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  • Python: Improving long cumulative sum

    - by Bo102010
    I have a program that operates on a large set of experimental data. The data is stored as a list of objects that are instances of a class with the following attributes: time_point - the time of the sample cluster - the name of the cluster of nodes from which the sample was taken code - the name of the node from which the sample was taken qty1 = the value of the sample for the first quantity qty2 = the value of the sample for the second quantity I need to derive some values from the data set, grouped in three ways - once for the sample as a whole, once for each cluster of nodes, and once for each node. The values I need to derive depend on the (time sorted) cumulative sums of qty1 and qty2: the maximum value of the element-wise sum of the cumulative sums of qty1 and qty2, the time point at which that maximum value occurred, and the values of qty1 and qty2 at that time point. I came up with the following solution: dataset.sort(key=operator.attrgetter('time_point')) # For the whole set sys_qty1 = 0 sys_qty2 = 0 sys_combo = 0 sys_max = 0 # For the cluster grouping cluster_qty1 = defaultdict(int) cluster_qty2 = defaultdict(int) cluster_combo = defaultdict(int) cluster_max = defaultdict(int) cluster_peak = defaultdict(int) # For the node grouping node_qty1 = defaultdict(int) node_qty2 = defaultdict(int) node_combo = defaultdict(int) node_max = defaultdict(int) node_peak = defaultdict(int) for t in dataset: # For the whole system ###################################################### sys_qty1 += t.qty1 sys_qty2 += t.qty2 sys_combo = sys_qty1 + sys_qty2 if sys_combo > sys_max: sys_max = sys_combo # The Peak class is to record the time point and the cumulative quantities system_peak = Peak(time_point=t.time_point, qty1=sys_qty1, qty2=sys_qty2) # For the cluster grouping ################################################## cluster_qty1[t.cluster] += t.qty1 cluster_qty2[t.cluster] += t.qty2 cluster_combo[t.cluster] = cluster_qty1[t.cluster] + cluster_qty2[t.cluster] if cluster_combo[t.cluster] > cluster_max[t.cluster]: cluster_max[t.cluster] = cluster_combo[t.cluster] cluster_peak[t.cluster] = Peak(time_point=t.time_point, qty1=cluster_qty1[t.cluster], qty2=cluster_qty2[t.cluster]) # For the node grouping ##################################################### node_qty1[t.node] += t.qty1 node_qty2[t.node] += t.qty2 node_combo[t.node] = node_qty1[t.node] + node_qty2[t.node] if node_combo[t.node] > node_max[t.node]: node_max[t.node] = node_combo[t.node] node_peak[t.node] = Peak(time_point=t.time_point, qty1=node_qty1[t.node], qty2=node_qty2[t.node]) This produces the correct output, but I'm wondering if it can be made more readable/Pythonic, and/or faster/more scalable. The above is attractive in that it only loops through the (large) dataset once, but unattractive in that I've essentially copied/pasted three copies of the same algorithm. To avoid the copy/paste issues of the above, I tried this also: def find_peaks(level, dataset): def grouping(object, attr_name): if attr_name == 'system': return attr_name else: return object.__dict__[attrname] cuml_qty1 = defaultdict(int) cuml_qty2 = defaultdict(int) cuml_combo = defaultdict(int) level_max = defaultdict(int) level_peak = defaultdict(int) for t in dataset: cuml_qty1[grouping(t, level)] += t.qty1 cuml_qty2[grouping(t, level)] += t.qty2 cuml_combo[grouping(t, level)] = (cuml_qty1[grouping(t, level)] + cuml_qty2[grouping(t, level)]) if cuml_combo[grouping(t, level)] > level_max[grouping(t, level)]: level_max[grouping(t, level)] = cuml_combo[grouping(t, level)] level_peak[grouping(t, level)] = Peak(time_point=t.time_point, qty1=node_qty1[grouping(t, level)], qty2=node_qty2[grouping(t, level)]) return level_peak system_peak = find_peaks('system', dataset) cluster_peak = find_peaks('cluster', dataset) node_peak = find_peaks('node', dataset) For the (non-grouped) system-level calculations, I also came up with this, which is pretty: dataset.sort(key=operator.attrgetter('time_point')) def cuml_sum(seq): rseq = [] t = 0 for i in seq: t += i rseq.append(t) return rseq time_get = operator.attrgetter('time_point') q1_get = operator.attrgetter('qty1') q2_get = operator.attrgetter('qty2') timeline = [time_get(t) for t in dataset] cuml_qty1 = cuml_sum([q1_get(t) for t in dataset]) cuml_qty2 = cuml_sum([q2_get(t) for t in dataset]) cuml_combo = [q1 + q2 for q1, q2 in zip(cuml_qty1, cuml_qty2)] combo_max = max(cuml_combo) time_max = timeline.index(combo_max) q1_at_max = cuml_qty1.index(time_max) q2_at_max = cuml_qty2.index(time_max) However, despite this version's cool use of list comprehensions and zip(), it loops through the dataset three times just for the system-level calculations, and I can't think of a good way to do the cluster-level and node-level calaculations without doing something slow like: timeline = defaultdict(int) cuml_qty1 = defaultdict(int) #...etc. for c in cluster_list: timeline[c] = [time_get(t) for t in dataset if t.cluster == c] cuml_qty1[c] = [q1_get(t) for t in dataset if t.cluster == c] #...etc. Does anyone here at Stack Overflow have suggestions for improvements? The first snippet above runs well for my initial dataset (on the order of a million records), but later datasets will have more records and clusters/nodes, so scalability is a concern. This is my first non-trivial use of Python, and I want to make sure I'm taking proper advantage of the language (this is replacing a very convoluted set of SQL queries, and earlier versions of the Python version were essentially very ineffecient straight transalations of what that did). I don't normally do much programming, so I may be missing something elementary. Many thanks!

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  • apache chokes after 300 connections

    - by john titus
    We have an apache webserver in front of Tomcat hosted on EC2, instance type is extra large with 34GB memory. Our application deals with lot of external webservices and we have a very lousy external webservice which takes almost 300 seconds to respond to requests during peak hours. During peak hours the server chokes at just about 300 httpd processes. ps -ef | grep httpd | wc -l =300 I have googled and found numerous suggestions but nothing seems to work.. following are some configuration i have done which are directly taken from online resources. I have increased the limits of max connection and max clients in both apache and tomcat. here are the configuration details: //apache <IfModule prefork.c> StartServers 100 MinSpareServers 10 MaxSpareServers 10 ServerLimit 50000 MaxClients 50000 MaxRequestsPerChild 2000 </IfModule> //tomcat <Connector port="8080" protocol="org.apache.coyote.http11.Http11NioProtocol" connectionTimeout="600000" redirectPort="8443" enableLookups="false" maxThreads="1500" compressableMimeType="text/html,text/xml,text/plain,text/css,application/x-javascript,text/vnd.wap.wml,text/vnd.wap.wmlscript,application/xhtml+xml,application/xml-dtd,application/xslt+xml" compression="on"/> //Sysctl.conf net.ipv4.tcp_tw_reuse=1 net.ipv4.tcp_tw_recycle=1 fs.file-max = 5049800 vm.min_free_kbytes = 204800 vm.page-cluster = 20 vm.swappiness = 90 net.ipv4.tcp_rfc1337=1 net.ipv4.tcp_max_orphans = 65536 net.ipv4.ip_local_port_range = 5000 65000 net.core.somaxconn = 1024 I have been trying numerous suggestions but in vain.. how to fix this? I'm sure m2xlarge server should serve more requests than 300, probably i might be going wrong with my configuration.. The server chokes only during peak hours and when there are 300 concurrent requests waiting for the [300 second delayed] webservice to respond. Please help..

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  • Oracle Data Mining a Star Schema: Telco Churn Case Study

    - by charlie.berger
    There is a complete and detailed Telco Churn case study "How to" Blog Series just posted by Ari Mozes, ODM Dev. Manager.  In it, Ari provides detailed guidance in how to leverage various strengths of Oracle Data Mining including the ability to: mine Star Schemas and join tables and views together to obtain a complete 360 degree view of a customer combine transactional data e.g. call record detail (CDR) data, etc. define complex data transformation, model build and model deploy analytical methodologies inside the Database  His blog is posted in a multi-part series.  Below are some opening excerpts for the first 3 blog entries.  This is an excellent resource for any novice to skilled data miner who wants to gain competitive advantage by mining their data inside the Oracle Database.  Many thanks Ari! Mining a Star Schema: Telco Churn Case Study (1 of 3) One of the strengths of Oracle Data Mining is the ability to mine star schemas with minimal effort.  Star schemas are commonly used in relational databases, and they often contain rich data with interesting patterns.  While dimension tables may contain interesting demographics, fact tables will often contain user behavior, such as phone usage or purchase patterns.  Both of these aspects - demographics and usage patterns - can provide insight into behavior.Churn is a critical problem in the telecommunications industry, and companies go to great lengths to reduce the churn of their customer base.  One case study1 describes a telecommunications scenario involving understanding, and identification of, churn, where the underlying data is present in a star schema.  That case study is a good example for demonstrating just how natural it is for Oracle Data Mining to analyze a star schema, so it will be used as the basis for this series of posts...... Mining a Star Schema: Telco Churn Case Study (2 of 3) This post will follow the transformation steps as described in the case study, but will use Oracle SQL as the means for preparing data.  Please see the previous post for background material, including links to the case study and to scripts that can be used to replicate the stages in these posts.1) Handling missing values for call data recordsThe CDR_T table records the number of phone minutes used by a customer per month and per call type (tariff).  For example, the table may contain one record corresponding to the number of peak (call type) minutes in January for a specific customer, and another record associated with international calls in March for the same customer.  This table is likely to be fairly dense (most type-month combinations for a given customer will be present) due to the coarse level of aggregation, but there may be some missing values.  Missing entries may occur for a number of reasons: the customer made no calls of a particular type in a particular month, the customer switched providers during the timeframe, or perhaps there is a data entry problem.  In the first situation, the correct interpretation of a missing entry would be to assume that the number of minutes for the type-month combination is zero.  In the other situations, it is not appropriate to assume zero, but rather derive some representative value to replace the missing entries.  The referenced case study takes the latter approach.  The data is segmented by customer and call type, and within a given customer-call type combination, an average number of minutes is computed and used as a replacement value.In SQL, we need to generate additional rows for the missing entries and populate those rows with appropriate values.  To generate the missing rows, Oracle's partition outer join feature is a perfect fit.  select cust_id, cdre.tariff, cdre.month, minsfrom cdr_t cdr partition by (cust_id) right outer join     (select distinct tariff, month from cdr_t) cdre     on (cdr.month = cdre.month and cdr.tariff = cdre.tariff);   ....... Mining a Star Schema: Telco Churn Case Study (3 of 3) Now that the "difficult" work is complete - preparing the data - we can move to building a predictive model to help identify and understand churn.The case study suggests that separate models be built for different customer segments (high, medium, low, and very low value customer groups).  To reduce the data to a single segment, a filter can be applied: create or replace view churn_data_high asselect * from churn_prep where value_band = 'HIGH'; It is simple to take a quick look at the predictive aspects of the data on a univariate basis.  While this does not capture the more complex multi-variate effects as would occur with the full-blown data mining algorithms, it can give a quick feel as to the predictive aspects of the data as well as validate the data preparation steps.  Oracle Data Mining includes a predictive analytics package which enables quick analysis. begin  dbms_predictive_analytics.explain(   'churn_data_high','churn_m6','expl_churn_tab'); end; /select * from expl_churn_tab where rank <= 5 order by rank; ATTRIBUTE_NAME       ATTRIBUTE_SUBNAME EXPLANATORY_VALUE RANK-------------------- ----------------- ----------------- ----------LOS_BAND                                      .069167052          1MINS_PER_TARIFF_MON  PEAK-5                   .034881648          2REV_PER_MON          REV-5                    .034527798          3DROPPED_CALLS                                 .028110322          4MINS_PER_TARIFF_MON  PEAK-4                   .024698149          5From the above results, it is clear that some predictors do contain information to help identify churn (explanatory value > 0).  The strongest uni-variate predictor of churn appears to be the customer's (binned) length of service.  The second strongest churn indicator appears to be the number of peak minutes used in the most recent month.  The subname column contains the interior piece of the DM_NESTED_NUMERICALS column described in the previous post.  By using the object relational approach, many related predictors are included within a single top-level column. .....   NOTE:  These are just EXCERPTS.  Click here to start reading the Oracle Data Mining a Star Schema: Telco Churn Case Study from the beginning.    

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  • Will a database server perform better running on 2 CPUs with 16 cores or 4 CPUs with 8 cores?

    - by AlexOdin
    What I have: an online financial application (ASP.NET, C#) at peak we have 5K+ simultaneous users backend is running on Oracle 11g (active server + stand-by using Active Data Guard). At peak - 4K-5K database sessions Oracle is installed on Linux 5.8 (Oracle's unbreakable version) the database size: 7TB disk storage: NetApp (connected with 10GB network) I would like to replace old servers (IT will purchase HP blades BL685C). Servers will have 256GB of RAM. I need your help to figure out what to do with CPUs and cores. Options: 2 CPUs (2.3 GHz) with 16 cores each 4 CPUs (3.0 GHz) with 8 cores each Question: Which one should I pick? P.S. Next year, we will migrate from Oracle to SQL server. I hope, whatever option you recommend will work for both platforms

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  • How many users are "many users"?

    - by kemp
    I need to find a solution for a website which is struggling under load. The site gets ~500 simultaneous connections during peak time, and counts around 42k hits per day. It's a wordpress based site bridged with a vbulletin forum with a lot of contents and a fairly complex structure which makes intensive use of the database. I already implemented code level full page caching (without this the server just crashes), and configured all other caching directives as well as combining css files and the like to limit http requests as much as possible. I need to understand if there is more that can be done via software or if the load is just too much for the server to handle and it needs to be upgraded, because the server goes down occasionally during peak times. Can't access the server now, but it's a dedicated CentOS machine (I think 4GB ram, can't say what CPU) running apache/mysql. So back to the main question: how can I know when the users are just too many?

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  • php-cgi.exe process on IIS

    - by HYP
    The production server runs a PHP application on IIS 6.0. During the peak hours we have had a few issues where the php-cgi.exe processes increase in numbers and approach around 200. The server comes to a crawl and we have to restart the server a multiple times to restore the normal behavior. When the server is running normally, I have noticed that there are only 10-15 php-cgi.exe processes in the task manager. What could be causing the php-cgi.exe processes to increase in number from 10-15 to around 200 during the peak hours? Where should I look for a cause?

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  • Script Speed vs Memory Usage

    - by Doug Neiner
    I am working on an image generation script in PHP and have gotten it working two ways. One way is slow but uses a limited amount of memory, the second is much faster, but uses 6x the memory . There is no leakage in either script (as far as I can tell). In a limited benchmark, here is how they performed: -------------------------------------------- METHOD | TOTAL TIME | PEAK MEMORY | IMAGES -------------------------------------------- One | 65.626 | 540,036 | 200 Two | 20.207 | 3,269,600 | 200 -------------------------------------------- And here is the average of the previous numbers (if you don't want to do your own math): -------------------------------------------- METHOD | TOTAL TIME | PEAK MEMORY | IMAGES -------------------------------------------- One | 0.328 | 540,036 | 1 Two | 0.101 | 3,269,600 | 1 -------------------------------------------- Which method should I use and why? I anticipate this being used by a high volume of users, with each user making 10-20 requests to this script during a normal visit. I am leaning toward the faster method because though it uses more memory, it is for a 1/3 of the time and would reduce the number of concurrent requests.

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  • Split query result by half in TSQL (obtain 2 resultsets/tables)

    - by rubdottocom
    I have a query that returns a large number of heavy rows. When I transform this rows in a list of CustomObject I have a big memory peak, and this transformation is made by a custom dotnet framework that I can't modify. I need to retrieve a less number of rows to do "the transform" in two passes and then avoid the momery peak. How can I split the result of a query by half? I need to do it in DB layer. I thing to do a "Top count(*)/2" but how to get the other half? Thank you!

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  • Free Online Performance Tuning Event

    - by Andrew Kelly
      On June 9th 2010 I will be showing several sessions related to performance tuning for SQL Server and they are the best kind because they are free :).  So mark your calendars. Here is the event info and URL: June 29, 2010 - 10:00 am - 3:00 pm Eastern SQL Server is the platform for business. In this day-long free virtual event, well-known SQL Server performance expert Andrew Kelly will provide you with the tools and knowledge you need to stay on top of three key areas related to peak performance...(read more)

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  • Copy Only Backups for Adhoc Backups

    Introduction In most organizations backup plans are implemented using full differential and transactional log backups. The normal scenario would be take a full backup on Sunday (off peak hours), differential backup daily at mid-night and transactional log backups on hourly basis. What ... [Read Full Article]

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  • Speaking in Raleigh NC June 15th

    - by Andrew Kelly
    Just a heads up to those in the area that I will be speaking at the (TriPASS) Raleigh SQL Server user group on the 15th of June 2010. The topic is Storage & I/O Best Practices. The abstract is listed below: SQL Server relies heavily on a well configured storage sub-system to perform at its peak but unfortunately this is one of the most neglected or mis-configured areas of a SQL Server instance. Here we will focus on the best practices related to how SQL Server works with the underlying storage...(read more)

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  • Google I/O 2010 - Using Google Chrome Frame

    Google I/O 2010 - Using Google Chrome Frame Google I/O 2010 - Using Google Chrome Frame Chrome 201 Alex Russell Google Chrome Frame brings the HTML5 platform and fast Javascript performance to IE6, 7 & 8. This session will cover the latest on Google Chrome Frame, what it can do for you and your customers, how it can be used, and a sneak peak into what's planned next. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 4 0 ratings Time: 50:16 More in Science & Technology

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  • iwlwifi on lenovo z570 disabled by hardware switch

    - by Kevin Gallagher
    It was working fine with windows 7. The hardware switch is not disabled. I've toggled it back and forth dozens of times. The wifi light never turns on and it always lists as hardware disabled. I have the latest updates installed. I've been searching for solutions, but none of them seem to work for me. I've tried removing acer-wmi. I've tried setting 11n_disable=1. I've tried resetting the bios. I've tried using rfkill to unblock (only removes soft block). I've rebooted dozens of times. The wifi light turns off as soon as grub loads. Edit: I have a usb edimax wireless nic. It shows hardware disabled as well (although rfkill lists as unblocked). If I unload iwlwifi the usb nic works fine. uname -a `Linux xxx-Ideapad-Z570 3.2.0-55-generic #85-Ubuntu SMP Wed Oct 2 12:29:27 UTC 2013 x86_64 x86_64 x86_64 GNU/Linu`x rfkill list 19: phy18: Wireless LAN Soft blocked: no Hard blocked: yes dmesg [43463.022996] Intel(R) Wireless WiFi Link AGN driver for Linux, in-tree: [43463.023002] Copyright(c) 2003-2011 Intel Corporation [43463.023107] iwlwifi 0000:03:00.0: PCI INT A -> GSI 17 (level, low) -> IRQ 17 [43463.023190] iwlwifi 0000:03:00.0: setting latency timer to 64 [43463.023253] iwlwifi 0000:03:00.0: pci_resource_len = 0x00002000 [43463.023257] iwlwifi 0000:03:00.0: pci_resource_base = ffffc900057c8000 [43463.023261] iwlwifi 0000:03:00.0: HW Revision ID = 0x0 [43463.023797] iwlwifi 0000:03:00.0: irq 43 for MSI/MSI-X [43463.024013] iwlwifi 0000:03:00.0: Detected Intel(R) Centrino(R) Wireless-N 1000 BGN, REV=0x6C [43463.024250] iwlwifi 0000:03:00.0: L1 Enabled; Disabling L0S [43463.045496] iwlwifi 0000:03:00.0: device EEPROM VER=0x15d, CALIB=0x6 [43463.045501] iwlwifi 0000:03:00.0: Device SKU: 0X50 [43463.045504] iwlwifi 0000:03:00.0: Valid Tx ant: 0X1, Valid Rx ant: 0X3 [43463.045542] iwlwifi 0000:03:00.0: Tunable channels: 13 802.11bg, 0 802.11a channels [43463.045744] iwlwifi 0000:03:00.0: RF_KILL bit toggled to disable radio. [43463.047652] iwlwifi 0000:03:00.0: loaded firmware version 39.31.5.1 build 35138 [43463.047823] Registered led device: phy18-led [43463.047895] cfg80211: Ignoring regulatory request Set by core since the driver uses its own custom regulatory domain [43463.048037] ieee80211 phy18: Selected rate control algorithm 'iwl-agn-rs' [43463.055533] ADDRCONF(NETDEV_UP): wlan0: link is not ready nm-tool State: connected (global) - Device: wlan0 ---------------------------------------------------------------- Type: 802.11 WiFi Driver: iwlwifi State: unavailable Default: no HW Address: 74:E5:0B:4A:9F:C2 Capabilities: Wireless Properties WEP Encryption: yes WPA Encryption: yes WPA2 Encryption: yes Wireless Access Points lshw -C network *-network DISABLED description: Wireless interface product: Centrino Wireless-N 1000 [Condor Peak] vendor: Intel Corporation physical id: 0 bus info: pci@0000:03:00.0 logical name: wlan0 version: 00 serial: 74:e5:0b:4a:9f:c2 width: 64 bits clock: 33MHz capabilities: pm msi pciexpress bus_master cap_list ethernet physical wireless configuration: broadcast=yes driver=iwlwifi driverversion=3.2.0-55-generic firmware=39.31.5.1 build 35138 latency=0 link=no multicast=yes wireless=IEEE 802.11bg resources: irq:43 memory:f1500000-f1501fff lspci 03:00.0 Network controller: Intel Corporation Centrino Wireless-N 1000 [Condor Peak]

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  • SharePoint 2010 Video - Business Connectivity Services

    - by Sahil Malik
    Ad:: SharePoint 2007 Training in .NET 3.5 technologies (more information). The latest DNRTV episode on SharePoint 2010 Business Connectivity Services is now online. This is a video we (Carl and I) had recorded back in April 2010, and is a sneak peak to the SharePoint 2010 DVD that is finally on sale. Hope you like both. Comment on the article ....

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  • google analytics - real-time user stats vs audience overview user stats

    - by udog
    When looking at the real-time analytics reporting for our app, it shows around 150-180 users, say around 10AM (our peak usage time). When I look at the Audience Overview report for the same day (hourly breakdown), the number of users shown for the 10AM hour is over 1000. I'm sure this has to do with some sort of aggregation, but I would like to know more about how these two numbers are calculated in order to understand it.

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  • Webcast: Redefining the CRM and E-Commerce Experience with Oracle Exalogic

    - by Sanjeev Sharma
    Have your CRM applications been growing in cost and complexity? Do your customers want to reach you through more channels than ever? Does your business need to handle peak sales and customer service demand, but your IT budget only covers current needs? Learn how Oracle Exalogic combines Oracle hardware and software to achieve breakthrough performance and scalability, and how real Oracle customers are simplifying the deployment and management of their CRM applications. Register for the webcast here.

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  • Oracle OpenWorld Preview

    Learn. Develop. Explore. Here’s a sneak peak at Oracle OpenWorld San Francisco, which offers hundreds of learning opportunities, special programs, and networking events. Make plans to be in San Francisco October 11 through 14.

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  • Oracle OpenWorld Preview

    Learn. Develop. Explore. Here’s a sneak peak at Oracle OpenWorld San Francisco, which offers hundreds of learning opportunities, special programs, and networking events. Make plans to be in San Francisco October 11 through 14.

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  • Oracle OpenWorld Preview

    Learn. Develop. Explore. Here’s a sneak peak at Oracle OpenWorld San Francisco, which offers hundreds of learning opportunities, special programs, and networking events. Make plans to be in San Francisco October 11 through 14.

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  • Windows Cloud Services Aren’t Exclusive to Microsoft

    - by Ken Cox [MVP]
    The Windows Azure brand has captured mindshare for the buzzword-du-jour, ‘cloud computing’. However, Microsoft certainly isn’t the only option for cranking up virtual machines to meet unexpected or peak demands. For example, I see that OrcsWeb has released its Windows Cloud Servers product , starting at $99.99 a month*.  Competition is a good thing - and make sure you do some cost comparisons when researching cloud resources. Some of us were unpleasantly surprised by Azure’s pricing structure...(read more)

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  • What To Keep In Mind When You Design A Logo?

    Do you want your business to reach the highest peak of success no matter how big your competitors are? If yes, then you can?t afford to be slow or lazy. Realistically speaking, running your business ... [Author: Sha Amen - Web Design and Development - May 19, 2010]

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  • Windows typeperf and pound signs.

    - by Weegee
    I'm currently trying to use typeperf to access some Windows performance counters. Unfortunately, a few of the instances I'm trying to check are of the format service#1. The command typeperf "\\server\Process(service#1)\Working Set Peak" is unfortunately returning the data for \\server\Process(service)\Working Set Peak rather than the data for the instance service#1. This holds true for any of the services that have pound signs in the counter string. Does anyone know of a method to get around this problem? Sample output: I:\>typeperf -s server "\Process(service#1)\Working Set" "(PDH-CSV 4.0)","\\server\Process(service)\Working Set" "10/08/2009 09:37:29.070","1643274240.000000" "10/08/2009 09:37:30.070","1643274240.000000" "10/08/2009 09:37:31.070","1643274240.000000" The command completed successfully. I:\>typeperf -s server "\Process(service#2)\Working Set" "(PDH-CSV 4.0)","\\server\Process(service)\Working Set" "10/08/2009 09:37:39.273","1643274240.000000" "10/08/2009 09:37:40.273","1643274240.000000" "10/08/2009 09:37:41.273","1643274240.000000" "10/08/2009 09:37:42.273","1643274240.000000" "10/08/2009 09:37:43.273","1643274240.000000" The command completed successfully. I can confirm in PerfMon that the Working Set value "1643274240.000000" is incorrect for both service#1 and service#2. I am running Windows XP Service Pack 2, but a co-worker who is running Windows Server 2003 was having the same troubles.

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