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  • Performance of Managed C++ Vs UnManaged/native C++

    - by bsobaid
    I am writing a very high performance application that handles and processes hundreds of events every millisecond. Is Unmanaged C++ faster than managed c++? and why? Managed C++ deals with CLR instead of OS and CLR takes care of memory management, which simplifies the code and is probably also more efficient than code written by "a programmer" in unmanaged C++? or there is some other reason? When using managed, how can one then avoid dynamic memory allocation, which causes a performance hit, if it is all transparent to the programmer and handled by CLR? So coming back to my question, Is managed C++ more efficient in terms of speed than unmanaged C++ and why?

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  • Tag Suggestion system, approaches and ideas

    - by Galois
    Hi guys! -- I am working on a (auto) tag suggestion system (NOT tag autocomplete). Lets say I want to suggest tags for a given question like here on SO (although SO's tagging system is auto-complete). My main idea is to get the intersection between the tags_set and the given question.split()_set. (In python the set_intersection is efficient enough). Also, in order to make it a little bit more accurate I might use words-distance to count as 'the same' very close words i.e movie == movies. For now I am not thinking about using any Collaborative Filtering technique looking for the tags to similar questions and so on, because I believe since the question text is pretty short (comparing with a blog article or a paper) it is not worth the effort. So I was wondering if you have any other (more) efficient approaches to suggest. Any ideas, specially from people who they have done something like that before, are more than welcome.

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  • Random List of millions of elements in Python Efficiently

    - by eWizardII
    Hello, I have read this answer potentially as the best way to randomize a list of strings in Python. I'm just wondering then if that's the most efficient way to do it because I have a list of about 30 million elements via the following code: import json from sets import Set from random import shuffle a = [] for i in range(0,193): json_data = open("C:/Twitter/user/user_" + str(i) + ".json") data = json.load(json_data) for j in range(0,len(data)): a.append(data[j]['su']) new = list(Set(a)) print "Cleaned length is: " + str(len(new)) ## Take Cleaned List and Randomize it for Analysis shuffle(new) If there is a more efficient way to do it, I'd greatly appreciate any advice on how to do it. Thanks,

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  • Operator + for matrices in C++

    - by cibercitizen1
    I suppose the naive implementation of a + operator for matrices (2D for instance) in C++ would be: class Matrix { Matrix operator+ (Matrix other) const { Matrix result; // fill result with *this.data plus other.data return result; } } so we could use it like Matrix a; Matrix b; Matrix c; c = a + b; Right? But if matrices are big this is not efficient as we are doing one not-necessary copy (return result). Therefore, If we wan't to be efficient we have to forget the clean call: c = a + b; Right? What would you suggest / prefer ? Thanks.

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  • Fitting maximum amount of shapes on a surface

    - by Fuu
    In industry, there is often a problem where you need to calculate the most efficient use of material, be it fabric, wood, metal etc. So the starting point is X amount of shapes of given dimensions, made out of polygons and/or curved lines, and target is another polygon of given dimensions. I assume many of the current CAM suites implement this, but having no experience using them or of their internals, what kind of computational algorithm is used to find the most efficient use of space? Can someone point me to a book or other reference that discusses this subject?

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  • String Occurance Counting Algorithm

    - by Hellnar
    Hello, I am curious what is the most efficient algorithm (or commonly used) to count the number of occurances of a string in a chunck of text. From what I read, Boyer–Moore string search algorithm is the standard for string search but I am not sure if counting occurance in an efficient way would be same as searching a string. In python this is what I want: text_chunck = "one two three four one five six one" occurance_count(text_chunck, "one") # gives 3. Regards EDIT: It seems like python str.count serves me such method however I am not able to find what algorithm it uses.

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  • Why is writing a compiler in a functional language easier?

    - by wvd
    Hello all, I've been thinking of this question very long, but really couldn't find the answer on Google as well a similar question on Stackoverflow. If there is a duplicate, I'm sorry for that. A lot of people seem to say that writing compilers and other language tools in functional languages such as OCaml and Haskell is much more efficient and easier then writing them in imperative languages. Is this true? And if so -- why is it so efficient and easy to write them in functional languages instead of in an imperative language, like C? Also -- isn't a language tool in a functional language slower then in some low-level language like C? Thanks in advance, William v. Doorn

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  • Is it inefficient to access a python class member container in a loop statement?

    - by Dave
    Hi there. I'm trying to adopt some best practices to keep my python code efficient. I've heard that accessing a member variable inside of a loop can incur a dictionary lookup for every iteration of the loop, so I cache these in local variables to use inside the loop. My question is about the loop statement itself... if I have the following class: class A(object): def init(self) self.myList = [ 'a','b','c', 'd', 'e' ] Does the following code in a member function incur one, or one-per-loop-iteration (5) dictionary lookups? for letter in self.myList: print letter IE, should I adopt the following pattern, if I am concerned about efficiency... localList = self.myList for letter in localList: print letter or is that actually LESS efficient due to the local variable assign? Note, I am aware that early optimization is a dangerous pitfall if I'm concerned about the overall efficiency of code development. Here I am specifically asking about the efficiency of the code, not the coding. Thanks in advance! D

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  • How is fseek() implemented in the filesystem?

    - by pajton
    This is not a pure programming question, however it impacts the performance of programs using fseek(), hence it is important to know how it works. A little disclaimer so that it doesn't get closed. I am wondering how efficient it is to insert data in the middle of the file. Supposing I have a file with 1MB data and then I insert something at the 512KB offset. How efficient would that be compared to appending my data at the end of the file? Just to make the example complete lets say I want to insert 16KB of data. I understand the answer varies depending on the filesystem, however I assume that the techniques used in common filesystems are quite similar and I just want to get the right notion of it.

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  • Should I dive into ASP.NET MVC or start with ASP.NET Webforms?

    - by Sahat
    I plan to pick up Silverlight in the future. Possibility of going into Microsoft WPF. Currently learning Objective-C 2.0 w/ Cocoa. I already know Pros and Cons of ASP.NET MVC vs ASP.NET Webforms. What I want to know is what would be more "efficient" for me to learn given the circumstances above? By efficient I mean learning one design pattern once and then re-using it. Objective-C I believe uses MVC approach? What about Silverlight? WPF? So what do you think? Also as a side question is it true that ASP.NET Webforms is often used by freelancers/small companies and ASP.NET MVC in large enterprises?

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  • Hibernate Hql find result size for paginator

    - by KCore
    Hi, I need to add paginator for my Hibernate application. I applied it to some of my database operations which I perform using Criteria by setting Projection.count().This is working fine. But when I use hql to query, I can't seem to get and efficient method to get the result count. If I do query.list().size() it takes lot of time and I think hibernate does load all the objects in memory. Can anyone please suggest an efficient method to retrieve the result count when using hql?

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  • How to close a form in UserControl

    - by FJPoort
    I created a UserControl with the buttons Save, Close and Cancel. I want to close the form without saving on the Cancel button, prompt a message to save on the Close button and Save without closing on the Save button. Normally, I would have used this.Close() on the Cancel button, but the UserControl doesn't have such an option. So I guess I have to set a property for that. Scrolling down the "Questions that may already have your answer" section, I came across this question: How to close a ChildWindow from an UserControl button loaded inside it? I used the following C# code: private void btnCancel_Click(object sender, EventArgs e) { ProjectInfo infoScreen = (ProjectInfo)this.Parent; infoScreen.Close(); } This does the job for one screen, but I wonder if I have to apply this code for all the screen I have? I think there should be a more efficient way. So my question is: Do I need to apply this code for every form I have, or is there another (more efficient) way?

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  • Flash Media Server dynamic file naming

    - by flying_tiger
    I'm trying to figure out most efficient/safe way to name recorded streams on FMS. The case is to get listing of recorded streams from the server (eg. rec_001, rec_002...) and dynamically add rec_003 filename to the new stream that is being recorded. I'm thinking about either using FMS File Object and put everything in array of files every time I start recording procedure or to create XML file that would serve as a database of file names. I'm searching for a solution efficient for MULTIPLE connections at a time and large amount of files. Which one of presented would be the best for this purpose? Or do you have any better suggestions of solving this problem?

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  • Using a large list of terms, search through page text and replace words with links

    - by dunc
    A while ago I posted this question asking if it's possible to convert text to HTML links if they match a list of terms from my database. I have a fairly huge list of terms - around 6000. The accepted answer on that question was superb, but having never used XPath, I was at a loss when problems started occurring. At one point, after fiddling with code, I somehow managed to add over 40,000 random characters to our database - the majority of which required manual removal. Since then I've lost faith in that idea and the more simple PHP solutions simply weren't efficient enough to deal with the amount of data and the quantity of terms. My next attempt at a solution is to write a JS script which, once the page has loaded, retrieves the terms and matches them against the text on a page. This answer has an idea which I'd like to attempt. I would use AJAX to retrieve the terms from the database, to build an object such as this: var words = [ { word: 'Something', link: 'http://www.something.com' }, { word: 'Something Else', link: 'http://www.something.com/else' } ]; When the object has been built, I'd use this kind of code: //for each array element $.each(words, function() { //store it ("this" is gonna become the dom element in the next function) var search = this; $('.message').each( function() { //if it's exactly the same if ($(this).text() === search.word) { //do your magic tricks $(this).html('<a href="' + search.link + '">' + search.link + '</a>'); } } ); } ); Now, at first sight, there is a major issue here: with 6,000 terms, will this code be in any way efficient enough to do what I'm trying to do?. One option would possibly be to perform some of the overhead within the PHP script that the AJAX communicates with. For instance, I could send the ID of the post and then the PHP script could use SQL statements to retrieve all of the information from the post and match it against all 6,000 terms.. then the return call to the JavaScript could simply be the matching terms, which would significantly reduce the number of matches the above jQuery would make (around 50 at most). I have no problem with the script taking a few seconds to "load" on the user's browser, as long as it isn't impacting their CPU usage or anything like that. So, two questions in one: Can I make this work? What steps can I take to make it as efficient as possible? Thanks in advance,

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  • How to reverse a dictionary that it has repeated values (python)

    - by Galois
    Hi guys! So, I have a dictionary with almost 100,000 (key, values) pairs and the majority of the keys map to the same values. For example imagine something like that: dict = {'a': 1, 'c': 2, 'b': 1, 'e': 2, 'd': 3, 'h': 1, 'j': 3} What I want to do, is to reverse the dictionary so that each value in dict is going to be a key at the reverse_dict and is going to map to a list of all the dict.keys that used to map to that value at the dict. So based on the example above I would get: reversed_dict = {1: ['a', 'b', 'h'], 2:['e', 'c'] , 3:['d', 'j']} I came up with a solution that is very expensive and I would really want to hear any ideas more efficient than mine. my expensive solution: reversed_dict = {} for value in dict.values(): reversed_dict[value] = [] for key in dict.keys(): if dict[key] == value: if key not in reversed_dict[value]: reversed_dict[value].append(key) Output >> reversed_dict = {1: ['a', 'b', 'h'], 2: ['c', 'e'], 3: ['d', 'j']} I would really appreciate to hear any ideas better and more efficient than than mine. Thanks!

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  • fastest way to perform string search in general and in python

    - by Rkz
    My task is to search for a string or a pattern in a list of documents that are very short (say 200 characters long). However, say there are 1 million documents of such time. What is the most efficient way to perform this search?. I was thinking of tokenizing each document and putting the words in hashtable with words as key and document number as value, there by creating a bag of words. Then perform the word search and retrieve the list of documents that contained this word. From what I can see is this operation will take O(n) operations. Is there any other way? may be without using hash-tables?. Also, is there a python library or third party package that can perform efficient searches?

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  • Building my first Javascript Application (jQuery), struggling on something

    - by Jason Wells
    I'd really appreciate recommendations on the most efficient way to approach this. I'm building a simple javascript application which displays a list of records and allows the user to edit a record by clicking an "Edit" link in the records row. The user also can click the "Add" link to pop open a dialog allowing them to add a new record. Here's a working prototype of this: http://jsfiddle.net/FfRcG/ You'll note if you click "Edit" a dialog pops up with some canned values. And, if you click "Add", a dialog pops up with empty values. I need help on how to approach two problems I believe we need to pass our index to our edit dialog and reference the values within the JSON, but I am unsure how to pass the index when the user clicks edit. It bothers me that the Edit and Add div contents are so similiar (Edit just pre populates the values). I feel like there is a more efficient way of doing this but am at a loss. Here is my code for reference $(document).ready( function(){ // Our JSON (This would actually be coming from an AJAX database call) people = { "COLUMNS":["DATEMODIFIED", "NAME","AGE"], "DATA":[ ["9/6/2012", "Person 1","32"], ["9/5/2012","Person 2","23"] ] } // Here we loop over our JSON and build our HTML (Will refactor to use templating eventually) members = people.DATA; var newcontent = '<table width=50%><tr><td>date</td><td>name</td><td>age</td><td></td></tr>'; for(var i=0;i<members.length;i++) { newcontent+= '<tr id="member'+i+'"><td>' + members[i][0] + '</td>'; newcontent+= '<td>' + members[i][1] + '</td>'; newcontent+= '<td>' + members[i][2] + '</td>'; newcontent+= '<td><a href="#" class="edit" id=edit'+i+'>Edit</a></td><td>'; } newcontent += "</table>"; $("#result").html(newcontent); // Bind a dialog to the edit link $(".edit").click( function(){ // Trigger our dialog to open $("#edit").dialog("open"); // Not sure the most efficient way to change our dialog field values $("#name").val() // ??? alert($()); return false; }); // Bind a dialog to the add link $(".edit").click( function(){ // Trigger our dialog to open $("#add").dialog("open"); return false; }); // Bind a dialog to our edit DIV $("#edit").dialog(); // Bind a dialog to our add DIV $("#add").dialog(); }); And here's the HTML <h1>People</h1> <a href="#" class="add">Add a new person</a> <!-- Where results show up --> <div id="result"></div> <!-- Here's our edit DIV - I am not clear as to the best way to pass the index in our JSON so that we can reference positions in our array to pre populate the input values. --> <div id="edit"> <form> <p>Name:<br/><input type="text" id="name" value="foo"></p> <p>Age:<br/><input type="text" id="age" value="33"></p> <input type="submit" value="Save" id="submitEdit"> </form> </div> <!-- Here's our add DIV - This layout is so similiar to our edit dialog. What is the most efficient way to handle a situation like this? --> <div id="add"> <form> <p>Name:<br/><input type="text" id="name"></p> <p>Age:<br/><input type="text" id="age"></p> <input type="submit" value="Save" id="submitEdit"> </form> </div>

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  • Random Windows application crashes on Windows Server Hyper-V Core 2012

    - by Marlamin
    We're having some issues with our Hyper-V Core 2012 R2 installation on a HP DL360G8. We have an identical server with Hyper-V Core 2012 (not R2) that does not have these issues. When logging off from the physical server/via remote desktop, we sometimes get this error: Configure-SMRemoting.exe - Application Error : The application was unable to start correctly (0xc0000142). Click OK to close the application. We've also once or twice seen a "memory could not be read" error mentioning LoginUI.exe (another Windows app in System32) but have been unable to get an exact description. It's rather worrying to get such errors on a fresh install of Hyper-V 2012 R2. Is this even anything to worry about? Things we've done: Memtest86+, no memory errors Checksummed the file that is crashing with the one in the verified correct ISO, files match Server firmware upgrade to latest firmware of all present hardware, no visible changes Remade the RAID5 array , no change Reinstalled a few times, no change Reinstall without applying Windows updates after, no change

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  • VMWare workstation: from command line, how to start a VM in service mode (run in background)?

    - by GenEric35
    Hi, I have tried the vmrun and vmware.exe executables, but both of them start the vmware GUI when starting the VM. What I want to do is start the VM without starting the VMWare GUI. The reason I am doing this is after a few hours of idle, the guest OS becomes sluggish. It has lots of RAM but the only way I found to keep it's responsiveness optimal is to shutdown(dumps the memory) and the start; a restart of the guest OS doesnt dump the memory so I need to be able to do a stop of the VM, and then a start. So far the command I use are: C:\Program Files (x86)\VMware\VMware Workstationvmrun stop F:\VirtualMachines\R2\R2.vmx C:\Program Files (x86)\VMware\VMware Workstationvmrun start F:\VirtualMachines\R2\R2.vmx But the start command actually starts the VMWare Workstation GUI, which I don't need. I'm looking for a solution to start the VM without the VMWare Wokstation GUI, or a solution to what is causing the VM to become sluggish after a few hours of running idle.

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  • How to tune down the Hyperic built-in postgresql database for a small setup

    - by Svish
    We are testing out Hyperic 4.5.1 in a quite small environment for now. Currently there are just 1-5 agents and there probably won't be any more than 10-15. When I run ps ax there are 20(!) postgres processes running. For a small setup like this, that can't be necessary, can it? I'm a software developer and don't have much experience with setting up servers and such though, so don't really know. Either way, what settings are appropriate for a small Hyperic setup like this? Current, default and untouched configuration file, hqdb/data/postgresql.conf: # ----------------------------- # PostgreSQL configuration file # ----------------------------- # # This file consists of lines of the form: # # name = value # # (The '=' is optional.) White space may be used. Comments are introduced # with '#' anywhere on a line. The complete list of option names and # allowed values can be found in the PostgreSQL documentation. The # commented-out settings shown in this file represent the default values. # # Please note that re-commenting a setting is NOT sufficient to revert it # to the default value, unless you restart the server. # # Any option can also be given as a command line switch to the server, # e.g., 'postgres -c log_connections=on'. Some options can be changed at # run-time with the 'SET' SQL command. # # This file is read on server startup and when the server receives a # SIGHUP. If you edit the file on a running system, you have to SIGHUP the # server for the changes to take effect, or use "pg_ctl reload". Some # settings, which are marked below, require a server shutdown and restart # to take effect. # # Memory units: kB = kilobytes MB = megabytes GB = gigabytes # Time units: ms = milliseconds s = seconds min = minutes h = hours d = days #--------------------------------------------------------------------------- # FILE LOCATIONS #--------------------------------------------------------------------------- # The default values of these variables are driven from the -D command line # switch or PGDATA environment variable, represented here as ConfigDir. #data_directory = 'ConfigDir' # use data in another directory # (change requires restart) #hba_file = 'ConfigDir/pg_hba.conf' # host-based authentication file # (change requires restart) #ident_file = 'ConfigDir/pg_ident.conf' # ident configuration file # (change requires restart) # If external_pid_file is not explicitly set, no extra PID file is written. #external_pid_file = '(none)' # write an extra PID file # (change requires restart) #--------------------------------------------------------------------------- # CONNECTIONS AND AUTHENTICATION #--------------------------------------------------------------------------- # - Connection Settings - #listen_addresses = 'localhost' # what IP address(es) to listen on; # comma-separated list of addresses; # defaults to 'localhost', '*' = all # (change requires restart) port = 9432 # (change requires restart) max_connections = 100 # (change requires restart) # Note: increasing max_connections costs ~400 bytes of shared memory per # connection slot, plus lock space (see max_locks_per_transaction). You # might also need to raise shared_buffers to support more connections. #superuser_reserved_connections = 3 # (change requires restart) #unix_socket_directory = '' # (change requires restart) #unix_socket_group = '' # (change requires restart) #unix_socket_permissions = 0777 # octal # (change requires restart) #bonjour_name = '' # defaults to the computer name # (change requires restart) # - Security & Authentication - #authentication_timeout = 1min # 1s-600s #ssl = off # (change requires restart) #password_encryption = on #db_user_namespace = off # Kerberos #krb_server_keyfile = '' # (change requires restart) #krb_srvname = 'postgres' # (change requires restart) #krb_server_hostname = '' # empty string matches any keytab entry # (change requires restart) #krb_caseins_users = off # (change requires restart) # - TCP Keepalives - # see 'man 7 tcp' for details #tcp_keepalives_idle = 0 # TCP_KEEPIDLE, in seconds; # 0 selects the system default #tcp_keepalives_interval = 0 # TCP_KEEPINTVL, in seconds; # 0 selects the system default #tcp_keepalives_count = 0 # TCP_KEEPCNT; # 0 selects the system default #--------------------------------------------------------------------------- # RESOURCE USAGE (except WAL) #--------------------------------------------------------------------------- # - Memory - shared_buffers = 64MB # min 128kB or max_connections*16kB # (change requires restart) #temp_buffers = 8MB # min 800kB #max_prepared_transactions = 5 # can be 0 or more # (change requires restart) # Note: increasing max_prepared_transactions costs ~600 bytes of shared memory # per transaction slot, plus lock space (see max_locks_per_transaction). work_mem = 2MB # min 64kB maintenance_work_mem = 32MB # min 1MB #max_stack_depth = 2MB # min 100kB # - Free Space Map - max_fsm_pages = 204800 # min max_fsm_relations*16, 6 bytes each # (change requires restart) #max_fsm_relations = 1000 # min 100, ~70 bytes each # (change requires restart) # - Kernel Resource Usage - #max_files_per_process = 1000 # min 25 # (change requires restart) #shared_preload_libraries = '' # (change requires restart) # - Cost-Based Vacuum Delay - #vacuum_cost_delay = 0 # 0-1000 milliseconds #vacuum_cost_page_hit = 1 # 0-10000 credits #vacuum_cost_page_miss = 10 # 0-10000 credits #vacuum_cost_page_dirty = 20 # 0-10000 credits #vacuum_cost_limit = 200 # 0-10000 credits # - Background writer - #bgwriter_delay = 200ms # 10-10000ms between rounds #bgwriter_lru_percent = 1.0 # 0-100% of LRU buffers scanned/round #bgwriter_lru_maxpages = 5 # 0-1000 buffers max written/round #bgwriter_all_percent = 0.333 # 0-100% of all buffers scanned/round #bgwriter_all_maxpages = 5 # 0-1000 buffers max written/round #--------------------------------------------------------------------------- # WRITE AHEAD LOG #--------------------------------------------------------------------------- # - Settings - fsync = on # turns forced synchronization on or off #wal_sync_method = fsync # the default is the first option # supported by the operating system: # open_datasync # fdatasync # fsync # fsync_writethrough # open_sync #full_page_writes = on # recover from partial page writes #wal_buffers = 64kB # min 32kB # (change requires restart) commit_delay = 100000 # range 0-100000, in microseconds #commit_siblings = 5 # range 1-1000 # - Checkpoints - checkpoint_segments = 10 # in logfile segments, min 1, 16MB each #checkpoint_timeout = 5min # range 30s-1h #checkpoint_warning = 30s # 0 is off # - Archiving - #archive_command = '' # command to use to archive a logfile segment #archive_timeout = 0 # force a logfile segment switch after this # many seconds; 0 is off #--------------------------------------------------------------------------- # QUERY TUNING #--------------------------------------------------------------------------- # - Planner Method Configuration - #enable_bitmapscan = on #enable_hashagg = on #enable_hashjoin = on #enable_indexscan = on #enable_mergejoin = on #enable_nestloop = on #enable_seqscan = on #enable_sort = on #enable_tidscan = on # - Planner Cost Constants - #seq_page_cost = 1.0 # measured on an arbitrary scale #random_page_cost = 4.0 # same scale as above #cpu_tuple_cost = 0.01 # same scale as above #cpu_index_tuple_cost = 0.005 # same scale as above #cpu_operator_cost = 0.0025 # same scale as above #effective_cache_size = 128MB # - Genetic Query Optimizer - #geqo = on #geqo_threshold = 12 #geqo_effort = 5 # range 1-10 #geqo_pool_size = 0 # selects default based on effort #geqo_generations = 0 # selects default based on effort #geqo_selection_bias = 2.0 # range 1.5-2.0 # - Other Planner Options - #default_statistics_target = 10 # range 1-1000 #constraint_exclusion = off #from_collapse_limit = 8 #join_collapse_limit = 8 # 1 disables collapsing of explicit # JOINs #--------------------------------------------------------------------------- # ERROR REPORTING AND LOGGING #--------------------------------------------------------------------------- # - Where to Log - log_destination = 'stderr' # Valid values are combinations of # stderr, syslog and eventlog, # depending on platform. # This is used when logging to stderr: redirect_stderr = on # Enable capturing of stderr into log # files # (change requires restart) # These are only used if redirect_stderr is on: log_directory = '../../logs' # Directory where log files are written # Can be absolute or relative to PGDATA log_filename = 'hqdb-%Y-%m-%d.log' # Log file name pattern. # Can include strftime() escapes #log_truncate_on_rotation = off # If on, any existing log file of the same # name as the new log file will be # truncated rather than appended to. But # such truncation only occurs on # time-driven rotation, not on restarts # or size-driven rotation. Default is # off, meaning append to existing files # in all cases. log_rotation_age = 1d # Automatic rotation of logfiles will # happen after that time. 0 to # disable. #log_rotation_size = 10MB # Automatic rotation of logfiles will # happen after that much log # output. 0 to disable. # These are relevant when logging to syslog: #syslog_facility = 'LOCAL0' #syslog_ident = 'postgres' # - When to Log - #client_min_messages = notice # Values, in order of decreasing detail: # debug5 # debug4 # debug3 # debug2 # debug1 # log # notice # warning # error #log_min_messages = notice # Values, in order of decreasing detail: # debug5 # debug4 # debug3 # debug2 # debug1 # info # notice # warning # error # log # fatal # panic #log_error_verbosity = default # terse, default, or verbose messages #log_min_error_statement = error # Values in order of increasing severity: # debug5 # debug4 # debug3 # debug2 # debug1 # info # notice # warning # error # fatal # panic (effectively off) log_min_duration_statement = 10000 # -1 is disabled, 0 logs all statements # and their durations. #silent_mode = off # DO NOT USE without syslog or # redirect_stderr # (change requires restart) # - What to Log - #debug_print_parse = off #debug_print_rewritten = off #debug_print_plan = off #debug_pretty_print = off #log_connections = off #log_disconnections = off #log_duration = off #log_line_prefix = '' # Special values: # %u = user name # %d = database name # %r = remote host and port # %h = remote host # %p = PID # %t = timestamp (no milliseconds) # %m = timestamp with milliseconds # %i = command tag # %c = session id # %l = session line number # %s = session start timestamp # %x = transaction id # %q = stop here in non-session # processes # %% = '%' # e.g. '<%u%%%d> ' #log_statement = 'none' # none, ddl, mod, all #log_hostname = off #--------------------------------------------------------------------------- # RUNTIME STATISTICS #--------------------------------------------------------------------------- # - Query/Index Statistics Collector - #stats_command_string = on #update_process_title = on stats_start_collector = on # needed for block or row stats # (change requires restart) stats_block_level = on stats_row_level = on stats_reset_on_server_start = off # (change requires restart) # - Statistics Monitoring - #log_parser_stats = off #log_planner_stats = off #log_executor_stats = off #log_statement_stats = off #--------------------------------------------------------------------------- # AUTOVACUUM PARAMETERS #--------------------------------------------------------------------------- #autovacuum = off # enable autovacuum subprocess? # 'on' requires stats_start_collector # and stats_row_level to also be on #autovacuum_naptime = 1min # time between autovacuum runs #autovacuum_vacuum_threshold = 500 # min # of tuple updates before # vacuum #autovacuum_analyze_threshold = 250 # min # of tuple updates before # analyze #autovacuum_vacuum_scale_factor = 0.2 # fraction of rel size before # vacuum #autovacuum_analyze_scale_factor = 0.1 # fraction of rel size before # analyze #autovacuum_freeze_max_age = 200000000 # maximum XID age before forced vacuum # (change requires restart) #autovacuum_vacuum_cost_delay = -1 # default vacuum cost delay for # autovacuum, -1 means use # vacuum_cost_delay #autovacuum_vacuum_cost_limit = -1 # default vacuum cost limit for # autovacuum, -1 means use # vacuum_cost_limit #--------------------------------------------------------------------------- # CLIENT CONNECTION DEFAULTS #--------------------------------------------------------------------------- # - Statement Behavior - #search_path = '"$user",public' # schema names #default_tablespace = '' # a tablespace name, '' uses # the default #check_function_bodies = on #default_transaction_isolation = 'read committed' #default_transaction_read_only = off #statement_timeout = 0 # 0 is disabled #vacuum_freeze_min_age = 100000000 # - Locale and Formatting - datestyle = 'iso, mdy' #timezone = unknown # actually, defaults to TZ # environment setting #timezone_abbreviations = 'Default' # select the set of available timezone # abbreviations. Currently, there are # Default # Australia # India # However you can also create your own # file in share/timezonesets/. #extra_float_digits = 0 # min -15, max 2 #client_encoding = sql_ascii # actually, defaults to database # encoding # These settings are initialized by initdb -- they might be changed lc_messages = 'C' # locale for system error message # strings lc_monetary = 'C' # locale for monetary formatting lc_numeric = 'C' # locale for number formatting lc_time = 'C' # locale for time formatting # - Other Defaults - #explain_pretty_print = on #dynamic_library_path = '$libdir' #local_preload_libraries = '' #--------------------------------------------------------------------------- # LOCK MANAGEMENT #--------------------------------------------------------------------------- #deadlock_timeout = 1s #max_locks_per_transaction = 64 # min 10 # (change requires restart) # Note: each lock table slot uses ~270 bytes of shared memory, and there are # max_locks_per_transaction * (max_connections + max_prepared_transactions) # lock table slots. #--------------------------------------------------------------------------- # VERSION/PLATFORM COMPATIBILITY #--------------------------------------------------------------------------- # - Previous Postgres Versions - #add_missing_from = off #array_nulls = on #backslash_quote = safe_encoding # on, off, or safe_encoding #default_with_oids = off #escape_string_warning = on #standard_conforming_strings = off #regex_flavor = advanced # advanced, extended, or basic #sql_inheritance = on # - Other Platforms & Clients - #transform_null_equals = off #--------------------------------------------------------------------------- # CUSTOMIZED OPTIONS #--------------------------------------------------------------------------- #custom_variable_classes = '' # list of custom variable class names SELECT * FROM pg_stat_activity; datid | datname | procpid | usesysid | usename | current_query | waiting | query_start | backend_start | client_addr | client_port -------+---------+---------+----------+---------+---------------------------------+---------+-------------------------------+-------------------------------+-------------+------------- 16384 | hqdb | 3267 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.036781+01 | 2011-02-08 15:51:20.02413+01 | 127.0.0.1 | 47892 16384 | hqdb | 3268 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.050994+01 | 2011-02-08 15:51:20.047393+01 | 127.0.0.1 | 47893 16384 | hqdb | 3269 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.056661+01 | 2011-02-08 15:51:20.053201+01 | 127.0.0.1 | 47894 16384 | hqdb | 3271 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.062351+01 | 2011-02-08 15:51:20.058822+01 | 127.0.0.1 | 47895 16384 | hqdb | 3272 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.068328+01 | 2011-02-08 15:51:20.064517+01 | 127.0.0.1 | 47896 16384 | hqdb | 3273 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.07444+01 | 2011-02-08 15:51:20.070755+01 | 127.0.0.1 | 47897 16384 | hqdb | 3274 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.080941+01 | 2011-02-08 15:51:20.076983+01 | 127.0.0.1 | 47898 16384 | hqdb | 3275 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.08741+01 | 2011-02-08 15:51:20.083697+01 | 127.0.0.1 | 47899 16384 | hqdb | 3276 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.093597+01 | 2011-02-08 15:51:20.089977+01 | 127.0.0.1 | 47900 16384 | hqdb | 3277 | 10 | hqadmin | <IDLE> in transaction | f | 2011-02-08 15:51:20.133974+01 | 2011-02-08 15:51:20.096149+01 | 127.0.0.1 | 47901 16384 | hqdb | 3308 | 10 | hqadmin | <IDLE> | f | 2011-02-09 10:49:27.402197+01 | 2011-02-08 15:51:29.826321+01 | 127.0.0.1 | 47902 16384 | hqdb | 3309 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.572395+01 | 2011-02-08 15:51:29.865243+01 | 127.0.0.1 | 47903 16384 | hqdb | 3310 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.586273+01 | 2011-02-08 15:51:29.874346+01 | 127.0.0.1 | 47904 16384 | hqdb | 3311 | 10 | hqadmin | <IDLE> | f | 2011-02-09 10:10:03.024088+01 | 2011-02-08 15:51:29.883598+01 | 127.0.0.1 | 47905 16384 | hqdb | 3312 | 10 | hqadmin | <IDLE> in transaction | f | 2011-02-08 15:51:35.804457+01 | 2011-02-08 15:51:29.892925+01 | 127.0.0.1 | 47906 16384 | hqdb | 3418 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.580207+01 | 2011-02-08 15:51:55.56911+01 | 127.0.0.1 | 47910 16384 | hqdb | 3419 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.59781+01 | 2011-02-08 15:51:55.588609+01 | 127.0.0.1 | 47911 16384 | hqdb | 3422 | 10 | hqadmin | <IDLE> | f | 2011-02-09 10:10:02.668836+01 | 2011-02-08 15:51:55.603076+01 | 127.0.0.1 | 47914 16384 | hqdb | 3421 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.770427+01 | 2011-02-08 15:51:55.603086+01 | 127.0.0.1 | 47913 16384 | hqdb | 3420 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.680785+01 | 2011-02-08 15:51:55.637058+01 | 127.0.0.1 | 47912 16384 | hqdb | 18233 | 10 | hqadmin | SELECT * FROM pg_stat_activity; | f | 2011-02-09 10:49:29.688949+01 | 2011-02-09 10:48:13.031475+01 | | -1 (21 rows)

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