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• Windows Azure – Write, Run or Use Software

  - by BuckWoody

  Windows Azure is a platform that has you covered, whether you need to write software, run software that is already written, or Install and use “canned” software whether you or someone else wrote it. Like any platform, it’s a set of tools you can use where it makes sense to solve a problem. The primary location for Windows Azure information is located at http://windowsazure.com. You can find everything there from the development kits for writing software to pricing, licensing and tutorials on all of that. I have a few links here for learning to use Windows Azure – although it’s best if you focus not on the tools, but what you want to solve. I’ve got it broken down here into various sections, so you can quickly locate things you want to know. I’ll include resources here from Microsoft and elsewhere – I use these same resources in the Architectural Design Sessions (ADS) I do with my clients worldwide. Write Software Also called “Platform as a Service” (PaaS), Windows Azure has lots of components you can use together or separately that allow you to write software in .NET or various Open Source languages to work completely online, or in partnership with code you have on-premises or both – even if you’re using other cloud providers. Keep in mind that all of the features you see here can be used together, or independently. For instance, you might only use a Web Site, or use Storage, but you can use both together. You can access all of these components through standard REST API calls, or using our Software Development Kit’s API’s, which are a lot easier. In any case, you simply use Visual Studio, Eclipse, Cloud9 IDE, or even a text editor to write your code from a Mac, PC or Linux.  Components you can use: Azure Web Sites: Windows Azure Web Sites allow you to quickly write an deploy websites, without setting a Virtual Machine, installing a web server or configuring complex settings. They work alone, with other Windows Azure Web Sites, or with other parts of Windows Azure. Web and Worker Roles: Windows Azure Web Roles give you a full stateless computing instance with Internet Information Services (IIS) installed and configured. Windows Azure Worker Roles give you a full stateless computing instance without Information Services (IIS) installed, often used in a "Services" mode. Scale-out is achieved either manually or programmatically under your control. Storage: Windows Azure Storage types include Blobs to store raw binary data, Tables to use key/value pair data (like NoSQL data structures), Queues that allow interaction between stateless roles, and a relational SQL Server database. Other Services: Windows Azure has many other services such as a security mechanism, a Cache (memcacheD compliant), a Service Bus, a Traffic Manager and more. Once again, these features can be used with a Windows Azure project, or alone based on your needs. Various Languages: Windows Azure supports the .NET stack of languages, as well as many Open-Source languages like Java, Python, PHP, Ruby, NodeJS, C++ and more.   Use Software Also called “Software as a Service” (SaaS) this often means consumer or business-level software like Hotmail or Office 365. In other words, you simply log on, use the software, and log off – there’s nothing to install, and little to even configure. For the Information Technology professional, however, It’s not quite the same. We want software that provides services, but in a platform. That means we want things like Hadoop or other software we don’t want to have to install and configure.  Components you can use: Kits: Various software “kits” or packages are supported with just a few clicks, such as Umbraco, Wordpress, and others. Windows Azure Media Services: Windows Azure Media Services is a suite of services that allows you to upload media for encoding, processing and even streaming – or even one or more of those functions. We can add DRM and even commercials to your media if you like. Windows Azure Media Services is used to stream large events all the way down to small training videos. High Performance Computing and “Big Data”: Windows Azure allows you to scale to huge workloads using a few clicks to deploy Hadoop Clusters or the High Performance Computing (HPC) nodes, accepting HPC Jobs, Pig and Hive Jobs, and even interfacing with Microsoft Excel. Windows Azure Marketplace: Windows Azure Marketplace offers data and programs you can quickly implement and use – some free, some for-fee.   Run Software Also known as “Infrastructure as a Service” (IaaS), this offering allows you to build or simply choose a Virtual Machine to run server-based software.  Components you can use: Persistent Virtual Machines: You can choose to install Windows Server, Windows Server with Active Directory, with SQL Server, or even SharePoint from a pre-configured gallery. You can configure your own server images with standard Hyper-V technology and load them yourselves – and even bring them back when you’re done. As a new offering, we also even allow you to select various distributions of Linux – a first for Microsoft. Windows Azure Connect: You can connect your on-premises networks to Windows Azure Instances. Storage: Windows Azure Storage can be used as a remote backup, a hybrid storage location and more using software or even hardware appliances.   Decision Matrix With all of these options, you can use Windows Azure to solve just about any computing problem. It’s often hard to know when to use something on-premises, in the cloud, and what kind of service to use. I’ve used a decision matrix in the last couple of years to take a particular problem and choose the proper technology to solve it. It’s all about options – there is no “silver bullet”, whether that’s Windows Azure or any other set of functions. I take the problem, decide which particular component I want to own and control – and choose the column that has that box darkened. For instance, if I have to control the wiring for a solution (a requirement in some military and government installations), that means the “Networking” component needs to be dark, and so I select the “On Premises” column for that particular solution. If I just need the solution provided and I want no control at all, I can look as “Software as a Service” solutions. Security, Pricing, and Other Info  Security: Security is one of the first questions you should ask in any distributed computing environment. We have certification info, coding guidelines and more, even a general “Request for Information” RFI Response already created for you.   Pricing: Are there licenses? How much does this cost? Is there a way to estimate the costs in this new environment? New Features: Many new features were added to Windows Azure - a good roundup of those changes can be found here. Support: Software Support on Virtual Machines, general support.    

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• Class instance clustering in object reference graph for multi-entries serialization

  - by Juh_

  My question is on the best way to cluster a graph of class instances (i.e. objects, the graph nodes) linked by object references (the -directed- edges of the graph) around specifically marked objects. To explain better my question, let me explain my motivation: I currently use a moderately complex system to serialize the data used in my projects: "marked" objects have a specific attributes which stores a "saving entry": the path to an associated file on disc (but it could be done for any storage type providing the suitable interface) Those object can then be serialized automatically (eg: obj.save()) The serialization of a marked object 'a' contains implicitly all objects 'b' for which 'a' has a reference to, directly s.t: a.b = b, or indirectly s.t.: a.c.b = b for some object 'c' This is very simple and basically define specific storage entries to specific objects. I have then "container" type objects that: can be serialized similarly (in fact their are or can-be "marked") they don't serialize in their storage entries the "marked" objects (with direct reference): if a and a.b are both marked, a.save() calls b.save() and stores a.b = storage_entry(b) So, if I serialize 'a', it will serialize automatically all objects that can be reached from 'a' through the object reference graph, possibly in multiples entries. That is what I want, and is usually provides the functionalities I need. However, it is very ad-hoc and there are some structural limitations to this approach: the multi-entry saving can only works through direct connections in "container" objects, and there are situations with undefined behavior such as if two "marked" objects 'a'and 'b' both have a reference to an unmarked object 'c'. In this case my system will stores 'c' in both 'a' and 'b' making an implicit copy which not only double the storage size, but also change the object reference graph after re-loading. I am thinking of generalizing the process. Apart for the practical questions on implementation (I am coding in python, and use Pickle to serialize my objects), there is a general question on the way to attach (cluster) unmarked objects to marked ones. So, my questions are: What are the important issues that should be considered? Basically why not just use any graph parsing algorithm with the "attach to last marked node" behavior. Is there any work done on this problem, practical or theoretical, that I should be aware of? Note: I added the tag graph-database because I think the answer might come from that fields, even if the question is not.

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• eSTEP Newsletter October 2012 now available

  - by uwes

  Dear Partners,We would like to inform you that the October '12 issue of our Newsletter is now available.The issue contains information to the following topics:News from CorpOracle Announces Oracle Solaris 11.1 at Oracle OpenWorld; Oracle Announces Oracle Exadata X3 Database In-Memory Machine; Oracle Enterprise Manager 12c introduces New Tools and Programs for Partners; Oracle Unveils First Industry-Specific Engineered System - the Oracle Networks Applications Platform,;  Oracle Unveils Expanded Oracle Cloud Offerings; Oracle Outlines Plans to Make the Future Java During JavaOne 2012 Strategy Keynote; Some interesting Java Facts and Figures; Oracle Announces MySQL 5.6 Release Candidate Technical Section What's up with LDoms (4 tech articles); Oracle SPARC T4 Systems cut Complexity, cost of Cryptographic Tasks; PeopleSoft Enterprise Financials 9.1; PeopleSoft HCM 9.1 combined online and batch benchmark,; Product Update Bulletin Oracle Solaris Cluster Oct 2012; Sun ZFS Storage 7420; SPARC Product Line Update; SPARC M-series -  New DAT 160 plus EOL of M3000 series; SPARC SuperCluster and SPARC T4 Servers Included in Enterprise Reference Architecture Sizing Tool; Oracle MagazineLearning & EventsRecently delivered Techcasts: An Update after the Oracle Open World, An Update on OVM Server for SPARC; Update to Oracle Database ApplianceReferencesBridgestone Aircraft Tire Reduces Required Disk Capacity by 50% with Virtualized Storage Solution; Fiat Group Automobiles Aligns Operational Decisions with Strategy by Using End-to-End Enterprise Performance Management System; Birkbeck, University of London Develops World-Class Computer Science Facilities While Reducing Costs with Ultrareliable and Scalable Data Infrastructure How toIntroducing Oracle System Assistant; How to Prepare a ZFS Storage Appliance to Serve as a Storage Device; Migrating Oracle Solaris 8 P2V with Oracle Database 10.2 and ASM; White paper on Best Practices for Building a Virtualized SPARC Computing Environment, How to extend the Oracle Solaris Studio IDE with NetBeans Plug-Ins; How I simplified Oracle Database 11g Installation on Oracle Linux 6You find the Newsletter on our portal under eSTEP News ---> Latest Newsletter. You will need to provide your email address and the pin below to get access. Link to the portal is shown below.URL: http://launch.oracle.com/PIN: eSTEP_2011Previous published Newsletters can be found under the Archived Newsletters section and more useful information under the Events, Download and Links tab. Feel free to explore and any feedback is appreciated to help us improve the service and information we deliver.Thanks and best regards,Partner HW Enablement EMEA

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• Online ALTER TABLE in MySQL 5.6

  - by Marko Mäkelä

  This is the low-level view of data dictionary language (DDL) operations in the InnoDB storage engine in MySQL 5.6. John Russell gave a more high-level view in his blog post April 2012 Labs Release – Online DDL Improvements. MySQL before the InnoDB Plugin Traditionally, the MySQL storage engine interface has taken a minimalistic approach to data definition language. The only natively supported operations were CREATE TABLE, DROP TABLE and RENAME TABLE. Consider the following example: CREATE TABLE t(a INT); INSERT INTO t VALUES (1),(2),(3); CREATE INDEX a ON t(a); DROP TABLE t; The CREATE INDEX statement would be executed roughly as follows: CREATE TABLE temp(a INT, INDEX(a)); INSERT INTO temp SELECT * FROM t; RENAME TABLE t TO temp2; RENAME TABLE temp TO t; DROP TABLE temp2; You could imagine that the database could crash when copying all rows from the original table to the new one. For example, it could run out of file space. Then, on restart, InnoDB would roll back the huge INSERT transaction. To fix things a little, a hack was added to ha_innobase::write_row for committing the transaction every 10,000 rows. Still, it was frustrating that even a simple DROP INDEX would make the table unavailable for modifications for a long time. Fast Index Creation in the InnoDB Plugin of MySQL 5.1 MySQL 5.1 introduced a new interface for CREATE INDEX and DROP INDEX. The old table-copying approach can still be forced by SET old_alter_table=0. This interface is used in MySQL 5.5 and in the InnoDB Plugin for MySQL 5.1. Apart from the ability to do a quick DROP INDEX, the main advantage is that InnoDB will execute a merge-sort algorithm before inserting the index records into each index that is being created. This should speed up the insert into the secondary index B-trees and potentially result in a better B-tree fill factor. The 5.1 ALTER TABLE interface was not perfect. For example, DROP FOREIGN KEY still invoked the table copy. Renaming columns could conflict with InnoDB foreign key constraints. Combining ADD KEY and DROP KEY in ALTER TABLE was problematic and not atomic inside the storage engine. The ALTER TABLE interface in MySQL 5.6 The ALTER TABLE storage engine interface was completely rewritten in MySQL 5.6. Instead of introducing a method call for every conceivable operation, MySQL 5.6 introduced a handful of methods, and data structures that keep track of the requested changes. In MySQL 5.6, online ALTER TABLE operation can be requested by specifying LOCK=NONE. Also LOCK=SHARED and LOCK=EXCLUSIVE are available. The old-style table copying can be requested by ALGORITHM=COPY. That one will require at least LOCK=SHARED. From the InnoDB point of view, anything that is possible with LOCK=EXCLUSIVE is also possible with LOCK=SHARED. Most ALGORITHM=INPLACE operations inside InnoDB can be executed online (LOCK=NONE). InnoDB will always require an exclusive table lock in two phases of the operation. The execution phases are tied to a number of methods: handler::check_if_supported_inplace_alter Checks if the storage engine can perform all requested operations, and if so, what kind of locking is needed. handler::prepare_inplace_alter_table InnoDB uses this method to set up the data dictionary cache for upcoming CREATE INDEX operation. We need stubs for the new indexes, so that we can keep track of changes to the table during online index creation. Also, crash recovery would drop any indexes that were incomplete at the time of the crash. handler::inplace_alter_table In InnoDB, this method is used for creating secondary indexes or for rebuilding the table. This is the ‘main’ phase that can be executed online (with concurrent writes to the table). handler::commit_inplace_alter_table This is where the operation is committed or rolled back. Here, InnoDB would drop any indexes, rename any columns, drop or add foreign keys, and finalize a table rebuild or index creation. It would also discard any logs that were set up for online index creation or table rebuild. The prepare and commit phases require an exclusive lock, blocking all access to the table. If MySQL times out while upgrading the table meta-data lock for the commit phase, it will roll back the ALTER TABLE operation. In MySQL 5.6, data definition language operations are still not fully atomic, because the data dictionary is split. Part of it is inside InnoDB data dictionary tables. Part of the information is only available in the *.frm file, which is not covered by any crash recovery log. But, there is a single commit phase inside the storage engine. Online Secondary Index Creation It may occur that an index needs to be created on a new column to speed up queries. But, it may be unacceptable to block modifications on the table while creating the index. It turns out that it is conceptually not so hard to support online index creation. All we need is some more execution phases: Set up a stub for the index, for logging changes. Scan the table for index records. Sort the index records. Bulk load the index records. Apply the logged changes. Replace the stub with the actual index. Threads that modify the table will log the operations to the logs of each index that is being created. Errors, such as log overflow or uniqueness violations, will only be flagged by the ALTER TABLE thread. The log is conceptually similar to the InnoDB change buffer. The bulk load of index records will bypass record locking. We still generate redo log for writing the index pages. It would suffice to log page allocations only, and to flush the index pages from the buffer pool to the file system upon completion. Native ALTER TABLE Starting with MySQL 5.6, InnoDB supports most ALTER TABLE operations natively. The notable exceptions are changes to the column type, ADD FOREIGN KEY except when foreign_key_checks=0, and changes to tables that contain FULLTEXT indexes. The keyword ALGORITHM=INPLACE is somewhat misleading, because certain operations cannot be performed in-place. For example, changing the ROW_FORMAT of a table requires a rebuild. Online operation (LOCK=NONE) is not allowed in the following cases: when adding an AUTO_INCREMENT column, when the table contains FULLTEXT indexes or a hidden FTS_DOC_ID column, or when there are FOREIGN KEY constraints referring to the table, with ON…CASCADE or ON…SET NULL option. The FOREIGN KEY limitations are needed, because MySQL does not acquire meta-data locks on the child or parent tables when executing SQL statements. Theoretically, InnoDB could support operations like ADD COLUMN and DROP COLUMN in-place, by lazily converting the table to a newer format. This would require that the data dictionary keep multiple versions of the table definition. For simplicity, we will copy the entire table, even for DROP COLUMN. The bulk copying of the table will bypass record locking and undo logging. For facilitating online operation, a temporary log will be associated with the clustered index of table. Threads that modify the table will also write the changes to the log. When altering the table, we skip all records that have been marked for deletion. In this way, we can simply discard any undo log records that were not yet purged from the original table. Off-page columns, or BLOBs, are an important consideration. We suspend the purge of delete-marked records if it would free any off-page columns from the old table. This is because the BLOBs can be needed when applying changes from the log. We have special logging for handling the ROLLBACK of an INSERT that inserted new off-page columns. This is because the columns will be freed at rollback.

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• 4.8M wasn't enough so we went for 5.055M tpmc with Unbreakable Enterprise Kernel r2 :-)

  - by wcoekaer

  We released a new set of benchmarks today. One is an updated tpc-c from a few months ago where we had just over 4.8M tpmc at $0.98 and we just updated it to go to 5.05M and $0.89. The other one is related to Java Middleware performance. You can find the press release here. Now, I don't want to talk about the actual relevance of the benchmark numbers, as I am not in the benchmark team. I want to talk about why these numbers and these efforts, unrelated to what they mean to your workload, matter to customers. The actual benchmark effort is a very big, long, expensive undertaking where many groups work together as a big virtual team. Having the virtual team be within a single company of course helps tremendously... We already start with a very big server setup with tons of storage, many disks, lots of ram, lots of cpu's, cores, threads, large database setups. Getting the whole setup going to start tuning, by itself, is no easy task, but then the real fun starts with tuning the system for optimal performance -and- stability. A benchmark is not just revving an engine at high rpm, it's actually hitting the circuit. The tests require long runs, require surviving availability tests, such as surviving crashes -and- recovery under load. In the TPC-C example, the x4800 system had 4TB ram, 160 threads (8 sockets, hyperthreaded, 10 cores/socket), tons of storage attached, tons of luns visible to the OS. flash storage, non flash storage... many things at high scale that all have to be perfectly synchronized. During this process, we find bugs, we fix bugs, we find performance issues, we fix performance issues, we find interesting potential features to investigate for the future, we start new development projects for future releases and all this goes back into the products. As more and more customers, for Oracle Linux, are running larger and larger, faster and faster, more mission critical, higher available databases..., these things are just absolutely critical. Unrelated to what anyone's specific opinion is about tpc-c or tpc-h or specjenterprise etc, there is a ton of effort that the customer benefits from. All this work makes Oracle Linux and/or Oracle Solaris better platforms. Whether it's faster, more stable, more scalable, more resilient. It helps. Another point that I always like to re-iterate around UEK and UEK2 : we have our kernel source git repository online. Complete changelog of the mainline kernel, and our changes, easy to pull, easy to dissect, easy to know what went in when, why and where. No need to go log into a website and manually click through pages to hopefully discover changes or patches. No need to untar 2 tar balls and run a diff.

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• ZFS for Database Log Files

  - by user12620111

  I've been troubled by drop outs in CPU usage in my application server, characterized by the CPUs suddenly going from close to 90% CPU busy to almost completely CPU idle for a few seconds. Here is an example of a drop out as shown by a snippet of vmstat data taken while the application server is under a heavy workload. # vmstat 1  kthr      memory            page            disk          faults      cpu  r b w   swap  free  re  mf pi po fr de sr s3 s4 s5 s6   in   sy   cs us sy id  1 0 0 130160176 116381952 0 16 0 0 0 0  0  0  0  0  0 207377 117715 203884 70 21 9  12 0 0 130160160 116381936 0 25 0 0 0 0 0  0  0  0  0 200413 117162 197250 70 20 9  11 0 0 130160176 116381920 0 16 0 0 0 0 0  0  1  0  0 203150 119365 200249 72 21 7  8 0 0 130160176 116377808 0 19 0 0 0 0  0  0  0  0  0 169826 96144 165194 56 17 27  0 0 0 130160176 116377800 0 16 0 0 0 0  0  0  0  0  1 10245 9376 9164 2  1 97  0 0 0 130160176 116377792 0 16 0 0 0 0  0  0  0  0  2 15742 12401 14784 4 1 95  0 0 0 130160176 116377776 2 16 0 0 0 0  0  0  1  0  0 19972 17703 19612 6 2 92  14 0 0 130160176 116377696 0 16 0 0 0 0 0  0  0  0  0 202794 116793 199807 71 21 8  9 0 0 130160160 116373584 0 30 0 0 0 0  0  0 18  0  0 203123 117857 198825 69 20 11 This behavior occurred consistently while the application server was processing synthetic transactions: HTTP requests from JMeter running on an external machine. I explored many theories trying to explain the drop outs, including: Unexpected JMeter behavior Network contention Java Garbage Collection Application Server thread pool problems Connection pool problems Database transaction processing Database I/O contention Graphing the CPU %idle led to a breakthrough: Several of the drop outs were 30 seconds apart. With that insight, I went digging through the data again and looking for other outliers that were 30 seconds apart. In the database server statistics, I found spikes in the iostat "asvc_t" (average response time of disk transactions, in milliseconds) for the disk drive that was being used for the database log files. Here is an example:                     extended device statistics     r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 2053.6    0.0 8234.3  0.0  0.2    0.0    0.1   0  24 c3t60080E5...F4F6d0s0     0.0 2162.2    0.0 8652.8  0.0  0.3    0.0    0.1   0  28 c3t60080E5...F4F6d0s0     0.0 1102.5    0.0 10012.8  0.0  4.5    0.0    4.1   0  69 c3t60080E5...F4F6d0s0     0.0   74.0    0.0 7920.6  0.0 10.0    0.0  135.1   0 100 c3t60080E5...F4F6d0s0     0.0  568.7    0.0 6674.0  0.0  6.4    0.0   11.2   0  90 c3t60080E5...F4F6d0s0     0.0 1358.0    0.0 5456.0  0.0  0.6    0.0    0.4   0  55 c3t60080E5...F4F6d0s0     0.0 1314.3    0.0 5285.2  0.0  0.7    0.0    0.5   0  70 c3t60080E5...F4F6d0s0 Here is a little more information about my database configuration: The database and application server were running on two different SPARC servers. Storage for the database was on a storage array connected via 8 gigabit Fibre Channel Data storage and log file were on different physical disk drives Reliable low latency I/O is provided by battery backed NVRAM Highly available: Two Fibre Channel links accessed via MPxIO Two Mirrored cache controllers The log file physical disks were mirrored in the storage device Database log files on a ZFS Filesystem with cutting-edge technologies, such as copy-on-write and end-to-end checksumming Why would I be getting service time spikes in my high-end storage? First, I wanted to verify that the database log disk service time spikes aligned with the application server CPU drop outs, and they did: At first, I guessed that the disk service time spikes might be related to flushing the write through cache on the storage device, but I was unable to validate that theory. After searching the WWW for a while, I decided to try using a separate log device: # zpool add ZFS-db-41 log c3t60080E500017D55C000015C150A9F8A7d0 The ZFS log device is configured in a similar manner as described above: two physical disks mirrored in the storage array. This change to the database storage configuration eliminated the application server CPU drop outs: Here is the zpool configuration: # zpool status ZFS-db-41   pool: ZFS-db-41  state: ONLINE  scan: none requested config:         NAME                                     STATE         ZFS-db-41                                ONLINE           c3t60080E5...F4F6d0  ONLINE         logs           c3t60080E5...F8A7d0  ONLINE Now, the I/O spikes look like this:                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1053.5    0.0 4234.1  0.0  0.8    0.0    0.7   0  75 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1131.8    0.0 4555.3  0.0  0.8    0.0    0.7   0  76 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1167.6    0.0 4682.2  0.0  0.7    0.0    0.6   0  74 c3t60080E5...F8A7d0s0     0.0  162.2    0.0 19153.9  0.0  0.7    0.0    4.2   0  12 c3t60080E5...F4F6d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1247.2    0.0 4992.6  0.0  0.7    0.0    0.6   0  71 c3t60080E5...F8A7d0s0     0.0   41.0    0.0   70.0  0.0  0.1    0.0    1.6   0   2 c3t60080E5...F4F6d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1241.3    0.0 4989.3  0.0  0.8    0.0    0.6   0  75 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1193.2    0.0 4772.9  0.0  0.7    0.0    0.6   0  71 c3t60080E5...F8A7d0s0 We can see the steady flow of 4k writes to the ZIL device from O_SYNC database log file writes. The spikes are from flushing the transaction group. Like almost all problems that I run into, once I thoroughly understand the problem, I find that other people have documented similar experiences. Thanks to all of you who have documented alternative approaches. Saved for another day: now that the problem is obvious, I should try "zfs:zfs_immediate_write_sz" as recommended in the ZFS Evil Tuning Guide. References: The ZFS Intent Log Solaris ZFS, Synchronous Writes and the ZIL Explained ZFS Evil Tuning Guide: Cache Flushes ZFS Evil Tuning Guide: Tuning ZFS for Database Performance

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• Red Gate Software announces speaker line up for US SQL in the City tour

  SQL in the City is a free, full day training and networking event for database professionals. After the success of last year’s event, Red Gate has expanded the event to cover six cities from sea to shining sea, including: New York, Austin, San Francisco, Chicago, Boston, and Seattle. Compress live data by 73% Red Gate's SQL Storage Compress reduces the size of live SQL Server databases, saving you disk space and storage costs. Learn more.

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• Disk Is Cheap! ORLY?

  People often conclude that the cheap price of storage is a license to use as much as possible, but there is a cost. Solomon Rutzky talks about the issues you may face if you are not careful with your storage decisions. Join SQL Backup’s 35,000+ customers to compress and strengthen your backups "SQL Backup will be a REAL boost to any DBA lucky enough to use it." Jonathan Allen. Download a free trial now.

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• CTERA Adds Data Protection to Linux File Systems

  <b>Enterprise Storage Forum: </b>"CTERA Networks is giving the Linux Ext3 file system additional data protection in the form of new snapshot capabilities. The file system is also the basis of the company's Cloud-Attached Storage appliances, the C200 and CloudPlug."

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• Oracle Magazine - Sep/Oct 2010

  Oracle Magazine Sep/Oct features articles on Oracle Exadata, Database Security, Oracle Enterprise Manager 11g, PL/Scope to analyze your PL/SQL, Using Oracle Essbase Release 11.1.2 Aggregate Storage Option Databases, Oracle Application Express 4.0 Websheets, Oracle Automatic Storage Management disk groups, Tom Kyte revisits a classic, recounts Cardinality Feedback, and remembers SQL*Plus and much more.

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• Showing ZFS some LOVE

  - by Kristin Rose

  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} L is for the way you look at us, and O because we’re Oracle, but V is very, very, extra ordinary, and E, well that’s obvious… E is because Oracle’s new Sun ZFS Storage Appliance is Excellent, and here at OPN, we like spell out the obvious!  If you haven’t already heard, the Sun ZFS Appliance has “A simple, GUI-driven setup and configuration, solid price-performance and world-class Oracle support behind it. The CRN Test Center recommends the Sun ZFS Storage”. Read more about what CRN said here. Oracle's Sun ZFS Appliance family delivers enterprise-class network attached storage (NAS) capabilities with leading Oracle integration, simplicity, efficiency, performance, and TCO.  The systems offer an easy way to manage and expand your storage environment at a lower cost, with more efficiency, better data integrity, and higher performance when compared with competitive NAS offerings. Did we mention that set up, including configuring, will take you less than an hour since it all comes in one box and is so darn simple to use? So if you L-O-V-E what you’re hearing about Oracle’s Sun Z-F-S, learn more by watching the video below, and visiting any of our available resources . It Had to Be You, The OPN Communications Team

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• New Marketing Kit Available

  - by Cinzia Mascanzoni

  Check out the NEW Marketing Kit- "Oracle Storage for Database & Engineered Systems" designed to help partners generate sales for Oracle Storage.

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• 13.10 upgrade dropping wifi [on hold]

  - by Daryl

  Almost a complete newb here. After my last upgrade from 12.04 to 13.10 my wifi now randomly drops. The only way I can get a signal back is a shutdown and restart otherwise it shows no network is even available to connect to. Had no problems until the upgrade. Any help would be appreciated. H/W path Device Class Description ==================================================== system h8-1534 (H2N64AA#ABA) /0 bus 2AC8 /0/0 memory 64KiB BIOS /0/4 processor AMD FX(tm)-6200 Six-Core Processor /0/4/5 memory 288KiB L1 cache /0/4/6 memory 6MiB L2 cache /0/4/7 memory 8MiB L3 cache /0/d memory 10GiB System Memory /0/d/0 memory DIMM Synchronous [empty] /0/d/1 memory 4GiB DIMM DDR3 Synchronous 1600 MHz (0.6 ns) /0/d/2 memory 2GiB DIMM DDR3 Synchronous 1600 MHz (0.6 ns) /0/d/3 memory 4GiB DIMM DDR3 Synchronous 1600 MHz (0.6 ns) /0/100 bridge RD890 PCI to PCI bridge (external gfx0 port B) /0/100/0.2 generic RD990 I/O Memory Management Unit (IOMMU) /0/100/2 bridge RD890 PCI to PCI bridge (PCI express gpp port B) /0/100/2/0 display Turks PRO [Radeon HD 7570] /0/100/2/0.1 multimedia Turks/Whistler HDMI Audio [Radeon HD 6000 Series] /0/100/5 bridge RD890 PCI to PCI bridge (PCI express gpp port E) /0/100/5/0 bus TUSB73x0 SuperSpeed USB 3.0 xHCI Host Controller /0/100/11 storage SB7x0/SB8x0/SB9x0 SATA Controller [RAID5 mode] /0/100/12 bus SB7x0/SB8x0/SB9x0 USB OHCI0 Controller /0/100/12.2 bus SB7x0/SB8x0/SB9x0 USB EHCI Controller /0/100/13 bus SB7x0/SB8x0/SB9x0 USB OHCI0 Controller /0/100/13.2 bus SB7x0/SB8x0/SB9x0 USB EHCI Controller /0/100/14 bus SBx00 SMBus Controller /0/100/14.2 multimedia SBx00 Azalia (Intel HDA) /0/100/14.3 bridge SB7x0/SB8x0/SB9x0 LPC host controller /0/100/14.4 bridge SBx00 PCI to PCI Bridge /0/100/14.5 bus SB7x0/SB8x0/SB9x0 USB OHCI2 Controller /0/100/15 bridge SB700/SB800/SB900 PCI to PCI bridge (PCIE port 0) /0/100/15.1 bridge SB700/SB800/SB900 PCI to PCI bridge (PCIE port 1) /0/100/15.2 bridge SB900 PCI to PCI bridge (PCIE port 2) /0/100/15.2/0 wlan0 network RT3290 Wireless 802.11n 1T/1R PCIe /0/100/15.2/0.1 generic RT3290 Bluetooth /0/100/15.3 bridge SB900 PCI to PCI bridge (PCIE port 3) /0/100/15.3/0 eth0 network RTL8111/8168/8411 PCI Express Gigabit Ethernet Controller /0/100/16 bus SB7x0/SB8x0/SB9x0 USB OHCI0 Controller /0/100/16.2 bus SB7x0/SB8x0/SB9x0 USB EHCI Controller /0/101 bridge Family 15h Processor Function 0 /0/102 bridge Family 15h Processor Function 1 /0/103 bridge Family 15h Processor Function 2 /0/104 bridge Family 15h Processor Function 3 /0/105 bridge Family 15h Processor Function 4 /0/106 bridge Family 15h Processor Function 5 /0/1 scsi0 storage /0/1/0.0.0 /dev/sda disk 1TB WDC WD1002FAEX-0 /0/1/0.0.0/1 volume 189MiB Windows FAT volume /0/1/0.0.0/2 /dev/sda2 volume 244MiB data partition /0/1/0.0.0/3 /dev/sda3 volume 931GiB LVM Physical Volume /0/2 scsi2 storage /0/2/0.0.0 /dev/cdrom disk DVD A DH16ACSHR /0/3 scsi6 storage /0/3/0.0.0 /dev/sdb disk SCSI Disk /0/3/0.0.1 /dev/sdc disk SCSI Disk /0/3/0.0.2 /dev/sdd disk SCSI Disk /0/3/0.0.3 /dev/sde disk MS/MS-Pro /0/3/0.0.3/0 /dev/sde disk /1 power Standard Efficiency I apologize for my newbness. I hope this is enough info for the hardware. Thanks Bruno for pointing out I needed to add more info. If I am lacking anything else please let me know and I'll post it.

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• Restructuring a large Chrome Extension/WebApp

  - by A.M.K

  I have a very complex Chrome Extension that has gotten too large to maintain in its current format. I'd like to restructure it, but I'm 15 and this is the first webapp or extension of it's type I've built so I have no idea how to do it. TL;DR: I have a large/complex webapp I'd like to restructure and I don't know how to do it. Should I follow my current restructure plan (below)? Does that sound like a good starting point, or is there a different approach that I'm missing? Should I not do any of the things I listed? While it isn't relevant to the question, the actual code is on Github and the extension is on the webstore. The basic structure is as follows: index.html <html> <head> <link href="css/style.css" rel="stylesheet" /> <!-- This holds the main app styles --> <link href="css/widgets.css" rel="stylesheet" /> <!-- And this one holds widget styles --> </head> <body class="unloaded"> <!-- Low-level base elements are "hardcoded" here, the unloaded class is used for transitions and is removed on load. i.e: --> <div class="tab-container" tabindex="-1"> <!-- Tab nav --> </div> <!-- Templates for all parts of the application and widgets are stored as elements here. I plan on changing these to <script> elements during the restructure since <template>'s need valid HTML. --> <template id="template.toolbar"> <!-- Template content --> </template> <!-- Templates end --> <!-- Plugins --> <script type="text/javascript" src="js/plugins.js"></script> <!-- This contains the code for all widgets, I plan on moving this online and downloading as necessary soon. --> <script type="text/javascript" src="js/widgets.js"></script> <!-- This contains the main application JS. --> <script type="text/javascript" src="js/script.js"></script> </body> </html> widgets.js (initLog || (window.initLog = [])).push([new Date().getTime(), "A log is kept during page load so performance can be analyzed and errors pinpointed"]); // Widgets are stored in an object and extended (with jQuery, but I'll probably switch to underscore if using Backbone) as necessary var Widgets = { 1: { // Widget ID, this is set here so widgets can be retreived by ID id: 1, // Widget ID again, this is used after the widget object is duplicated and detached size: 3, // Default size, medium in this case order: 1, // Order shown in "store" name: "Weather", // Widget name interval: 300000, // Refresh interval nicename: "weather", // HTML and JS safe widget name sizes: ["tiny", "small", "medium"], // Available widget sizes desc: "Short widget description", settings: [ { // Widget setting specifications stored as an array of objects. These are used to dynamically generate widget setting popups. type: "list", nicename: "location", label: "Location(s)", placeholder: "Enter a location and press Enter" } ], config: { // Widget settings as stored in the tabs object (see script.js for storage information) size: "medium", location: ["San Francisco, CA"] }, data: {}, // Cached widget data stored locally, this lets it work offline customFunc: function(cb) {}, // Widgets can optionally define custom functions in any part of their object refresh: function() {}, // This fetches data from the web and caches it locally in data, then calls render. It gets called after the page is loaded for faster loads render: function() {} // This renders the widget only using information from data, it's called on page load. } }; script.js (initLog || (window.initLog = [])).push([new Date().getTime(), "These are also at the end of every file"]); // Plugins, extends and globals go here. i.e. Number.prototype.pad = .... var iChrome = function(refresh) { // The main iChrome init, called with refresh when refreshing to not re-run libs iChrome.Status.log("Starting page generation"); // From now on iChrome.Status.log is defined, it's used in place of the initLog iChrome.CSS(); // Dynamically generate CSS based on settings iChrome.Tabs(); // This takes the tabs stored in the storage (see fetching below) and renders all columns and widgets as necessary iChrome.Status.log("Tabs rendered"); // These will be omitted further along in this excerpt, but they're used everywhere // Checks for justInstalled => show getting started are run here /* The main init runs the bare minimum required to display the page, this sets all non-visible or instantly need things (such as widget dragging) on a timeout */ iChrome.deferredTimeout = setTimeout(function() { iChrome.deferred(refresh); // Pass refresh along, see above }, 200); }; iChrome.deferred = function(refresh) {}; // This calls modules one after the next in the appropriate order to finish rendering the page iChrome.Search = function() {}; // Modules have a base init function and are camel-cased and capitalized iChrome.Search.submit = function(val) {}; // Methods within modules are camel-cased and not capitalized /* Extension storage is async and fetched at the beginning of plugins.js, it's then stored in a variable that iChrome.Storage processes. The fetcher checks to see if processStorage is defined, if it is it gets called, otherwise settings are left in iChromeConfig */ var processStorage = function() { iChrome.Storage(function() { iChrome.Templates(); // Templates are read from their elements and held in a cache iChrome(); // Init is called }); }; if (typeof iChromeConfig == "object") { processStorage(); } Objectives of the restructure Memory usage: Chrome apparently has a memory leak in extensions, they're trying to fix it but memory still keeps on getting increased every time the page is loaded. The app also uses a lot on its own. Code readability: At this point I can't follow what's being called in the code. While rewriting the code I plan on properly commenting everything. Module interdependence: Right now modules call each other a lot, AFAIK that's not good at all since any change you make to one module could affect countless others. Fault tolerance: There's very little fault tolerance or error handling right now. If a widget is causing the rest of the page to stop rendering the user should at least be able to remove it. Speed is currently not an issue and I'd like to keep it that way. How I think I should do it The restructure should be done using Backbone.js and events that call modules (i.e. on storage.loaded = init). Modules should each go in their own file, I'm thinking there should be a set of core files that all modules can rely on and call directly and everything else should be event based. Widget structure should be kept largely the same, but maybe they should also be split into their own files. AFAIK you can't load all templates in a folder, therefore they need to stay inline. Grunt should be used to merge all modules, plugins and widgets into one file. Templates should also all be precompiled. Question: Should I follow my current restructure plan? Does that sound like a good starting point, or is there a different approach that I'm missing? Should I not do any of the things I listed? Do applications written with Backbone tend to be more intensive (memory and speed) than ones written in Vanilla JS? Also, can I expect to improve this with a proper restructure or is my current code about as good as can be expected?

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• PROUHD: RAID for the end-user

  <b>Linuxconfig:</b> "Therefore, there is currently no storage solution that manages heterogeneous storage devices efficiently. In this article, we propose such a solution and we call it PROUHD (Pool of RAID Over User Heterogeneous Devices)."

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• Practical PowerShell for SQL Server Developers and DBAs – Part 1

  There is a lot of confusion amongst DBAs about using PowerShell due to existence the deprecated SQLPS mini-shell of SSMS and the newer SQLPS module. In a two-part article and wallchart, Michael Sorens explains how to install it, what it is, and some of the excellent things it has to offer. Compress live data by 73% Red Gate's SQL Storage Compress reduces the size of live SQL Server databases, saving you disk space and storage costs. Learn more.

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• The Seven Sins against T-SQL Performance

  There are seven common antipatterns in T-SQL coding that make code perform badly, and three good habits which will generally ensure that your code runs fast. If you learn nothing else from this list of great advice from Grant, just keep in mind that you should 'write for the optimizer'. Compress live data by 73% Red Gate's SQL Storage Compress reduces the size of live SQL Server databases, saving you disk space and storage costs. Learn more.

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• Cleaning Up SQL Server Deployment Scripts

  Although, generally speaking, source control is the truth, a database doesn't quite conform to the ideal because the target schema can, for valid reasons, contain other conflicting truths that can't easily be captured in source control. Dave Ballantyne explains the problems and suggests a solution. Compress live data by 73% Red Gate's SQL Storage Compress reduces the size of live SQL Server databases, saving you disk space and storage costs. Learn more.

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• Announcement: ZFS Backup Appliance

  - by uwes

  Announcing Product Software Changes for Sun ZFS Backup Appliance Effective December 4th, 2012, Replication and Cloning software licenses are no longer mandatory purchases with Sun ZFS Backup Appliance.   Replication and Cloning are still available as optional additions on new Sun ZFS Backup Appliance quotes, or as additions to existing systems. For More Product Information Go To External: ZFS Storage Appliance Oracle.com page External: ZFS Storage Appliance Oracle Technical Network.com page External: Software download support.oracle.com page

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• How To Support Multiple SQL Server Package Configurations in SSIS

  I have several applications that use SSIS packages and I want to be able to store all the configurations together in a single table when I deploy. When a package executes I need a way of specifying the "application" and having SSIS automatically handle the package configuration based on the application. Compress live data by 73% Red Gate's SQL Storage Compress reduces the size of live SQL Server databases, saving you disk space and storage costs. Learn more.

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• Hassle-free Backup with Deja Dup

  <b>Linux Pro Magazine:</b> "The Dé Dup backup utility may not be the most powerful or flexible backup tool out there, but it does have its advantages. Its straightforward interface makes it dead-easy to configure backups, while the support for the Amazon S3 storage back-end is a boon for users looking for unlimited backup storage on the cheap."

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• Handling Constraint Violations and Errors in SQL Server

  The database developer can, of course, throw all errors back to the application developer to deal with, but this is neither kind nor necessary. How errors are dealt with is dependent on the application, but the process itself isn't entirely obvious. Compress live data by 73% Red Gate's SQL Storage Compress reduces the size of live SQL Server databases, saving you disk space and storage costs. Learn more.

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• Windows Azure from a Data Perspective

  Before creating a data application in Windows Azure, it is important to make choices based on the type of data you have, as well as the security and the business requirements. There are a wide range of options, because Windows Azure has intrinsic data storage, completely separate from SQL Azure, that is highly available and replicated. Your data requirements are likely to dictate the type of data storage options you choose.

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• Web Development Resources - A Guide on How to Sell Online

  You need to actualize things. First, you need to look at the budget and choose the right web host for you. This web host is an important resource for you to be able to have a site that will cater your system storage and other things such as uploading and storage keeping. This host site will be very helpful in getting the right steps for the web design you will make as well the complexity of the matter.

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• Windows Azure from a Data Perspective

  Before creating a data application in Windows Azure, it is important to make choices based on the type of data you have, as well as the security and the business requirements. There are a wide range of options, because Windows Azure has intrinsic data storage, completely separate from SQL Azure, that is highly available and replicated. Your data requirements are likely to dictate the type of data storage options you choose.

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