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  • How-To: Run CMSDK against a RAC cluster

    - by frank.closheim
    Using CMSDK in a production environment often requires a robust, reliable and failover enabled repository. When using Oracle Real Application Cluster (RAC) with your CMSDK repository you need to have a specific configuration in place to support such a setup. This post will explain the configuration steps required when running CMSDK 9.0.4.6 with Oracle WebLogic Server (WLS).In the previous CMSDK 9.0.4.2 version a RAC enabled connect string looked like this: (DESCRIPTION = (ADDRESS = (PROTOCOL = TCP)(HOST = rac1)(PORT = 1521))(ADDRESS = (PROTOCOL = TCP)(HOST = rac2)(PORT = 1521))(LOAD_BALANCE = NO)(FAILOVER = ON)(CONNECT_DATA =(SERVICE_NAME = rac)(failover_mode = (type=select)(method=basic)))CMSDK 9.0.4.6 makes use of data sources to connect to the underlying database. These data sources are configured inside your Application Server, such as Oracle WebLogic Server.In Oracle WebLogic Server 10.3.4, a single data source implementation has been introduced to support an RAC cluster. It responds to Fast Application Notification (FAN) events to provide Fast Connection Failover (FCF), Runtime Connection Load-Balancing (RCLB), and RAC instance graceful shutdown. XA affinity is supported at the global transaction Id level. The new feature is called WebLogic Active GridLink for RAC; which is implemented as the GridLink data source within WebLogic Server.This GridLink data source also works with Oracle Single Client Access Name (SCAN). SCAN is a feature used in RAC environments that provides a single name for clients to access any Oracle Database running in a cluster. You can think of SCAN as a cluster alias for databases in the cluster. The benefit is that the client’s connect information does not need to change if you add or remove nodes or databases in the cluster.The CMSDK 9.0.4.6 documentation describes how to create a regular JDBC data source named jdbc/OracleDS. Please refer to the following document which describes in detail how to create a GridLink data source in WLS.

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  • How to delete/edit files from readonly filesystem

    - by Santosh Linkha
    I am having problem with my memory device (actually a memory card that act external memory device like pendrive). experimentx@workmateX:/var/www/zendtest$ sudo rm /media/A88F-8788/python-2.7.1-docs-html.zip rm: cannot remove `/media/A88F-8788/python-2.7.1-docs-html.zip': Read-only file system I tried to change the file permission of the system but that doesn't work experimentx@workmateX:/var/www/zendtest$ sudo chmod 0777 /media/A88F-8788/python-2.7.1-docs-html.zip chmod: changing permissions of `/media/A88F-8788/python-2.7.1-docs-html.zip': Read-only file system But it perfectly works on windows. UPDATE On opening the drive and running command sudo mount -o remount,rw /media/A88F-8788 /var/log/syslog: Mar 23 15:29:48 workmateX kernel: [18042.257407] fat_get_cluster: 11 callbacks suppressed Mar 23 15:29:48 workmateX kernel: [18042.257414] FAT: Filesystem error (dev sdb1) Mar 23 15:29:48 workmateX kernel: [18042.257418] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:29:48 workmateX kernel: [18042.257425] FAT: Filesystem has been set read-only Mar 23 15:29:48 workmateX kernel: [18042.258187] FAT: Filesystem error (dev sdb1) Mar 23 15:29:48 workmateX kernel: [18042.258194] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:35 workmateX kernel: [18149.333787] FAT: Filesystem error (dev sdb1) Mar 23 15:31:35 workmateX kernel: [18149.333795] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:35 workmateX kernel: [18149.335949] FAT: Filesystem error (dev sdb1) Mar 23 15:31:35 workmateX kernel: [18149.335957] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:35 workmateX kernel: [18149.354903] FAT: Filesystem error (dev sdb1) Mar 23 15:31:35 workmateX kernel: [18149.354911] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:35 workmateX kernel: [18149.357213] FAT: Filesystem error (dev sdb1) Mar 23 15:31:35 workmateX kernel: [18149.357221] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:35 workmateX kernel: [18149.359547] FAT: Filesystem error (dev sdb1) Mar 23 15:31:35 workmateX kernel: [18149.359555] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:35 workmateX kernel: [18149.361929] FAT: Filesystem error (dev sdb1) Mar 23 15:31:35 workmateX kernel: [18149.361936] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:35 workmateX kernel: [18149.377416] FAT: Filesystem error (dev sdb1) Mar 23 15:31:35 workmateX kernel: [18149.377424] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:35 workmateX kernel: [18149.379384] FAT: Filesystem error (dev sdb1) Mar 23 15:31:35 workmateX kernel: [18149.379392] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:35 workmateX kernel: [18149.381898] FAT: Filesystem error (dev sdb1) Mar 23 15:31:35 workmateX kernel: [18149.381906] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:35 workmateX kernel: [18149.383764] FAT: Filesystem error (dev sdb1) Mar 23 15:31:35 workmateX kernel: [18149.383772] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:40 workmateX kernel: [18154.569747] fat_get_cluster: 11 callbacks suppressed Mar 23 15:31:40 workmateX kernel: [18154.569754] FAT: Filesystem error (dev sdb1) Mar 23 15:31:40 workmateX kernel: [18154.569758] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:40 workmateX kernel: [18154.569765] FAT: Filesystem has been set read-only Mar 23 15:31:40 workmateX kernel: [18154.572022] FAT: Filesystem error (dev sdb1) Mar 23 15:31:40 workmateX kernel: [18154.572029] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:40 workmateX kernel: [18154.582933] FAT: Filesystem error (dev sdb1) Mar 23 15:31:40 workmateX kernel: [18154.582941] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:40 workmateX kernel: [18154.585921] FAT: Filesystem error (dev sdb1) Mar 23 15:31:40 workmateX kernel: [18154.585929] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:40 workmateX kernel: [18154.587819] FAT: Filesystem error (dev sdb1) Mar 23 15:31:40 workmateX kernel: [18154.587827] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:40 workmateX kernel: [18154.597547] FAT: Filesystem error (dev sdb1) Mar 23 15:31:40 workmateX kernel: [18154.597555] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:40 workmateX kernel: [18154.599503] FAT: Filesystem error (dev sdb1) Mar 23 15:31:40 workmateX kernel: [18154.599511] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:40 workmateX kernel: [18154.602896] FAT: Filesystem error (dev sdb1) Mar 23 15:31:40 workmateX kernel: [18154.602905] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:40 workmateX kernel: [18154.615338] FAT: Filesystem error (dev sdb1) Mar 23 15:31:40 workmateX kernel: [18154.615346] fat_get_cluster: invalid cluster chain (i_pos 0) Mar 23 15:31:40 workmateX kernel: [18154.618574] FAT: Filesystem error (dev sdb1) Mar 23 15:31:40 workmateX kernel: [18154.618581] fat_get_cluster: invalid cluster chain (i_pos 0) var/log/message: Mar 23 15:29:48 workmateX kernel: [18042.257407] fat_get_cluster: 11 callbacks suppressed Mar 23 15:31:40 workmateX kernel: [18154.569747] fat_get_cluster: 11 callbacks suppressed

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  • How to Profile R Code that Includes SNOW Cluster

    - by James
    Hi, I have a nested loop that I'm using foreach, DoSNOW, and a SNOW socket cluster to solve for. How should I go about profiling the code to make sure I'm not doing something grossly inefficient. Also is there anyway to measure the data flows going between the master and nodes in a Snow cluster? Thanks, James

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  • SQL Server cluster install issue

    - by George2
    Hello everyone, I am going to install SQL Server 2008 Enterprise cluster on Windows Server 2008. I am wondering whether I have to setup a Windows domain (or active directory) in order to install SQL Server cluster? thanks in advance, George

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  • Redhat cluster Vs Pacemaker Vs Gluster Vs Sheepdog

    - by chandank
    Changing the entire question as earlier one was very confusing. I have been exploring different clustering system to run Virtual machines on two different machines on LAN with high availability. Currently I am already using DRBD resource on two different machines on Primary/Secondary mode. In case the primary fails I manually promote the secondary to Primary and start the VM. I also explored Gluster and looks good, however, I would rather prefer clustering over Gluster (user space FS). So if anyone has idea which one would be better from ease of use prospective please I would be interested in. Moreover, sheepdog project appears good, however, could not find much documentations/Howtos. I am using Centos 6.

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  • cluster live postgres 8.3 server

    - by bobinabottle
    Our web application is getting more and more traffic, which is making our poor pg8.3 database server have a little trouble keeping up. I've had a look into using pgpool II for clustering the db to relieve a little strain, and I was wondering how this should be done to minimise downtime considering I would be clustering a live database. Has anyone had experience with this or know of any guides to follow? Cheers :)

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  • Shared storage for web cluster

    - by user52475
    Hi all! Have a big question about shared/clustered/distributed file system for storage. It will shared storage for shared web hosting (web files + maildir) and OpenVZ containers storage . Have any one working example of such system? The options are: Lustre GFS1/GFS2 - GFS2 - as I understand is EXPERIMENTAL... NFS This 3 systems which I consider for shared storage. Now I have storage with HW RAID 10 - 1TB. NFS - As I know there will be problem with locking? GFS/Lustre - problems when there will be a lot of small files , what is typical for hosting environment and problems with maildir.

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  • SQL Monitor’s data repository: Alerts

    - by Chris Lambrou
    In my previous post, I introduced the SQL Monitor data repository, and described how the monitored objects are stored in a hierarchy in the data schema, in a series of tables with a _Keys suffix. In this post I had planned to describe how the actual data for the monitored objects is stored in corresponding tables with _StableSamples and _UnstableSamples suffixes. However, I’m going to postpone that until my next post, as I’ve had a request from a SQL Monitor user to explain how alerts are stored. In the SQL Monitor data repository, alerts are stored in tables belonging to the alert schema, which contains the following five tables: alert.Alert alert.Alert_Cleared alert.Alert_Comment alert.Alert_Severity alert.Alert_Type In this post, I’m only going to cover the alert.Alert and alert.Alert_Type tables. I may cover the other three tables in a later post. The most important table in this schema is alert.Alert, as each row in this table corresponds to a single alert. So let’s have a look at it. SELECT TOP 100 AlertId, AlertType, TargetObject, [Read], SubType FROM alert.Alert ORDER BY AlertId DESC;  AlertIdAlertTypeTargetObjectReadSubType 165550397:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,9:SqlServer,1,4:Name,s0:,10 265549387:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,7:Machine,1,4:Name,s0:,10 365548187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 465547157:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 565546147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 665545187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 765544157:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 865543147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 965542187:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 1065541147:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 11…     So what are we seeing here, then? Well, AlertId is an auto-incrementing identity column, so ORDER BY AlertId DESC ensures that we see the most recent alerts first. AlertType indicates the type of each alert, such as Job failed (6), Backup overdue (14) or Long-running query (12). The TargetObject column indicates which monitored object the alert is associated with. The Read column acts as a flag to indicate whether or not the alert has been read. And finally the SubType column is used in the case of a Custom metric (40) alert, to indicate which custom metric the alert pertains to. Okay, now lets look at some of those columns in more detail. The AlertType column is an easy one to start with, and it brings use nicely to the next table, data.Alert_Type. Let’s have a look at what’s in this table: SELECT AlertType, Event, Monitoring, Name, Description FROM alert.Alert_Type ORDER BY AlertType;  AlertTypeEventMonitoringNameDescription 1100Processor utilizationProcessor utilization (CPU) on a host machine stays above a threshold percentage for longer than a specified duration 2210SQL Server error log entryAn error is written to the SQL Server error log with a severity level above a specified value. 3310Cluster failoverThe active cluster node fails, causing the SQL Server instance to switch nodes. 4410DeadlockSQL deadlock occurs. 5500Processor under-utilizationProcessor utilization (CPU) on a host machine remains below a threshold percentage for longer than a specified duration 6610Job failedA job does not complete successfully (the job returns an error code). 7700Machine unreachableHost machine (Windows server) cannot be contacted on the network. 8800SQL Server instance unreachableThe SQL Server instance is not running or cannot be contacted on the network. 9900Disk spaceDisk space used on a logical disk drive is above a defined threshold for longer than a specified duration. 101000Physical memoryPhysical memory (RAM) used on the host machine stays above a threshold percentage for longer than a specified duration. 111100Blocked processSQL process is blocked for longer than a specified duration. 121200Long-running queryA SQL query runs for longer than a specified duration. 131400Backup overdueNo full backup exists, or the last full backup is older than a specified time. 141500Log backup overdueNo log backup exists, or the last log backup is older than a specified time. 151600Database unavailableDatabase changes from Online to any other state. 161700Page verificationTorn Page Detection or Page Checksum is not enabled for a database. 171800Integrity check overdueNo entry for an integrity check (DBCC DBINFO returns no date for dbi_dbccLastKnownGood field), or the last check is older than a specified time. 181900Fragmented indexesFragmentation level of one or more indexes is above a threshold percentage. 192400Job duration unusualThe duration of a SQL job duration deviates from its baseline duration by more than a threshold percentage. 202501Clock skewSystem clock time on the Base Monitor computer differs from the system clock time on a monitored SQL Server host machine by a specified number of seconds. 212700SQL Server Agent Service statusThe SQL Server Agent Service status matches the status specified. 222800SQL Server Reporting Service statusThe SQL Server Reporting Service status matches the status specified. 232900SQL Server Full Text Search Service statusThe SQL Server Full Text Search Service status matches the status specified. 243000SQL Server Analysis Service statusThe SQL Server Analysis Service status matches the status specified. 253100SQL Server Integration Service statusThe SQL Server Integration Service status matches the status specified. 263300SQL Server Browser Service statusThe SQL Server Browser Service status matches the status specified. 273400SQL Server VSS Writer Service statusThe SQL Server VSS Writer status matches the status specified. 283501Deadlock trace flag disabledThe monitored SQL Server’s trace flag cannot be enabled. 293600Monitoring stopped (host machine credentials)SQL Monitor cannot contact the host machine because authentication failed. 303700Monitoring stopped (SQL Server credentials)SQL Monitor cannot contact the SQL Server instance because authentication failed. 313800Monitoring error (host machine data collection)SQL Monitor cannot collect data from the host machine. 323900Monitoring error (SQL Server data collection)SQL Monitor cannot collect data from the SQL Server instance. 334000Custom metricThe custom metric value has passed an alert threshold. 344100Custom metric collection errorSQL Monitor cannot collect custom metric data from the target object. Basically, alert.Alert_Type is just a big reference table containing information about the 34 different alert types supported by SQL Monitor (note that the largest id is 41, not 34 – some alert types have been retired since SQL Monitor was first developed). The Name and Description columns are self evident, and I’m going to skip over the Event and Monitoring columns as they’re not very interesting. The AlertId column is the primary key, and is referenced by AlertId in the alert.Alert table. As such, we can rewrite our earlier query to join these two tables, in order to provide a more readable view of the alerts: SELECT TOP 100 AlertId, Name, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType ORDER BY AlertId DESC;  AlertIdNameTargetObjectReadSubType 165550Monitoring error (SQL Server data collection)7:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,9:SqlServer,1,4:Name,s0:,00 265549Monitoring error (host machine data collection)7:Cluster,1,4:Name,s29:srp-mr03.testnet.red-gate.com,7:Machine,1,4:Name,s0:,00 365548Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 465547Log backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 565546Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s15:FavouriteThings,00 665545Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 765544Log backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 865543Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,00 965542Integrity check overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 1065541Backup overdue7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s4:msdb,00 Okay, the next column to discuss in the alert.Alert table is TargetObject. Oh boy, this one’s a bit tricky! The TargetObject of an alert is a serialized string representation of the position in the monitored object hierarchy of the object to which the alert pertains. The serialization format is somewhat convenient for parsing in the C# source code of SQL Monitor, and has some helpful characteristics, but it’s probably very awkward to manipulate in T-SQL. I could document the serialization format here, but it would be very dry reading, so perhaps it’s best to consider an example from the table above. Have a look at the alert with an AlertID of 65543. It’s a Backup overdue alert for the SqlMonitorData database running on the default instance of granger, my laptop. Each different alert type is associated with a specific type of monitored object in the object hierarchy (I described the hierarchy in my previous post). The Backup overdue alert is associated with databases, whose position in the object hierarchy is root → Cluster → SqlServer → Database. The TargetObject value identifies the target object by specifying the key properties at each level in the hierarchy, thus: Cluster: Name = "granger" SqlServer: Name = "" (an empty string, denoting the default instance) Database: Name = "SqlMonitorData" Well, look at the actual TargetObject value for this alert: "7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s14:SqlMonitorData,". It is indeed composed of three parts, one for each level in the hierarchy: Cluster: "7:Cluster,1,4:Name,s7:granger," SqlServer: "9:SqlServer,1,4:Name,s0:," Database: "8:Database,1,4:Name,s14:SqlMonitorData," Each part is handled in exactly the same way, so let’s concentrate on the first part, "7:Cluster,1,4:Name,s7:granger,". It comprises the following: "7:Cluster," – This identifies the level in the hierarchy. "1," – This indicates how many different key properties there are to uniquely identify a cluster (we saw in my last post that each cluster is identified by a single property, its Name). "4:Name,s14:SqlMonitorData," – This represents the Name property, and its corresponding value, SqlMonitorData. It’s split up like this: "4:Name," – Indicates the name of the key property. "s" – Indicates the type of the key property, in this case, it’s a string. "14:SqlMonitorData," – Indicates the value of the property. At this point, you might be wondering about the format of some of these strings. Why is the string "Cluster" stored as "7:Cluster,"? Well an encoding scheme is used, which consists of the following: "7" – This is the length of the string "Cluster" ":" – This is a delimiter between the length of the string and the actual string’s contents. "Cluster" – This is the string itself. 7 characters. "," – This is a final terminating character that indicates the end of the encoded string. You can see that "4:Name,", "8:Database," and "14:SqlMonitorData," also conform to the same encoding scheme. In the example above, the "s" character is used to indicate that the value of the Name property is a string. If you explore the TargetObject property of alerts in your own SQL Monitor data repository, you might find other characters used for other non-string key property values. The different value types you might possibly encounter are as follows: "I" – Denotes a bigint value. For example, "I65432,". "g" – Denotes a GUID value. For example, "g32116732-63ae-4ab5-bd34-7dfdfb084c18,". "d" – Denotes a datetime value. For example, "d634815384796832438,". The value is stored as a bigint, rather than a native SQL datetime value. I’ll describe how datetime values are handled in the SQL Monitor data repostory in a future post. I suggest you have a look at the alerts in your own SQL Monitor data repository for further examples, so you can see how the TargetObject values are composed for each of the different types of alert. Let me give one further example, though, that represents a Custom metric alert, as this will help in describing the final column of interest in the alert.Alert table, SubType. Let me show you the alert I’m interested in: SELECT AlertId, a.AlertType, Name, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType WHERE AlertId = 65769;  AlertIdAlertTypeNameTargetObjectReadSubType 16576940Custom metric7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s6:master,12:CustomMetric,1,8:MetricId,I2,02 An AlertType value of 40 corresponds to the Custom metric alert type. The Name taken from the alert.Alert_Type table is simply Custom metric, but this doesn’t tell us anything about the specific custom metric that this alert pertains to. That’s where the SubType value comes in. For custom metric alerts, this provides us with the Id of the specific custom alert definition that can be found in the settings.CustomAlertDefinitions table. I don’t really want to delve into custom alert definitions yet (maybe in a later post), but an extra join in the previous query shows us that this alert pertains to the CPU pressure (avg runnable task count) custom metric alert. SELECT AlertId, a.AlertType, at.Name, cad.Name AS CustomAlertName, TargetObject, [Read], SubType FROM alert.Alert a JOIN alert.Alert_Type at ON a.AlertType = at.AlertType JOIN settings.CustomAlertDefinitions cad ON a.SubType = cad.Id WHERE AlertId = 65769;  AlertIdAlertTypeNameCustomAlertNameTargetObjectReadSubType 16576940Custom metricCPU pressure (avg runnable task count)7:Cluster,1,4:Name,s7:granger,9:SqlServer,1,4:Name,s0:,8:Database,1,4:Name,s6:master,12:CustomMetric,1,8:MetricId,I2,02 The TargetObject value in this case breaks down like this: "7:Cluster,1,4:Name,s7:granger," – Cluster named "granger". "9:SqlServer,1,4:Name,s0:," – SqlServer named "" (the default instance). "8:Database,1,4:Name,s6:master," – Database named "master". "12:CustomMetric,1,8:MetricId,I2," – Custom metric with an Id of 2. Note that the hierarchy for a custom metric is slightly different compared to the earlier Backup overdue alert. It’s root → Cluster → SqlServer → Database → CustomMetric. Also notice that, unlike Cluster, SqlServer and Database, the key property for CustomMetric is called MetricId (not Name), and the value is a bigint (not a string). Finally, delving into the custom metric tables is beyond the scope of this post, but for the sake of avoiding any future confusion, I’d like to point out that whilst the SubType references a custom alert definition, the MetricID value embedded in the TargetObject value references a custom metric definition. Although in this case both the custom metric definition and custom alert definition share the same Id value of 2, this is not generally the case. Okay, that’s enough for now, not least because as I’m typing this, it’s almost 2am, I have to go to work tomorrow, and my alarm is set for 6am – eek! In my next post, I’ll either cover the remaining three tables in the alert schema, or I’ll delve into the way SQL Monitor stores its monitoring data, as I’d originally planned to cover in this post.

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  • Guide to MySQL & NoSQL, Webinar Q&A

    - by Mat Keep
    0 0 1 959 5469 Homework 45 12 6416 14.0 Normal 0 false false false EN-US JA X-NONE /* 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-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} Yesterday we ran a webinar discussing the demands of next generation web services and how blending the best of relational and NoSQL technologies enables developers and architects to deliver the agility, performance and availability needed to be successful. Attendees posted a number of great questions to the MySQL developers, serving to provide additional insights into areas like auto-sharding and cross-shard JOINs, replication, performance, client libraries, etc. So I thought it would be useful to post those below, for the benefit of those unable to attend the webinar. Before getting to the Q&A, there are a couple of other resources that maybe useful to those looking at NoSQL capabilities within MySQL: - On-Demand webinar (coming soon!) - Slides used during the webinar - Guide to MySQL and NoSQL whitepaper  - MySQL Cluster demo, including NoSQL interfaces, auto-sharing, high availability, etc.  So here is the Q&A from the event  Q. Where does MySQL Cluster fit in to the CAP theorem? A. MySQL Cluster is flexible. A single Cluster will prefer consistency over availability in the presence of network partitions. A pair of Clusters can be configured to prefer availability over consistency. A full explanation can be found on the MySQL Cluster & CAP Theorem blog post.  Q. Can you configure the number of replicas? (the slide used a replication factor of 1) Yes. A cluster is configured by an .ini file. The option NoOfReplicas sets the number of originals and replicas: 1 = no data redundancy, 2 = one copy etc. Usually there's no benefit in setting it >2. Q. Interestingly most (if not all) of the NoSQL databases recommend having 3 copies of data (the replication factor).    Yes, with configurable quorum based Reads and writes. MySQL Cluster does not need a quorum of replicas online to provide service. Systems that require a quorum need > 2 replicas to be able to tolerate a single failure. Additionally, many NoSQL systems take liberal inspiration from the original GFS paper which described a 3 replica configuration. MySQL Cluster avoids the need for a quorum by using a lightweight arbitrator. You can configure more than 2 replicas, but this is a tradeoff between incrementally improved availability, and linearly increased cost. Q. Can you have cross node group JOINS? Wouldn't that run into the risk of flooding the network? MySQL Cluster 7.2 supports cross nodegroup joins. A full cross-join can require a large amount of data transfer, which may bottleneck on network bandwidth. However, for more selective joins, typically seen with OLTP and light analytic applications, cross node-group joins give a great performance boost and network bandwidth saving over having the MySQL Server perform the join. Q. Are the details of the benchmark available anywhere? According to my calculations it results in approx. 350k ops/sec per processor which is the largest number I've seen lately The details are linked from Mikael Ronstrom's blog The benchmark uses a benchmarking tool we call flexAsynch which runs parallel asynchronous transactions. It involved 100 byte reads, of 25 columns each. Regarding the per-processor ops/s, MySQL Cluster is particularly efficient in terms of throughput/node. It uses lock-free minimal copy message passing internally, and maximizes ID cache reuse. Note also that these are in-memory tables, there is no need to read anything from disk. Q. Is access control (like table) planned to be supported for NoSQL access mode? Currently we have not seen much need for full SQL-like access control (which has always been overkill for web apps and telco apps). So we have no plans, though especially with memcached it is certainly possible to turn-on connection-level access control. But specifically table level controls are not planned. Q. How is the performance of memcached APi with MySQL against memcached+MySQL or any other Object Cache like Ecache with MySQL DB? With the memcache API we generally see a memcached response in less than 1 ms. and a small cluster with one memcached server can handle tens of thousands of operations per second. Q. Can .NET can access MemcachedAPI? Yes, just use a .Net memcache client such as the enyim or BeIT memcache libraries. Q. Is the row level locking applicable when you update a column through memcached API? An update that comes through memcached uses a row lock and then releases it immediately. Memcached operations like "INCREMENT" are actually pushed down to the data nodes. In most cases the locks are not even held long enough for a network round trip. Q. Has anyone published an example using something like PHP? I am assuming that you just use the PHP memcached extension to hook into the memcached API. Is that correct? Not that I'm aware of but absolutely you can use it with php or any of the other drivers Q. For beginner we need more examples. Take a look here for a fully worked example Q. Can I access MySQL using Cobol (Open Cobol) or C and if so where can I find the coding libraries etc? A. There is a cobol implementation that works well with MySQL, but I do not think it is Open Cobol. Also there is a MySQL C client library that is a standard part of every mysql distribution Q. Is there a place to go to find help when testing and/implementing the NoSQL access? If using Cluster then you can use the [email protected] alias or post on the MySQL Cluster forum Q. Are there any white papers on this?  Yes - there is more detail in the MySQL Guide to NoSQL whitepaper If you have further questions, please don’t hesitate to use the comments below!

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  • Why Ultra-Low Power Computing Will Change Everything

    - by Tori Wieldt
    The ARM TechCon keynote "Why Ultra-Low Power Computing Will Change Everything" was anything but low-powered. The speaker, Dr. Johnathan Koomey, knows his subject: he is a Consulting Professor at Stanford University, worked for more than two decades at Lawrence Berkeley National Laboratory, and has been a visiting professor at Stanford University, Yale University, and UC Berkeley's Energy and Resources Group. His current focus is creating a standard (computations per kilowatt hour) and measuring computer energy consumption over time. The trends are impressive: energy consumption has halved every 1.5 years for the last 60 years. Battery life has made roughly a 10x improvement each decade since 1960. It's these improvements that have made laptops and cell phones possible. What does the future hold? Dr. Koomey said that in the past, the race by chip manufacturers was to create the fastest computer, but the priorities have now changed. New computers are tiny, smart, connected and cheap. "You can't underestimate the importance of a shift in industry focus from raw performance to power efficiency for mobile devices," he said. There is also a confluence of trends in computing, communications, sensors, and controls. The challenge is how to reduce the power requirements for these tiny devices. Alternate sources of power that are being explored are light, heat, motion, and even blood sugar. The University of Michigan has produced a miniature sensor that harnesses solar energy and could last for years without needing to be replaced. Also, the University of Washington has created a sensor that scavenges power from existing radio and TV signals.Specific devices designed for a purpose are much more efficient than general purpose computers. With all these sensors, instead of big data, developers should focus on nano-data, personalized information that will adjust the lights in a room, a machine, a variable sign, etc.Dr. Koomey showed some examples:The Proteus Digital Health Feedback System, an ingestible sensor that transmits when a patient has taken their medicine and is powered by their stomach juices. (Gives "powered by you" a whole new meaning!) Streetline Parking Systems, that provide real-time data about available parking spaces. The information can be sent to your phone or update parking signs around the city to point to areas with available spaces. Less driving around looking for parking spaces!The BigBelly trash system that uses solar power, compacts trash, and sends a text message when it is full. This dramatically reduces the number of times a truck has to come to pick up trash, freeing up resources and slashing fuel costs. This is a classic example of the efficiency of moving "bits not atoms." But researchers are approaching the physical limits of sensors, Dr. Kommey explained. With the current rate of technology improvement, they'll reach the three-atom transistor by 2041. Once they hit that wall, it will force a revolution they way we do computing. But wait, researchers at Purdue University and the University of New South Wales are both working on a reliable one-atom transistors! Other researchers are working on "approximate computing" that will reduce computing requirements drastically. So it's unclear where the wall actually is. In the meantime, as Dr. Koomey promised, ultra-low power computing will change everything.

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  • SQL Cluster install on Hyper V options

    - by Chris W
    I've been reading up on running a SQL Cluster in a Hyper V environment and there seems to be a couple of options: Install guest cluster on 2 VMs that are themselves part of a fail over cluster. Install SQL cluster on 2 VMs but the VMs themselves are not part of an underlying cluster. With option 1, it's little more complex as there's effectively two clusters in play but this adds some flexibility in the sense that I'm free to migrate the VMs between and physical blades in their cluster for physical maintenance without affecting the status of the SQL guest cluster that's running within them. With option 2, the set-up is a bit simpler as there's only 1 cluster in the mix but my VMs are anchored to the physical blades that they're set-up on (I'll ignore the fact I could manually move the VHDs for the purposes of this question). Are there any other factors that I should consider here when deciding which option to go for? I'm free to test out both options and probably will do but if any one has working experience of these set-ups and can offer some input that would be great.

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  • Commercial uses for grid computing?

    - by paxdiablo
    I keep hearing from associates about grid computing which, from what I can gather, is highly distributed stuff along the lines of SETI@Home. Is anyone working on these sort of systems for business use? My interest is in figuring out if there's a commercial reason for starting software development in this field.

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  • Suggestions for open source testing tool for cloud computing

    - by vikraman
    Hi, I want to know if there is any open source testing tool for cloud computing. We have built a cloud framework with Xen, Eucalyptus, Hadoop, HBase as different layers. I am not looking at testing each of these tools separately, but i want to test them from the perspective of fitting into a cloud environment (for example scalability of xen hypervisor to handle multiple VMs). Would be great if you can suggest me some tool (open source) for the above.

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  • How do I debug a cluster running Microsoft server 2003?

    - by alcor
    I'm sole developer of a complex critical software system, written in Visual C++ 2005. It's deployed on a classical Microsoft cluster scenario (active/passive), that has Windows Server 2003 R2. If a server A goes down, the other one (B) starts and take the ownership of its duties. You have to know that: Both servers have the same Microsoft patches/fixes, same hardware, same everything. Both servers use the same memory storage (a RAID-6 through fiber channel). This software has a main module that launches the peripheral modules. if a peripheral module crashes, the main module restarts it. When I switch the application in one of the two servers (let's say the B server) two of the peripheral modules of the main applications just started to crash apparently without reason about 2 seconds after the start of the peripheral module. What could I do to analyze/inspect/resolve this weird situation?

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  • Opportunities in Cloud Computing

    - by Paul Sorensen
    A recent article from CIO Journal indicates that there is an extreme labor shortage (in certain technology areas) that is is leading to upward pressure on wages for IT Workers. This represents a great opportunity for those with certain skill-sets, among which include Java (Oracle certification is mentioned specifically). The article points out that a key driver of the labor shortage is the expansion of cloud computing. Cloud computing is set up to make life extremely simple for end-users, but the model pushes the complexity to back-end systems which are sophisticated, enterprise-level computing stacks (Oracle has an extensive set of cloud computing solutions). These complex systems require very highly-skilled IT professionals (the best-of-the-best) to successfully develop, implement, administer and maintain them. What this mean for you is that there is opportunity for those who have the appropriate skills at the appropriate levels. If you want to be a part of this opportunity you should do a self-assessment of your own skill-sets and experience. Based upon your results you can decide where it would be most appropriate to spend your time and resources for the highest return on your investment. By expanding and sharpening your skills and by gaining greater experience you will be better prepared to take advantage of career opportunities (like this) that come along periodically. As you evaluate your needs remember that Oracle University has a tremendous selection of high-quality eduction offerings (including training and certification) that can you help move your career forward. Thanks and best of luck!

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  • F# performance in scientific computing

    - by aaa
    hello. I am curious as to how F# performance compares to C++ performance? I asked a similar question with regards to Java, and the impression I got was that Java is not suitable for heavy numbercrunching. I have read that F# is supposed to be more scalable and more performant, but how is this real-world performance compares to C++? specific questions about current implementation are: How well does it do floating-point? Does it allow vector instructions how friendly is it towards optimizing compilers? How big a memory foot print does it have? Does it allow fine-grained control over memory locality? does it have capacity for distributed memory processors, for example Cray? what features does it have that may be of interest to computational science where heavy number processing is involved? Are there actual scientific computing implementations that use it? Thanks

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  • Managed language for scientific computing software

    - by heisen
    Scientific computing is algorithm intensive and can also be data intensive. It often needs to use a lot of memory to run analysis and release it before continuing with the next. Sometime it also uses memory pool to recycle memory for each analysis. Managed language is interesting here because it can allow the developer to concentrate on the application logic. Since it might need to deal with huge dataset, performance is important too. But how can we control memory and performance with managed language?

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  • Using the C Cluster library in Visual C++.

    - by Stefan K.
    Right so i'm trying to use a C library in C++, never actually done this before i thought it would be a case of declaring the header includes under a extern "C" and setting the compile as flag to "default" but i'm still getting linker errors and think that the header file might have to be complied as a DLL. I have no idea really. Is it the library that's the problem or is it me? There are some make files in the cluster-1.47\src, but i don't know how or if they relate to "cluster.h". I've uploaded a visual studio 2008 project for anyone to take a gander, any help would be appreciated as i've been hitting my head against the wall for time now. thanks Stefan Link to Visual Studio 2008 Project

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  • Solution 6 : Kill a Non-Clustered Process during Two-Node Cluster Failover

    - by StanleyGu
    Using Visual Studio 2008 and C#, I developed a windows service A and deployed it to two nodes of a windows server 2008 failover cluster. The service A is part of the failover cluster service, which means, when failover occurs at node1, the cluster service will failover the windows service A from node 1 to node 2. One of the tasks implemented by the windows service A is to start, monitor or kill a process B. The process B is installed to the two nodes but is not part of the failover cluster service. When a failover occurs at node1, the cluster service does not failover the process B from node 1 to node 2, and the process B continues running at node1. The requirement is: When failover occurs at node1, we want the process B running at node1 gets killed, but we do not want the process B be part of the failover cluster service. The first idea that pops up immediately is to put some code in an event handler triggered by the failover in the windows service A. The failover effect to the windows service A is similar to using the task manager to kill the process of the windows service A, but there is no event in windows service that can be triggered by killing the process of the window service. The events related to terminating a windows service are OnStop and OnShutDown, but killing the process of windows service A triggers neither of them. The OnStop event can only be triggered by stopping the windows service using Services Control Manager or Services Management Console. Apparently, the first idea is not feasible. The second idea that emerges is to put code into the OnStart event handler of the windows service A. When failover occurs at node 1, the windows service A is killed at node 1 and started at node 2. During the starting, the windows service A at node 2 kills the process B that is running at node 1. It is a workaround and works very well. The C# code implementation within the OnStart event handler is as following: 1.       Capture server names of the two nodes from App.config 2.       Determine server name of the remote node. 3.       Kill the process B running on the remote node. Check here for sample code.  

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  • Parallel Computing in .Net 4.0

    - by kaleidoscope
    Technorati Tags: Ram,Parallel Computing in .Net 4.0 Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem: To be run using multiple CPUs A problem is broken into discrete parts that can be solved concurrently Each part is further broken down to a series of instructions Instructions from each part execute simultaneously on different CPUs Parallel Extensions in .NET 4.0 provides a set of libraries and tools to achieve the above mentioned objectives. This supports two paradigms of parallel computing Data Parallelism – This refers to dividing the data across multiple processors for parallel execution.e.g we are processing an array of 1000 elements we can distribute the data between two processors say 500 each. This is supported by the Parallel LINQ (PLINQ) in .NET 4.0 Task Parallelism – This breaks down the program into multiple tasks which can be parallelized and are executed on different processors. This is supported by Task Parallel Library (TPL) in .NET 4.0 A high level view is shown below:

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