store, document database, fleetdb)
CouchDB Chat App with _changes and Evently
“Watching Chris Anderson ( @jchris ) demoing CouchDB and CouchApp is always fun and relaxing. In this short video, Chris is showing the details of a simple chat app built on top of CouchApp. …”

(Tags: couchdb, video, couchapp)
6 Valid Questions for Every (NoSQL) Project
“6 extremely interesting questions that I think everyone should try to answer before deciding on using a new storage solution being it NoSQL or not. …”

(Tags: nosql debate)
SQL is scalable. SQL scalability isn’t for everyone. NoSQL isn’t for everyone either
“Dennis Forbes has a great post about RDBMS scalability and the hype around NoSQL that ends up with something like “SQL is scalable and NoSQL isn’t for everyone”. I have posted a long comment to the original post and suggested a slight modification to that conclusion: SQL is scalable. SQL scalability isn’t for everyone. NoSQL isn’t for everyone either…”

(Tags: nosql debate)
Cassandra Partitioning Strategies
“This should become part of the official Cassandra documentation. …”

(Tags: cassandra)
MongoDB and File System Durability Explained
“MongoDB durability is a tradeoff. You can see the details of the various file system durability methods and compare those with MongoDB implementation …”

(Tags: mongodb)
Note taking apps a la NoSQL
“Sometimes the best way to learn about a new technology or tool is to find a project that might be interesting to you, start playing with it and why not end up customizing and extending it to fit your needs. …”

(Tags: node.js, mongodb, usecase, redis, python)
Presentation: Persistent graphs in Python with Neo4j
“These are the slides Tobias Ivarsson presented at PyCon to introduce Neo4j with a Python flavor. …”

(Tags: python, neo4j, graph database, django)
Redis Queues: An Emerging Usecase
“We’ve been covering tons of Redis usecases, not to mention this amazing list of ideas . Lately, it looks like there is a new emerging usecase that Redis can be proud of: queues. …”

(Tags: node.js, python, usecase, redis, ruby)
3 Sweet Spots for MapReduce
“The presentation given by Andrew Pavlo “MapReduce and Parallel DBMSs” identifies the following 3 sweet spots for MapReduce: …”

(Tags: mapreduce)
Cassandra As An Embedded Service
“With the help of the community I’ve built an embedded cassandra service ideal for unit testing and perhaps other uses. I’ve also built a cleanup utility that helps wipe out all data before the service starts running so the combination of both provides isolation etc. Now each test process runs an in-process, embedded instance of cassandra. …”

(Tags: cassandra)
Presentation: Intro to MongoDB by Alex Sharp
“We’ve never got enough introductions to NoSQL systems. Embedded below are the slides from Alex Sharp’s ( @ajsharp ): Intro to MongoDB presentation. Just to allow you quick overview, you can find below also the text only version. …”

(Tags: mongodb, presentation)
Video: Will Leinweber: Relaxing with CouchDB
“If we never have enough intro presentations to MongoDB , why would we have enough CouchDB videos ? …”

(Tags: video, couchdb, presentation)
Offline and Production Notes on MongoDB
“Last week has featured two of the most interesting posts about MongoDB: first a MongoDB documentation overview and the second, a set of notes from running MongoDB in production. …”

(Tags: mongodb, usecase)

Before closing up with our weekly wish, I was wondering if you noticed the new logo on the right column. And with that, I wish us all a great NoSQL week!

CBO arithmetic

Anyone who’s keen to keep track of how the cost based optimizer does its arithmetic, and how that arithmetic can change with version, may want to keep an eye on this blog.

Treedump – 2

In an earlier article about investigating the state of an index in detail I supplied a piece of SQL that would analyse an index (no, not using the Analyze command) and summarise the number of entries in each leaf block that currently held any entries at all. Here’s a sample of the type of output [...]

Oracle 11gR2 Grid Infrastructure — Memory Footprint

Upgrading to 11g Release Grid Infrastructure? You probably want to read on… Oracle 11g Release 2 Grid Infrastructure has been dramatically redesigned compare to 10g and 11gR1 Clusterware. Coming with impressive set of new features, Grid Infrastructure also uses much more memory. While RAM is rather inexpensive these days, it does pose an inconvenience in some [...]

ACOUG - All China Oracle User Group Start

有时候我们不去做一件事情的借口是忙，事实上不是，我想即便我自己每个月抽出一两天，花费1~2小时去分享一个技术主题是非常容易的，唯一的借口是我们的懒惰。另外一个问题是，很多人不愿意为没有收益的事情付出，而我的观点是，有付出一定是有回报的，虽然这个回报可能在很久之后。而我愿意做这件事，更主要的是如前所述，在赤裸裸的为了追逐金钱而努力之外，我们还一直保有一点理想主义，我相信大家都有。如果缺少一个组织者，那我们愿意来试试看。

ACOUG！

有感于国外Oracle用户组的发达，我跟eygle从今年开始尝试组建中国的Oracle用户组，All China Oracle User Group。我们的站点是acoug.org。我们希望能够为全中国的Oracle技术爱好者提供一个交流与活动的平台，我们希望能够激发更多人对于Oracle技术以及相关知识的兴趣，我们希望沉浸在这些技术中的人员获得更多的乐趣。这是我跟eygle一直以来的一个理想，但愿我们能为此付出并且有所回报。我们认识到中国目前掌握Oracle技术的人员广度和深度都跟美国、欧洲甚或是印度存在着较大差距，这需要我们更有热情的去付出，但愿在不久的将来ACOUG在全球Oracle用户组中占据一席之地。 ACOUG计划定期举行线下活动，而线下活动也将是ACOUG未来工作的重点，邀请著名的业内人士（不仅限于国内）来进行主题演讲，并围绕相关主题进行广泛的讨论从而使更多人获益。 eygle的文章有关于ACOUG更多的阐述，请猛击这里。预计在这个月的3月20日（周六）开展ACOUG第一次线下活动，详细的地点、场地以及演讲主题还未确定，但是请有兴趣的朋友时刻关注ACOUG网站，欢迎参加。

简化SQLULDR2的命令行选项设置

看到Kamus对SQLULDR2的留言后, 破有感触. 人们应当比较关注, 他们想要的功能用起来方便是否, 关键并不在于功能的多少. 而SQLULDR2的众多的命令行选项, 也确实有些让人发晕, 包括我自已. 为了方便大多数人使用, 简化了SQLULDR2的命令行帮助, 简化到如下所示. SQL*UnLoader: Fast Oracle Text Unloader (GZIP), Release 3.0.1 (@) Copyright Lou Fangxin (AnySQL.net) 2004 - 2010, all rights reserved. Usage: SQLULDR2 keyword=value [,keyword=value,...] Valid Keywords: user = username/password@tnsname sql = SQL file name query = select [...]

Blogrotate #18: The Weekly Roundup of News for System Administrators

Is it Friday already? Where does the time go? Lots of stuff going on this week–here’s a few of the things that I found interesting. Operating Systems Russia Today-TV announced the existence of “Red Star”, the new OS developed in North Korea and based on Linux. I found this by way of Slashdot [...]

Advert – ODTUG

Latest update from the ODTUG about my presentations at Kaleidoscope 2010. My presentations are at the following times: 28th June 10:30 - 12:00 How to write Efficient SQL 29th June 16:45 - 17:45 Co-operating with the database 30th June 08:30 - 09:30 Why isn't Oracle using my index Apart from the three presentations I’ll also be sitting on the “Database Experts” panel [...]

Log Buffer #181: a Carnival of the Vanities for DBAs

The 181st edition of Log Buffer has been published by Gary Myers on his Sydney Oracle Lab. Having recently moved his blog, Gary approached the Log Buffer coordinator to volunteer for an edition because he knows that, with LB being a popular and established destination in the database blogoshphere, it would help him broadcast his new [...]

On the Perils of Importing Remote Tags in Git

So mothers keep your hackers at home Don’t let them journey all alone Tell them this world is full of danger And to shun the repositories of strangers – The Tag Set of Strangers, (with apologies [...]

Offline and Production Notes on MongoDB

Last week has featured two of the most interesting posts about MongoDB: first coming from Mathias Meyer (@roidrage) ☞ offline investigation of MongoDB and the second, a set of notes from running MongoDB in production published on ☞ Boxed Iced blog.

If you are interested in getting started with MongoDB, I’d encourage you take the time to go through Mathias’ post which covers the following aspects (I’ve also included a couple of comments)

collections and capped collections
Note: I couldn’t really understand the usage of namespaces and the implication on indexes
data format
references
Note: I’d also strongly suggest taking a look at MongoDB documentation on ☞ schema design for more details
indexes
updates
querying
durability
Note: we have covered before the MongoDB durability tradeoff in much detail
replication
caching
backup
storage
concurrency
Note: I’d really appreciate more details on this topic as it is not completely clear if all access (both read and writes) is serialized or just writes are serialized (or not?); also the impact on indexes is not clear either.
memory
GridFS
protocol access
Note: We argued before that access protocols are extremely important. MongoDB is one of the NoSQL solutions using a proprietary protocols and tries to “compensate” for that with tons of libraries
sharding
Note: probably biased, but I still wait for the moment MongoDB sharding would become at least beta.

MapReduce support seems to be missing from Mathias notes, but luckily we have that covered for you: MongoDB MapReduce tutorial.

While keeping in mind that some of these features are not unique to MongoDB and can be found in other systems, you should be ready to cross check your app requirements with the lessons learned by the guys at Boxed Ice:

namespace limits

We split our customers across (currently) 3 MongoDB databases because there is a namespace limit of 24,000 per database. This is essentially the number of collections + number of indexes.
initial sync/replication of large databases

Our databases are very large and it takes about 48-72 hours to fully sync all our current data onto a new slave in a different DC (via a site-to-site VPN for security). During this time you’re at risk because the slave is not up to date.
initial sync “slows” things

When doing a fresh sync from a master to a slave, we have observed a “slowdown” in our application response times.
index creation blocks

However, if you have an existing collection and create a new index on it then that process will block the database until the index is created.
efficiency of reclaiming diskspace

We have found that there is a massive discrepancy between a master and a freshly copied slave.

Even if not every application will have to deal with the size Boxed Ice is dealing, I couldn’t stop noticing that parts of the process of scaling MongoDB were really painful. Or as Sergio Bossa (@sbtourist) put it in ☞ one of the comments:

Anyways, it seems indeed you had almost the same problems you would had with a MySQL solution:

Huge data to deal with.

Manual sharding.

Sync/replication delays.

So why didn’t you evaluate to switch to a more “large-scale” nosql solution like Cassandra or Riak?

Last but not least, drop me a note if you are planning to use or already using MongoDB in production and you’d like to share your experience with the NoSQL community

日志文件切换

本文主要翻译自Jonathan Lewis的Blog Entry Log File Switch, 最后关于checkpoint部分,我添加了部分我自己的注释(以斜体字显示). 日志文件切换几天前,我发布一个简单的查询语句,它通过查询v$log_history视图展示日志文件切换之间的时间间隔.如果你运行这个脚本,并且认为你的系统的日志文件切换频率不合适,那么你该如何处理呢? 如果时间间隔太短(由于日志文件频繁切换引发的checkpoint动作可能会导致DBWR进程过于活跃),可以通过新增新的更大的日志文件并删除旧的日志文件来解决. 如果切换的时间间隔太久,或者是时间间隔波动太大,而你希望使日志切换更加有规律,那么可以使用参数archive_lag_target(在Oracle 9i中引入)来为日志文件切换设置一个超时值(单位为秒).如果日志文件没有在指定的时间间隔内发生切换,那么系统会强制其切换日志,并且归档对应的已经使用的在线重做日志. 在此之前,人们一般使用类似于cron或者dbms_job一类的工具来实现(通过执行一条”alter system switch logfile“命令).从Oracle 9i开始,利用这个参数来实现就简单多了. 附录 Instance Recovery Checkpoint 实例恢复触发的检查点 Media Recovery Checkpoint 介质恢复触发的检查点 Thread Checkpoint 单个instance关闭以及log file switch触发的检查点 Interval Checkpoint log_checkpoint_timeout参数超时触发的检查点 Tablespace Checkpoint 表空间online/offline/read only/read write操作对应的检查点 PQ Tablespace Checkpoint 不清楚具体原因 Close Database Checkpoint Incremental Checkpoint 常规的检查点动作 Local Database Checkpoint 对应于alter system checkpoint Global Database Checkpoint 对应于alter system checkpoint all命令的检查点. Object Reuse Checkpoint 对应于truncate table操作的检查点 Object Checkpoint 对应于drop table的检查点操作 Oracle支持多种不同类型的checkpoint,这个列表会随着Oracle版本的不同而有所不同.Oracle 10.2.0.3的对应的checkpoint看似有如下这些: 在这些中间,我认为日志文件切换时引发的checkpoint可能是ThreadCheckpoint. No related posts. No related posts.

Video: Will Leinweber: Relaxing with CouchDB

If we never have enough intro presentations to MongoDB, why would we have enough CouchDB videos?

Embedded below is a video of Will Leinweber presenting Relaxing with CouchDB (38 minutes)

Presentation: Intro to MongoDB by Alex Sharp

We’ve never got enough introductions to NoSQL systems. Embedded below are the slides from Alex Sharp’s (@ajsharp): Intro to MongoDB presentation. Just to allow you quick overview, you can find below also the text only version.

Text-only version of Intro to MongoDB

Slide: 1

Intro to MongoDB

Alex Sharp

twitter: @ajsharp
Slide: 2
So what is MongoDB?
Slide: 3
First and foremost…
Slide: 4
IT’S THE NEW HOTNESS!!!
Slide: 5

omgomgomg

SHINY OBJECTS

omgomgomg
Slide: 6
MongoDB (from “humongous”) is a scalable, high-performance, open source, schema-free, document-oriented database.

- mongodb.org
Slide: 7
Philosophy
Slide: 8
Philosophy

“One size fits all” approach no longer applies
Slide: 9
Philosophy

Non-relational DBs scale more easily, especially horizontally
Slide: 10
Philosophy

Focus on speed, performance, flexibility and scalability
Slide: 11
Philosophy

Not concerned with transactional stuff and relational semantics
Slide: 12
Philosophy

DBs should be an on-demand commodity, in a cloud-like fashion
Slide: 13
Philosophy

Mongo tries to achieve the performance of traditional key-value stores while maintaining functionality of traditional RDBMS
Slide: 14
Features
Slide: 15
Features

Standard database stuff
Slide: 16
Features

Standard database stuff

Indexing
Slide: 17
Features

Standard database stuff

Indexing

replication/failover support
Slide: 18
Features: Document Storage

Documents are stored in BSON (binary JSON)
Slide: 19
BSON is a binary serialization of JSON-like objects

Features: Document Storage
Slide: 20
Features: Document Storage

This is extremely powerful, b/c it means mongo understands JSON natively
Slide: 21
Features: Document Storage

Any valid JSON can be easily imported and queried
Slide: 22
Features

Schema-less; very flexible
Slide: 23
Features

Schema-less; very flexible

no more blocking ALTER TABLE
Slide: 24
Features

Auto-sharding (alpha)
Slide: 25
Features

Makes for easy horizontal scaling
Slide: 26
Features

Map/Reduce
Slide: 27
Features

Very, very fast
Slide: 28
Features

Super easy to install
Slide: 29
Features

Strong with major languages
Slide: 30
Features

Document-oriented = flexible
Slide: 31
Features: Querying

Rich, javascript-based query syntax
Slide: 32
Features: Querying

Rich, javascript-based query syntax

Allows us to deep, nested queries
Slide: 33
Features: Querying

Rich, javascript-based query syntax

Allows us to do deep, nested queries

db.order.find( { shipping: { carrier: "usps" } } );
Slide: 34
Features: Querying

Rich, javascript-based query syntax

Allows us to deep, nested queries

db.order.find( { shipping: { carrier: "usps" } } );

shipping is an embedded document (object)
Slide: 35
Features: Binary Object Store

Efficient binary large object store via GridFS
Slide: 36
Features: Binary Object Store

Efficient binary large object store via GridFS

i.e. store images, videos, anything
Slide: 37
Concepts
Slide: 38
Concepts: Document-oriented

Think of “documents” as database records
Slide: 39
Concepts: Document-oriented

Think of “documents” as database records

Documents are basically just JSON objects that Mongo stores in binary
Slide: 40
Concepts: Document-oriented

Think of “collections” as database tables
Slide: 44
Concept Mapping

RDBMS (mysql, postgres)

Tables

Records/rows

Queries return record(s)

MongoDB

Collections

Documents/objects

Queries return a cursor

???
Slide: 45
Concepts: Cursors

Queries return “cursors” instead of collections
Slide: 46
Concepts: Cursors

Queries return “cursors” instead of collections

A cursor allows you to iterate through the result set
Slide: 47
Concepts: Cursors

Queries return “cursors” instead of collections

A cursor allows you to iterate through the result set

A big reason for this is performance
Slide: 48
Concepts: Cursors

Queries return “cursors” instead of collections

A cursor allows you to iterate through the result set

A big reason for this is performance

Much more efficient than loading all objects into memory
Slide: 49
Concepts: Cursors

The find() function returns a cursor object
Slide: 50
Concepts: Cursors

The find() function returns a cursor object

var cursor = db.logged_requests.find({ 'status_code' : 200 })

cursor.hasNext() // "true"

cursor.forEach( function (item) {

print(tojson(item))

});

cursor.hasNext() // "false"
Slide: 51
Cool Features
Slide: 52
Cool Features

Capped collections
Slide: 53
Cool Features

Capped collections

Fixed-sized, limited operation, auto-LRU age-out collections
Slide: 54
Cool Features

Capped collections

Fixed-sized, limited operation, auto-LRU age-out collections

Fixed insertion order
Slide: 55
Cool Features

Capped collections

Fixed-sized, limited operation, auto-LRU age-out collections

Fixed insertion order

Super fast
Slide: 56
Cool Features

Capped collections

Fixed-sized, limited operation, auto-LRU age-out collections

Fixed insertion order

Super fast

Ideal for logging and caching
Slide: 57
Cool Uses

Data Warehouse

Mongo understands JSON natively
Slide: 58
Cool Uses

Data Warehouse

Mongo understands JSON natively

Very powerful for analysis
Slide: 59
Cool Uses

Data Warehouse

Mongo understands JSON natively

Very powerful for analysis

Query a bunch of data from some web service
Slide: 60
Cool Uses

Data Warehouse

Mongo understands JSON natively

Very powerful for analysis

Query a bunch of data from some web service

Import into mongo (mongoimport -f filename.json)
Slide: 61
Cool Uses

Data Warehouse

Mongo understands JSON natively

Very powerful for analysis

Query a bunch of data from some web service

Import into mongo (mongoimport -f filename.json)

Analyze to your heart’s content
Slide: 62
Cool Uses

Harmonyapp.com

Large rails app for building websites (kind of a CMS)
Slide: 63
Cool Uses

Hardcore debugging

Spit out large amounts of data
Slide: 64
Limitations

Transaction support
Slide: 65
Limitations

Transaction support

Relational integrity
Slide: 66
Resources

http://mongodb.org

http://blog.mongodb.org/post/172254834/mongodb-is-fantastic-for-logging

http://github.com/ajsharp/mongo-conf

http://www.mongodb.org/display/DOCS/Tutorial

http://www.mongodb.org/display/DOCS/Use+Cases

NoSQL Ecosystem News 2010-03-04

Would you like to play with a tapir on your couch? If yes, then you have TapirWiki, a basic wiki built on CouchDB ☞
I almost always fall for these geeky things: Tokyo Cabinet API for Clojure ☞

Cassandra As An Embedded Service

Cassandra As An Embedded Service:

With the help of the community I’ve built an embedded cassandra service ideal for unit testing and perhaps other uses. I’ve also built a cleanup utility that helps wipe out all data before the service starts running so the combination of both provides isolation etc. Now each test process runs an in-process, embedded instance of cassandra.

While I do agree with the main goal behind this initiative (i.e. testability), I do think that a stub solution (take for example RockingChair a Ruby stub library for CouchDB) would be a much better fit and would perform a lot better. As the original post mentions, the embedded Cassandra service requires each test to be run in a separate JVM and as you can imagine that will be far from being quick.

French Translations

I’ve just had another email about translating some of my postings, in this case into French. Franck Pachot has asked if he can post translations on his blog, and the answer is yes. His intention is to select the postings which are more about how the database engine works, rather than examples of specific solutions, and [...]

支付宝开通聋哑客户绿色通道

网购以及网络支付已经成为网民日常生活的一部分，正如马云所言，电子支付的发展可以不让老太太去银行排队缴费，我们也希望通过自己的努力让更多的人享受到支付发展带来的价值，通过提升用户体验，降低使用门槛，并以此逐步提高整个行业的水平。为了方便聋哑的客户遇到问题时可以第一时间找到我们的小二解决问题，近日，支付宝特别开通聋哑客户旺旺绿色通道。只要客户通过邮件提交申请，附上证明材料，经支付宝小二审核后，即可开通与支付宝客服小二的旺旺绿色通道。在线服务时间：周一到周五，早上9点到下午6点。报名方法：发送申请邮件至green@alipay.com 邮件中请写明您的淘宝会员ID，对应的支付宝账户名（邮箱 /手机），同时请附上您本人的身份证原件扫描件及残疾人证明原件的扫描件。在收到您的申请邮件后，我们将不会回复邮件。客服小二会在三个工作日内完成审核，审核通过后,我们将为您开通旺旺绿色通道。该申请方法长期有效。绿色通道的定义：支付宝绿色通道是指支付宝通过在线旺旺的形式专门为聋哑客户提供咨询的服务。聋哑客户可以通过绿色通道咨询支付宝业务以及本人支付宝账户的问题，小二将竭力为您解答；如果涉及账户问题的处理，支付宝仍需按照现有流程受理。 PS：如果您已经是淘宝绿色通道的客户，支付宝在收到淘宝反馈后为您开通绿色通道服务。同时支付宝绿色通道客户信息也将提供给淘宝。让我们沟通方便起来！开通聋哑客户绿色通道是支付宝庞大的用户体验提升计划的一部分，此前已经相继开放全新设计的“个人版”用户平台等项目。 No related posts. No related posts.

3 Sweet Spots for MapReduce

The presentation given by Andrew Pavlo “MapReduce and Parallel DBMSs”, embedded below for reference, identifies the following 3 sweet spots for MapReduce:

Extract-Transform-Load

“Read Once” data sets

Allows for quick-and-dirty data analysis

Semi-Structured Data

Can easily store semi-structured data which would otherwise be awkward to be stored in RDBMS

Limited Budget Operations

the alternative, parallel DBMSs are expensive

When speaking about the possible MapReduce and RDBMS integration, something that for example Oracle has already been considering, Andrew and his colleagues mention the following advantages:

What can MapReduce learn from Databases?

Fast query times.

Schemas.

Supporting tools.

What can Databases learn from MapReduce?

Ease of use, “out of box” experience.

Attractive fault tolerance properties.

Fast load times.

SQL Server 2

Following on from my posting about the presentation (about Enterprise databases) that I did at a Microsoft event on SQL Server 2008, I’ve just had an article on how to design efficient SQL published on a website that’s strongly biased towards SQL Server developers – using an example of SQL written in the SQL Server dialect. It’s [...]

Redis Queues: An Emerging Usecase

We’ve been covering tons of Redis usecases, not to mention this amazing list of ideas. Lately, it looks like there is a new emerging usecase that Redis can be proud of: queues.

Now if that already sounds interesting then I guess you could just take a look at QR, a Python ☞ GitHub hosted project that makes it easy to create queues, stacks and deques on top of Redis. For some help on using it you could check Ted Nyman’s posts on ☞ queues and ☞ deques and stacks. Another option would be to head to Resque, a Ruby ☞ GitHub hosted library for creating and processing jobs using Redis queues.

Anyway, if you don’t have yet an idea on how this can be useful, then I hope these following posts will wet your appetite. David Czarnecki’s ☞ article covers a very simple Redis-based queue scenario: inter-application communication (basically the two apps will get an easy way to pass from one to another any kind of messages). If this is still not enough, then Paull Gross’s ☞ post is introducing you to a web proxy built using node.js and Redis queues for high availability.

Last, but not least, I should emphasize the fact that what sets aside Redis as a good tool for this sort of things is not the fact that Redis is a extremely fast, persistent key-value store, but rather Redis native support for ☞ data structures like lists, sets and ordered sets and a set of specific ☞ commands to deal with these.

Define the row filters for reports in WebChart

Supposed that we are creating a report of the top 3 highest salary employees for every department (check the Oracle demo table : SCOTT.EMP). If the table is stored in Oracle database, we could generate the report with...

Presentation: Persistent graphs in Python with Neo4j

These are the slides Tobias Ivarsson (@thobe) presented at PyCon to introduce Neo4j with a Python flavor.

I really liked this slide in particular:

Python code starts at slide 23. A couple of my comments:

I am not really sure I understand how the Python scripts are accessing the Neo4j storage when using CPython (Neo4j is supposed to run in a JVM)
traversals in graph databases are somewhat synonymous to queries
having the traversal implemented like classes extending neo4j.Traversal doesn’t really look Pythonic
Django and Neo4j can work together

Live RAC SIG Web-cast Today: Oracle ASM 11g — The Evolution

Just a quick announcements… If you didn’t manage to attend my presentation, Oracle 11g ASM — The Evolution, during RMOUG or other conferences, you have a chance to see it online today. I’m doing it a web-cast at RAC SIG. It’s today, 4-Mar-10 at 12:00pm EST (9:00am PST).

为DataReport增加条件过滤功能

为了查询出保存在员工表(SCOTT.EMP)中, 每个部门工资最高的三个人, 如果是Oracle数据库, 大家可以使用Windows分组汇总函数来轻松地实现, 如下所示. SELECT * FROM ( SELECT DEPTNO, EMPNO, ENAME, SAL, RANK() OVER (PARTITION BY DEPTNO ORDER BY SAL DESC) RNK FROM EMP ) WHERE RNK <= 3 但如果员工表存放在MySQL数据库, 或其他数据库, 如SQLLite中, 要实现同样的功能, 就比较复杂了, 至少我现在都还不会. 但利用DataReport以前开发的功能, 及刚增加的条件过滤功能, 就可以轻松实现这个需求. webchart.query_1=select deptno, empno, ename, sal from emp webchart.express_1=rank|x|rnk::sal|deptno webchart.filter_1=3.5-x|rank webchart.sort_1=deptno,rank webchart.group_1=1 如果Filter中的公司算出来的值小于0, 那么这条记录就会被删除, 在这个例子中, 如果排名这一列的值大于3, 这个公式算出来的值就为负数, [...]

votedisk & OCR maintains

Votedisk can’t be add & remove while CRS is running, or else it would corrupt something. OCR can be added & replaced while CRS is running. ENV: Oracle Clusterware 10.2.0.4 Detailed logfile: While CRS is still running… # ./crsctl add css votedisk /dev/rdsk/ c4t60060E80056F160000006F1600000669d0s1 Cluster is not in a ready state for [...]

kcbgtcr 错误小记

kcbgtcr 是Oracle数据库最重要的函数之一，其含义为：Kernal Cache Buffer GeT Cosistents Read,也就是数据库的一致性读操作，后面的代码有很多种，代表在不同的层面上出现问题。 Metalink 的 Note 415773.1 - Diagnostics and Solutions for kcbgtcr() Related Internal Errors记录了与此相关的很多问题。

换号码了

换手机号码了，原号码永久保留电话功能，不再支持短信功能；新号码，要知道我新号码，可以继续打原来号码问我的，嘿嘿。

Note taking apps a la NoSQL

Sometimes the best way to learn about a new technology or tool is to find a project that might be interesting to you, start playing with it and why not end up customizing and extending it to fit your needs.

While these days you can find tons of note taking applications for your mobile, desktop or “in the cloud”, I think this usecase is extremely easy to understand and it will allow you to focus on the underlying technologies and not some complicated logic.

Snip = Node.js + Redis ☞

This is a basic application that would allow you to store code snippets and have some syntax coloring when displaying them. Source code is available on ☞ bitbucket.

YourCached.info = MongoDB + Python ☞

Another basic application that allows you to store notes and bookmarks. A lot of functionality you’d expect from such an application is missing and that could be a good excuse for you to play with its source code available on ☞ GitHub and add exactly what you’d like.

I am pretty sure I have missed a lot of similar apps, so please do forward yours to be added to the list. I am pretty sure that building an extensive list like we did for NoSQL Twitter apps or NoSQL-based blog engines will be both fun and useful.

RobB's Question about M/M/m

Index Efficiency 3

My last posting about indexes was an introduction to a pl/sql script that estimated the sizes that your indexes would be if you rebuilt them at a given percentage of utilisation for leaf blocks. By comparing these estimates with the actual size of the indexes you can get a quick report of indexes that are probably significantly [...]

MongoDB and File System Durability Explained

MongoDB and File System Durability Explained:

I just found this post from Ivan Voras which is covering exactly what I’ve written a while back about MongoDB durability being a tradeoff. As a plus, Ivan’s post also includes a quick summary of various file system durability methods:

fully synchronous writes
partially synchronous writes
logging
journaling
soft updates

I only hope that based on these two posts, all MongoDB users will become aware of the MongoDB durability behavior (or differently put durability tradeoff).

Cassandra Partitioning Strategies

Cassandra Partitioning Strategies:

I think this should become part of the official Cassandra documentation.

When building a Cassandra cluster, the “key” question (sorry, that’s weak) is whether to use the RandomPartitioner (RP), or the OrderPreservingPartitioner (OPP). These control how your data is distributed over your nodes. Once you have chosen your partitioner, you cannot change without wiping your data, so think carefully!

2010年支付宝春季招聘之运维校园招聘篇

您想验证自己的IT潜质吗？您想开启互联网职业之门吗？您想寻求一个施展IT技术的舞台吗？学习就是工作，挑战自己，只等你来。体验全新校园招聘模式。岗位要求：理工科专业本科学历，有较强的学习能力和责任心，熟悉互联网。职位详情查询请登陆支付宝招聘网站：http://job.alipay.com/ 简历投递邮箱：resume@alipay.com No related posts. No related posts.

Define different SQLs for different databases in WebChart

Some people like to run WebChart demo application on Oracle database, while some other people may like to run WebChart demo application on MySQL database. Seems I have to write two WebChart demo applications, one for Oracle, another...

每个(NoSQL)项目都需要考虑的6个问题

http://nosql.mypopescu.com/post/422286341/6-valid-questions-for-every-nosql-project No related posts.

解决Legato问题，相信LTT吧！

最近遇到某省的legato备份问题，这个问题在春节后一来就遇到了。在legato界面，我们看到的报错是： ……02/24/10 12:01:00 nsrd: savegroup info: starting db_arch (with 1 client(s))02/24/10 12:01:15 nsrd: Jukebox 'm...

2010年支付宝春季招聘之工程师篇

支付宝招聘 No related posts.

NO .CN!

现在个人要注册并且使用一个cn域名，手续需要： 1. 购买域名，付费 2. 付费成功之后提供审核资料（包括一些打印件和身份证的扫描件） 3. 审核通过之后去工信部备案网站中自行备案 4. 备案成功之后域名开通为此条款我特地致电了国内某域名注册商的客服。 Q：费用计费是从什么时候开始的？ A：从成功付费以后。 Q：那如果工信部备案拖了我几个月都备案不成功，这段时间如何补偿？ A：没有补偿。 Q：如果备案不成功怎么办？ A：那你就重复提交备案申请，总会成功的。 Q：如果我厌倦了备案的繁琐，决定放弃这个域名，既然你们自从我注册了这个域名之后就没有提供任何服务，可以退款吗？ A：抱歉，一旦付费成功了，就无法退款。 cao你大爷，贵国真是威武。老子惹不起总躲得起，本来一直说cn域名还代表咱是中国人，用着挺爱国的，但是从今以后决定不再注册任何cn域名。做出这个决定，就好比方鸿渐老爹抱怨的那样“他爱国而国不爱他，大有青年守节的孀妇不见宠于翁姑的怨抑”，又跟五折叔说的那样怎么老是有种挥之不去的忧郁感呢？日你个麻哟。最后看一眼工信部备案管理网站糟糕至极的用户体验。 1. “建议使用:1024 * 768 分辨率”，这种土掉渣的提示应该在进入21世纪以后就很少看到了吧。 2. “刷新验证码”这个链接是假的，毫无用处。不偷工减料会死啊。 3. 每个新网站必须要重新注册新用户才可以备案，即使登陆备案网站是通过用户名而不是网站名。也就是一个名下有10个网站的主办者需要注册10次才可以，这是什么脑子思考出来的模式？ 4. 作为一个互联网域名备案的网上办公系统，毫无专业性。为什么“典型问题指南”是用弹出窗口这样被用户诟病了多年的方式来表达？为什么在用户登陆之后页面左侧的树状结构在Firefox和Chrome浏览器里面一团糟完全没法正常使用？不知道这个网站用了多少钱造出来的，我只想说在如今这个年代大概再差劲的架站初学者应该也会比这个做的好看吧。

公告：3月3日起用户登录支付宝将默认进个人版系统

3月2日，支付宝消息，自3日开始，个人用户登录支付宝网站后将默认进入个人版系统界面，但用户可选择返回原操作界面。相关负责人表示，在经过一段时间的公测之后，新的系统性能得到了较大的改善，默认进入个人版系统将有助于大幅提升用户体验。据了解，为了提升个人用户在支付、交易管理等需求上的使用效率与体验，支付宝自2008年开始筹划个人版系统，此前已于2009年支付宝5周年之际，向50万用户开放了个人版的公测。此后还于2010年1月8日起全面开放该入口。自1月8日开放以来，支付宝总共收到近两千位用户的反馈意见，主要集中在消费记录、提现、安全校验等方面，基于用户提出的问题，支付宝进行了不断的优化，问题得到了妥善的解决。与此前的功能相比，当前版本为个人用户提供了更多实用的功能。通过“生活助手”、“联系人”、“财务管理”等功能矩阵，支付宝个人版已经形成了一个“生活支付圈”，与个人的日常生活实现高度融合，支付宝营造的个人网络消费大后台逐渐成型。支付宝是国内最大、最繁忙的第三方电子支付平台，高达2.8亿的个人用户与46万家商户通过支付宝实现更便捷的生活与更有效的网络贸易。 No related posts. No related posts.

不平衡的索引?

本文翻译自Jonathan Lewis早年写在dbazine上的文章unbalanced indexes? 本文的word版本可以到此处下载不平衡的索引? by Jonathan Lewis 网络上有多篇介绍Oracle索引实现机制的文章,都提及需要经常重建索引.在这些文章中的某处,总是会出现这样一段简短的描述,索引会如何变的不平衡,以及可能导致的后果.很不幸,它们好像忽视了这样一个事实,Oracle使用的B-tree机制是一种”平衡B-tree”索引,也就是说,索引无法变得不平衡. “平衡”到底意味着什么? 既然Oracle的索引使用的是平衡B-tree,为什么还有如此多的人相信他们的索引会变得不平衡呢? 另外,平衡B-tree到底又是什么呢? 第二个问题的答案可能能够帮助我们得到第一个问题的答案. 从技术角度讲,平衡B-tree的显要特性是,在任意时间点,任何叶子节点(leaf block)到根节点(root block)的距离都相等,平衡是指从顶部到底部的平衡. 就Oracle来说,执行一个treedump命令就可以很容易发现这一点,如图-1所示: select object_id from user_objects where object_type = 'INDEX' and object_name = 'T1_IDX1' -- and subobject_name = . . . ; alter session set events ' 'immediate trace name treedump level N'; 图-1: 一次索引树转储涉及到的步骤首先,需要找到你想要转储(dump)索引或者索引分区(index partition)的object_id;接着,将object_id作为level的参数来调用treedump事件. 如果检查这个索引树(tree dump)转储生成的跟踪文件,你将发现类似于图-2中所示的结果. branch: 0x14001aa 20971946 (.. level 2) branch: 0x14003ef 20972527 (.. level 1) leaf: 0x14001ab 20971947 (..) leaf: 0x14001ac [...] No related posts.

RAC+ASM 3 years in production. Stories to share (slides from RMOUG10)

Here are the slides from my presentation at RMOUG 2010. I am not sure how much sense all this will make without my comments. We may do it in a webinar if there is sufficient interest. Regardless I will probably be doing it again at some point in the future. RAC+ASM: Stories to Share View more presentations [...]

ORA-00600 3020 错误案例一则

出现这个错误后，恢复中断，无法继续，3020错误在Metalink上的解释，主要原因是在恢复时发现Redo里面记录的信息和被恢复的数据块信息不一致，导致恢复无法继续。比如Update Some record from 3 to 2,结果发现该记录根本不是3，恢复无法继续。

Database tuning: ratio vs. rate

Baron makes an excellent point in Why you should ignore MySQL’s key cache hit ratio — ratio is not the same as rate. Furthermore, rate is [often] the important thing to look at. This is something that, at Pythian, we internalized a long time ago when thinking about MySQL tuning. In fact, mysqltuner 2.0 [...]

When the ALTER TABLE privilege is not enough to run ALTER TABLE

I recently granted ALTER access in MySQL so a user could run the ALTER TABLE command . However after I granted the necessary privileges, the user was still not able to perform the tasks needed. Reproducing the issue using a test instance, I granted a test user the required privileges and MySQL reported no errors [...]

Some use cases of Cassadra

source : http://n2.nabble.com/Cassandra-users-survey-td4040068.html survey By Jonathan Ellis of Rackspace user site application others evaluated Jonathan Ellis-3 Rackspace stats collection (testing, almost production),Mail & Apps division (early testing) HBase, Hypertable, dynomite,and Voldemort Ryan King Twitter storage for all tweets a custom mysql impl, voldemort, hbase,mongodb, memcachdb, hypertable, and others Edmond Lau Ooyala store and serve our near real-time video analytics data HBase, Cassandra, Voldemort, [...] No related posts.