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  • Why after deleting a 110+ GB collection, my /var/lib/mongodb directory still have same size?

    - by tunnuz
    I am having some troubles with MongoDB and space usage. In particular, I once used to have a large collection of about 600 million records totaling 110+ GB on disk. Recently I decided to drop it because the data was outdated, to do so I dropped the collection through rockmongo's web interface. Accordingly, rockmongo doesn't show me the collection anymore, however my disk usage hasn't changed at all. Is there any clean operation which I am not aware of, which must be run in order to synchronize the database with database files on disk? I have tried to perform a "repair" but the system complains that there's not enough space on disk ... that's because it is all used by MongoDB.

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  • What hardware makes a good MongoDB Server ? Where to get it ?

    - by João Pinto Jerónimo
    Suppose you're on dell.com right now and you're buying a server to run your MongoDB database for your small startup. You will have to handle literally tens of thousands of writes and reads per minute (but small objects). Would you go for 2 processors ? Invest more on RAM ? I've heard (correct me if I'm wrong) MongoDB handles the most it can on the RAM and then flushes everything to the disk, in that case I should invest on a CPU with a large L2 cache, probably 40GB of RAM and a solid state drive.. right ? Would I be better off with a high end (~$11,309, 2 expensive processors, 96GB of RAM) server or 2x(~$6,419, 2 expensive processors, 12GB of RAM) servers ? Is Dell ok or do you have better sugestions ? (I'm outside the US, on Portugal)

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  • Why doesn't MongoDb store my slashes in this string?

    - by Rob Dudley
    Can anyone tell me why this command doesn't work from the MongoDB shell client: db.coll.update({'live':true},{$set:{'mask':"\D\D\D\D\D\D\D\D"}},false,true) but db.coll.findOne({'id':'someId'}) returns the mask field as: "mask" : "DDDDDDDD", Where are the slashes going? I've tried "double escaping" with \\D and that inserts both slashes: "mask" : "\\D\\D\\D\\D\\D\\D\\D\\D", MongoDB shell version: 2.0.6, MongoDB version: 2.0.5, OSX Lion Thanks

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  • Does the order of columns in a query matter?

    - by James Simpson
    When selecting columns from a MySQL table, is performance affected by the order that you select the columns as compared to their order in the table (not considering indexes that may cover the columns)? For example, you have a table with rows uid, name, bday, and you have the following query. SELECT uid, name, bday FROM table Does MySQL see the following query any differently and thus cause any sort of performance hit? SELECT uid, bday, name FROM table

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  • Problem with JMX query of Coherence node MBeans visible in JConsole

    - by Quinn Taylor
    I'm using JMX to build a custom tool for monitoring remote Coherence clusters at work. I'm able to connect just fine and query MBeans directly, and I've acquired nearly all the information I need. However, I've run into a snag when trying to query MBeans for specific caches within a cluster, which is where I can find stats about total number of gets/puts, average time for each, etc. The MBeans I'm trying to access programatically are visible when I connect to the remote process using JConsole, and have names like this: Coherence:type=Cache,service=SequenceQueue,name=SEQ%GENERATOR,nodeId=1,tier=back It would make it more flexible if I can dynamically grab all type=Cache MBeans for a particular node ID without specifying all the caches. I'm trying to query them like this: QueryExp specifiedNodeId = Query.eq(Query.attr("nodeId"), Query.value(nodeId)); QueryExp typeIsCache = Query.eq(Query.attr("type"), Query.value("Cache")); QueryExp cacheNodes = Query.and(specifiedNodeId, typeIsCache); ObjectName coherence = new ObjectName("Coherence:*"); Set<ObjectName> cacheMBeans = mBeanServer.queryMBeans(coherence, cacheNodes); However, regardless of whether I use queryMBeans() or queryNames(), the query returns a Set containing... ...0 objects if I pass the arguments shown above ...0 objects if I pass null for the first argument ...all MBeans in the Coherence:* domain (112) if I pass null for the second argument ...every single MBean (128) if I pass null for both arguments The first two results are the unexpected ones, and suggest a problem in the QueryExp I'm passing, but I can't figure out what the problem is. I even tried just passing typeIsCache or specifiedNodeId for the second parameter (with either coherence or null as the first parameter) and I always get 0 results. I'm pretty green with JMX — any insight on what the problem is? (FYI, the monitoring tool will be run on Java 5, so things like JMX 2.0 won't help me at this point.)

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  • MySQL select query result set changes based on column order

    - by user197191
    I have a drupal 7 site using the Views module to back-end site content search results. The same query with the same dataset returns different results from MySQL 5.5.28 to MySQL 5.6.14. The results from 5.5.28 are the correct, expected results. The results from 5.6.14 are not. If, however, I simply move a column in the select statement, the query returns the correct results. Here is the code-generated query in question (modified for readability). I apologize for the length; I couldn't find a way to reproduce it without the whole query: SELECT DISTINCT node_node_revision.nid AS node_node_revision_nid, node_revision.title AS node_revision_title, node_field_revision_field_position_institution_ref.nid AS node_field_revision_field_position_institution_ref_nid, node_revision.vid AS vid, node_revision.nid AS node_revision_nid, node_node_revision.title AS node_node_revision_title, SUM(search_index.score * search_total.count) AS score, 'node' AS field_data_field_system_inst_name_node_entity_type, 'node' AS field_revision_field_position_college_division_node_entity_t, 'node' AS field_revision_field_position_department_node_entity_type, 'node' AS field_revision_field_search_lvl_degree_lvls_node_entity_type, 'node' AS field_revision_field_position_app_deadline_node_entity_type, 'node' AS field_revision_field_position_start_date_node_entity_type, 'node' AS field_revision_body_node_entity_type FROM node_revision node_revision LEFT JOIN node node_node_revision ON node_revision.nid = node_node_revision.nid LEFT JOIN field_revision_field_position_institution_ref field_revision_field_position_institution_ref ON node_revision.vid = field_revision_field_position_institution_ref.revision_id AND (field_revision_field_position_institution_ref.entity_type = 'node' AND field_revision_field_position_institution_ref.deleted = '0') LEFT JOIN node node_field_revision_field_position_institution_ref ON field_revision_field_position_institution_ref.field_position_institution_ref_target_id = node_field_revision_field_position_institution_ref.nid LEFT JOIN field_revision_field_position_cip_code field_revision_field_position_cip_code ON node_revision.vid = field_revision_field_position_cip_code.revision_id AND (field_revision_field_position_cip_code.entity_type = 'node' AND field_revision_field_position_cip_code.deleted = '0') LEFT JOIN node node_field_revision_field_position_cip_code ON field_revision_field_position_cip_code.field_position_cip_code_target_id = node_field_revision_field_position_cip_code.nid LEFT JOIN node node_node_revision_1 ON node_revision.nid = node_node_revision_1.nid LEFT JOIN field_revision_field_position_vacancy_status field_revision_field_position_vacancy_status ON node_revision.vid = field_revision_field_position_vacancy_status.revision_id AND (field_revision_field_position_vacancy_status.entity_type = 'node' AND field_revision_field_position_vacancy_status.deleted = '0') LEFT JOIN search_index search_index ON node_revision.nid = search_index.sid LEFT JOIN search_total search_total ON search_index.word = search_total.word WHERE ( ( (node_node_revision.status = '1') AND (node_node_revision.type IN ('position')) AND (field_revision_field_position_vacancy_status.field_position_vacancy_status_target_id IN ('38')) AND( (search_index.type = 'node') AND( (search_index.word = 'accountant') ) ) AND ( (node_revision.vid=node_node_revision.vid AND node_node_revision.status=1) ) ) ) GROUP BY search_index.sid, vid, score, field_data_field_system_inst_name_node_entity_type, field_revision_field_position_college_division_node_entity_t, field_revision_field_position_department_node_entity_type, field_revision_field_search_lvl_degree_lvls_node_entity_type, field_revision_field_position_app_deadline_node_entity_type, field_revision_field_position_start_date_node_entity_type, field_revision_body_node_entity_type HAVING ( ( (COUNT(*) >= '1') ) ) ORDER BY node_node_revision_title ASC LIMIT 20 OFFSET 0; Again, this query returns different sets of results from MySQL 5.5.28 (correct) to 5.6.14 (incorrect). If I move the column named "score" (the SUM() column) to the end of the column list, the query returns the correct set of results in both versions of MySQL. My question is: Is this expected behavior (and why), or is this a bug? I'm on the verge of reverting my entire environment back to 5.5 because of this.

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  • In MySQL, what is the most effective query design for joining large tables with many to many relatio

    - by lighthouse65
    In our application, we collect data on automotive engine performance -- basically source data on engine performance based on the engine type, the vehicle running it and the engine design. Currently, the basis for new row inserts is an engine on-off period; we monitor performance variables based on a change in engine state from active to inactive and vice versa. The related engineState table looks like this: +---------+-----------+---------------+---------------------+---------------------+-----------------+ | vehicle | engine | engine_state | state_start_time | state_end_time | engine_variable | +---------+-----------+---------------+---------------------+---------------------+-----------------+ | 080025 | E01 | active | 2008-01-24 16:19:15 | 2008-01-24 16:24:45 | 720 | | 080028 | E02 | inactive | 2008-01-24 16:19:25 | 2008-01-24 16:22:17 | 304 | +---------+-----------+---------------+---------------------+---------------------+-----------------+ For a specific analysis, we would like to analyze table content based on a row granularity of minutes, rather than the current basis of active / inactive engine state. For this, we are thinking of creating a simple productionMinute table with a row for each minute in the period we are analyzing and joining the productionMinute and engineEvent tables on the date-time columns in each table. So if our period of analysis is from 2009-12-01 to 2010-02-28, we would create a new table with 129,600 rows, one for each minute of each day for that three-month period. The first few rows of the productionMinute table: +---------------------+ | production_minute | +---------------------+ | 2009-12-01 00:00 | | 2009-12-01 00:01 | | 2009-12-01 00:02 | | 2009-12-01 00:03 | +---------------------+ The join between the tables would be engineState AS es LEFT JOIN productionMinute AS pm ON es.state_start_time <= pm.production_minute AND pm.production_minute <= es.event_end_time. This join, however, brings up multiple environmental issues: The engineState table has 5 million rows and the productionMinute table has 130,000 rows When an engineState row spans more than one minute (i.e. the difference between es.state_start_time and es.state_end_time is greater than one minute), as is the case in the example above, there are multiple productionMinute table rows that join to a single engineState table row When there is more than one engine in operation during any given minute, also as per the example above, multiple engineState table rows join to a single productionMinute row In testing our logic and using only a small table extract (one day rather than 3 months, for the productionMinute table) the query takes over an hour to generate. In researching this item in order to improve performance so that it would be feasible to query three months of data, our thoughts were to create a temporary table from the engineEvent one, eliminating any table data that is not critical for the analysis, and joining the temporary table to the productionMinute table. We are also planning on experimenting with different joins -- specifically an inner join -- to see if that would improve performance. What is the best query design for joining tables with the many:many relationship between the join predicates as outlined above? What is the best join type (left / right, inner)?

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  • MongoDB and GrifFS. What are the best storage options in the range of 1 TB?

    - by Nerian
    We are going to launch a service that will require between 1 and 2 GB for file storage per paid user. I am going to use GridFS for storing files. I am pondering the different options for storing the database. But since I am unexperienced at deployment and it is my first time with Mongodb I need your experience. Criteria: I want to spend my time developing my core business, that is, my own application. I am a Ruby on Rails developer. I do not like to mess with server configuration. Hence, I would like a fully managed hosting solution. But I would like to know about any other option, if you think it is worth it. It should be able to scale. Cloud style. Pay as you go. The lower the price, the better. So far I known of these services: https://mongohq.com/pricing https://mongomachine.com/pricing https://mongolab.com/about/pricing/ http://cloudcontrol.com/add-ons/mongodb/ And they seem to be OK for common needs, that is no file storage. But I am going to use GridFS, so the size matters. These services seems to scale, in price, quite poorly. MongoHQ: The larger plan max storage is 20 GB. Seems like a very little storage, for GridFS. MongoMachine: Flat price, 2.5$ per GB. I didn't found the limit. Seems like a good price, comparing the others. MongoLab: 3.984 GB max, which I don't think I will hit, so perfect. 8$ per GB, quite costly. CloudControl: The larger plan is 20 Gb. The custom service starts at 250€ plus some unspecified charge per GB. What is your experience with these services? Any downtimes? Other possibilities?

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  • SQL SERVER – Signal Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28

    - by pinaldave
    In this post, let’s delve a bit more in depth regarding wait stats. The very first question: when do the wait stats occur? Here is the simple answer. When SQL Server is executing any task, and if for any reason it has to wait for resources to execute the task, this wait is recorded by SQL Server with the reason for the delay. Later on we can analyze these wait stats to understand the reason the task was delayed and maybe we can eliminate the wait for SQL Server. It is not always possible to remove the wait type 100%, but there are few suggestions that can help. Before we continue learning about wait types and wait stats, we need to understand three important milestones of the query life-cycle. Running - a query which is being executed on a CPU is called a running query. This query is responsible for CPU time. Runnable – a query which is ready to execute and waiting for its turn to run is called a runnable query. This query is responsible for Signal Wait time. (In other words, the query is ready to run but CPU is servicing another query). Suspended – a query which is waiting due to any reason (to know the reason, we are learning wait stats) to be converted to runnable is suspended query. This query is responsible for wait time. (In other words, this is the time we are trying to reduce). In simple words, query execution time is a summation of the query Executing CPU Time (Running) + Query Wait Time (Suspended) + Query Signal Wait Time (Runnable). Again, it may be possible a query goes to all these stats multiple times. Let us try to understand the whole thing with a simple analogy of a taxi and a passenger. Two friends, Tom and Danny, go to the mall together. When they leave the mall, they decide to take a taxi. Tom and Danny both stand in the line waiting for their turn to get into the taxi. This is the Signal Wait Time as they are ready to get into the taxi but the taxis are currently serving other customer and they have to wait for their turn. In other word they are in a runnable state. Now when it is their turn to get into the taxi, the taxi driver informs them he does not take credit cards and only cash is accepted. Neither Tom nor Danny have enough cash, they both cannot get into the vehicle. Tom waits outside in the queue and Danny goes to ATM to fetch the cash. During this time the taxi cannot wait, they have to let other passengers get into the taxi. As Tom and Danny both are outside in the queue, this is the Query Wait Time and they are in the suspended state. They cannot do anything till they get the cash. Once Danny gets the cash, they are both standing in the line again, creating one more Signal Wait Time. This time when their turn comes they can pay the taxi driver in cash and reach their destination. The time taken for the taxi to get from the mall to the destination is running time (CPU time) and the taxi is running. I hope this analogy is bit clear with the wait stats. You can check the Signalwait stats using following query of Glenn Berry. -- Signal Waits for instance SELECT CAST(100.0 * SUM(signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%signal (cpu) waits], CAST(100.0 * SUM(wait_time_ms - signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%resource waits] FROM sys.dm_os_wait_stats OPTION (RECOMPILE); Higher the Signal wait stats are not good for the system. Very high value indicates CPU pressure. In my experience, when systems are running smooth and without any glitch the Signal wait stat is lower than 20%. Again, this number can be debated (and it is from my experience and is not documented anywhere). In other words, lower is better and higher is not good for the system. In future articles we will discuss in detail the various wait types and wait stats and their resolution. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • SQL SERVER – Single Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28

    - by pinaldave
    In this post, let’s delve a bit more in depth regarding wait stats. The very first question: when do the wait stats occur? Here is the simple answer. When SQL Server is executing any task, and if for any reason it has to wait for resources to execute the task, this wait is recorded by SQL Server with the reason for the delay. Later on we can analyze these wait stats to understand the reason the task was delayed and maybe we can eliminate the wait for SQL Server. It is not always possible to remove the wait type 100%, but there are few suggestions that can help. Before we continue learning about wait types and wait stats, we need to understand three important milestones of the query life-cycle. Running - a query which is being executed on a CPU is called a running query. This query is responsible for CPU time. Runnable – a query which is ready to execute and waiting for its turn to run is called a runnable query. This query is responsible for Single Wait time. (In other words, the query is ready to run but CPU is servicing another query). Suspended – a query which is waiting due to any reason (to know the reason, we are learning wait stats) to be converted to runnable is suspended query. This query is responsible for wait time. (In other words, this is the time we are trying to reduce). In simple words, query execution time is a summation of the query Executing CPU Time (Running) + Query Wait Time (Suspended) + Query Single Wait Time (Runnable). Again, it may be possible a query goes to all these stats multiple times. Let us try to understand the whole thing with a simple analogy of a taxi and a passenger. Two friends, Tom and Danny, go to the mall together. When they leave the mall, they decide to take a taxi. Tom and Danny both stand in the line waiting for their turn to get into the taxi. This is the Signal Wait Time as they are ready to get into the taxi but the taxis are currently serving other customer and they have to wait for their turn. In other word they are in a runnable state. Now when it is their turn to get into the taxi, the taxi driver informs them he does not take credit cards and only cash is accepted. Neither Tom nor Danny have enough cash, they both cannot get into the vehicle. Tom waits outside in the queue and Danny goes to ATM to fetch the cash. During this time the taxi cannot wait, they have to let other passengers get into the taxi. As Tom and Danny both are outside in the queue, this is the Query Wait Time and they are in the suspended state. They cannot do anything till they get the cash. Once Danny gets the cash, they are both standing in the line again, creating one more Single Wait Time. This time when their turn comes they can pay the taxi driver in cash and reach their destination. The time taken for the taxi to get from the mall to the destination is running time (CPU time) and the taxi is running. I hope this analogy is bit clear with the wait stats. You can check the single wait stats using following query of Glenn Berry. -- Signal Waits for instance SELECT CAST(100.0 * SUM(signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%signal (cpu) waits], CAST(100.0 * SUM(wait_time_ms - signal_wait_time_ms) / SUM (wait_time_ms) AS NUMERIC(20,2)) AS [%resource waits] FROM sys.dm_os_wait_stats OPTION (RECOMPILE); Higher the single wait stats are not good for the system. Very high value indicates CPU pressure. In my experience, when systems are running smooth and without any glitch the single wait stat is lower than 20%. Again, this number can be debated (and it is from my experience and is not documented anywhere). In other words, lower is better and higher is not good for the system. In future articles we will discuss in detail the various wait types and wait stats and their resolution. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Query returns too few rows

    - by Tareq
    setup: mysql> create table product_stock( product_id integer, qty integer, branch_id integer); Query OK, 0 rows affected (0.17 sec) mysql> create table product( product_id integer, product_name varchar(255)); Query OK, 0 rows affected (0.11 sec) mysql> insert into product(product_id, product_name) values(1, 'Apsana White DX Pencil'); Query OK, 1 row affected (0.05 sec) mysql> insert into product(product_id, product_name) values(2, 'Diamond Glass Marking Pencil'); Query OK, 1 row affected (0.03 sec) mysql> insert into product(product_id, product_name) values(3, 'Apsana Black Pencil'); Query OK, 1 row affected (0.03 sec) mysql> insert into product_stock(product_id, qty, branch_id) values(1, 100, 1); Query OK, 1 row affected (0.03 sec) mysql> insert into product_stock(product_id, qty, branch_id) values(1, 50, 2); Query OK, 1 row affected (0.03 sec) mysql> insert into product_stock(product_id, qty, branch_id) values(2, 80, 1); Query OK, 1 row affected (0.03 sec) my query: mysql> SELECT IFNULL(SUM(s.qty),0) AS stock, product_name FROM product_stock s RIGHT JOIN product p ON s.product_id=p.product_id WHERE branch_id=1 GROUP BY product_name ORDER BY product_name; returns: +-------+-------------------------------+ | stock | product_name | +-------+-------------------------------+ | 100 | Apsana White DX Pencil | | 80 | Diamond Glass Marking Pencil | +-------+-------------------------------+ 1 row in set (0.00 sec) But I want to have the following result: +-------+------------------------------+ | stock | product_name | +-------+------------------------------+ | 0 | Apsana Black Pencil | | 100 | Apsana White DX Pencil | | 80 | Diamond Glass Marking Pencil | +-------+------------------------------+ To get this result what mysql query should I run?

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  • How do I filter one of the columns in a SQL Server SQL Query

    - by Kent S. Clarkson
    I have a table (that relates to a number of other tables) where I would like to filter ONE of the columns (RequesterID) - that column will be a combobox where only people that are not sales people should be selectable. Here is the "unfiltered" query, lets call it QUERY 1: SELECT RequestsID, RequesterID, ProductsID FROM dbo.Requests If using a separate query, lets call it QUERY 2, to filter RequesterID (which is a People related column, connected to People.PeopleID), it would look like this: SELECT People.PeopleID FROM People INNER JOIN Roles ON People.RolesID = Roles.RolesID INNER JOIN Requests ON People.PeopleID = Requests.RequesterID WHERE (Roles.Role <> N'SalesGuy') ORDER BY Requests.RequestsID Now, is there a way of "merging" the QUERY 2 into QUERY 1? (dbo.Requests in QUERY 1 has RequesterID populated as a Foreign Key from dbo.People, so no problem there... The connections are all right, just not know how to write the SQL query!)

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  • Learning AngularJS by Example – The Customer Manager Application

    - by dwahlin
    I’m always tinkering around with different ideas and toward the beginning of 2013 decided to build a sample application using AngularJS that I call Customer Manager. It’s not exactly the most creative name or concept, but I wanted to build something that highlighted a lot of the different features offered by AngularJS and how they could be used together to build a full-featured app. One of the goals of the application was to ensure that it was approachable by people new to Angular since I’ve never found overly complex applications great for learning new concepts. The application initially started out small and was used in my AngularJS in 60-ish Minutes video on YouTube but has gradually had more and more features added to it and will continue to be enhanced over time. It’ll be used in a new “end-to-end” training course my company is working on for AngularjS as well as in some video courses that will be coming out. Here’s a quick look at what the application home page looks like: In this post I’m going to provide an overview about how the application is organized, back-end options that are available, and some of the features it demonstrates. I’ve already written about some of the features so if you’re interested check out the following posts: Building an AngularJS Modal Service Building a Custom AngularJS Unique Value Directive Using an AngularJS Factory to Interact with a RESTful Service Application Structure The structure of the application is shown to the right. The  homepage is index.html and is located at the root of the application folder. It defines where application views will be loaded using the ng-view directive and includes script references to AngularJS, AngularJS routing and animation scripts, plus a few others located in the Scripts folder and to custom application scripts located in the app folder. The app folder contains all of the key scripts used in the application. There are several techniques that can be used for organizing script files but after experimenting with several of them I decided that I prefer things in folders such as controllers, views, services, etc. Doing that helps me find things a lot faster and allows me to categorize files (such as controllers) by functionality. My recommendation is to go with whatever works best for you. Anyone who says, “You’re doing it wrong!” should be ignored. Contrary to what some people think, there is no “one right way” to organize scripts and other files. As long as the scripts make it down to the client properly (you’ll likely minify and concatenate them anyway to reduce bandwidth and minimize HTTP calls), the way you organize them is completely up to you. Here’s what I ended up doing for this application: Animation code for some custom animations is located in the animations folder. In addition to AngularJS animations (which are defined using CSS in Content/animations.css), it also animates the initial customer data load using a 3rd party script called GreenSock. Controllers are located in the controllers folder. Some of the controllers are placed in subfolders based upon the their functionality while others are placed at the root of the controllers folder since they’re more generic:   The directives folder contains the custom directives created for the application. The filters folder contains the custom filters created for the application that filter city/state and product information. The partials folder contains partial views. This includes things like modal dialogs used in the application. The services folder contains AngularJS factories and services used for various purposes in the application. Most of the scripts in this folder provide data functionality. The views folder contains the different views used in the application. Like the controllers folder, the views are organized into subfolders based on their functionality:   Back-End Services The Customer Manager application (grab it from Github) provides two different options on the back-end including ASP.NET Web API and Node.js. The ASP.NET Web API back-end uses Entity Framework for data access and stores data in SQL Server (LocalDb). The other option on the back-end is Node.js, Express, and MongoDB.   Using the ASP.NET Web API Back-End To run the application using ASP.NET Web API/SQL Server back-end open the .sln file at the root of the project in Visual Studio 2012 or higher (the free Express 2013 for Web version is fine). Press F5 and a browser will automatically launch and display the application. Using the Node.js Back-End To run the application using the Node.js/MongoDB back-end follow these steps: In the CustomerManager directory execute 'npm install' to install Express, MongoDB and Mongoose (package.json). Load sample data into MongoDB by performing the following steps: Execute 'mongod' to start the MongoDB daemon Navigate to the CustomerManager directory (the one that has initMongoCustData.js in it) then execute 'mongo' to start the MongoDB shell Enter the following in the mongo shell to load the seed files that handle seeding the database with initial data: use custmgr load("initMongoCustData.js") load("initMongoSettingsData.js") load("initMongoStateData.js") Start the Node/Express server by navigating to the CustomerManager/server directory and executing 'node app.js' View the application at http://localhost:3000 in your browser. Key Features The Customer Manager application certainly doesn’t cover every feature provided by AngularJS (as mentioned the intent was to keep it as simple as possible) but does provide insight into several key areas: Using factories and services as re-useable data services (see the app/services folder) Creating custom directives (see the app/directives folder) Custom paging (see app/views/customers/customers.html and app/controllers/customers/customersController.js) Custom filters (see app/filters) Showing custom modal dialogs with a re-useable service (see app/services/modalService.js) Making Ajax calls using a factory (see app/services/customersService.js) Using Breeze to retrieve and work with data (see app/services/customersBreezeService.js). Switch the application to use the Breeze factory by opening app/services.config.js and changing the useBreeze property to true. Intercepting HTTP requests to display a custom overlay during Ajax calls (see app/directives/wcOverlay.js) Custom animations using the GreenSock library (see app/animations/listAnimations.js) Creating custom AngularJS animations using CSS (see Content/animations.css) JavaScript patterns for defining controllers, services/factories, directives, filters, and more (see any JavaScript file in the app folder) Card View and List View display of data (see app/views/customers/customers.html and app/controllers/customers/customersController.js) Using AngularJS validation functionality (see app/views/customerEdit.html, app/controllers/customerEditController.js, and app/directives/wcUnique.js) More… Conclusion I’ll be enhancing the application even more over time and welcome contributions as well. Tony Quinn contributed the initial Node.js/MongoDB code which is very cool to have as a back-end option. Access the standard application here and a version that has custom routing in it here. Additional information about the custom routing can be found in this post.

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  • How to know whether mongodb is running on 64 bit mode or 32 bit mode

    - by Jim Thio
    My programmer install mongodb. Then somehow it doesn't work. I run C:\mongod\bin>mongod mongod --help for help and startup options Sat Aug 11 22:57:50 Sat Aug 11 22:57:50 warning: 32-bit servers don't have journaling enabled by def ault. Please use --journal if you want durability. Sat Aug 11 22:57:50 Sat Aug 11 22:57:50 [initandlisten] MongoDB starting : pid=3800 port=27017 dbpat h=/data/db 32-bit host=haryantoi5 Sat Aug 11 22:57:50 [initandlisten] Sat Aug 11 22:57:50 [initandlisten] ** NOTE: when using MongoDB 32 bit, you are limited to about 2 gigabytes of data Sat Aug 11 22:57:50 [initandlisten] ** see http://blog.mongodb.org/post/13 7788967/32-bit-limitations Sat Aug 11 22:57:50 [initandlisten] ** with --journal, the limit is lower Sat Aug 11 22:57:50 [initandlisten] Sat Aug 11 22:57:50 [initandlisten] db version v2.0.7-rc1, pdfile version 4.5 Sat Aug 11 22:57:50 [initandlisten] git version: 9efe4cce272373b52b96de1309c1fbf 0c984305f Sat Aug 11 22:57:50 [initandlisten] build info: windows sys.getwindowsversion(ma jor=6, minor=0, build=6002, platform=2, service_pack='Service Pack 2') BOOST_LIB _VERSION=1_42 Sat Aug 11 22:57:50 [initandlisten] options: {} ************** Unclean shutdown detected. Please visit http://dochub.mongodb.org/core/repair for recovery instructions. ************* Sat Aug 11 22:57:50 [initandlisten] exception in initAndListen: 12596 old lock f ile, terminating Sat Aug 11 22:57:50 dbexit: Sat Aug 11 22:57:50 [initandlisten] shutdown: going to close listening sockets.. . Sat Aug 11 22:57:50 [initandlisten] shutdown: going to flush diaglog... Sat Aug 11 22:57:50 [initandlisten] shutdown: going to close sockets... Sat Aug 11 22:57:50 [initandlisten] shutdown: waiting for fs preallocator... Sat Aug 11 22:57:50 [initandlisten] shutdown: closing all files... Sat Aug 11 22:57:50 [initandlisten] closeAllFiles() finished Sat Aug 11 22:57:50 dbexit: really exiting now It seems that mongod is running on 32 bit. I have a 64 bit computer and I want to run mongodb in 64 bit enviroment. How do I do so?

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  • MySQL slow query log logging all queries

    - by Blanka
    We have a MySQL 5.1.52 Percona Server 11.6 instance that suddenly started logging every single query to the slow query log. The long_query_time configuration is set to 1, yet, suddenly we're seeing every single query (e.g. just saw one that took 0.000563s!). As a result, our log files are growing at an insane pace. We just had to truncate a 180G slow query log file. I tried setting the long_query_time variable to a really large number to see if it stopped altogether (1000000), but same result. show global variables like 'general_log%'; +------------------+--------------------------+ | Variable_name | Value | +------------------+--------------------------+ | general_log | OFF | | general_log_file | /usr2/mysql/data/db4.log | +------------------+--------------------------+ 2 rows in set (0.00 sec) show global variables like 'slow_query_log%'; +---------------------------------------+-------------------------------+ | Variable_name | Value | +---------------------------------------+-------------------------------+ | slow_query_log | ON | | slow_query_log_file | /usr2/mysql/data/db4-slow.log | | slow_query_log_microseconds_timestamp | OFF | +---------------------------------------+-------------------------------+ 3 rows in set (0.00 sec) show global variables like 'long%'; +-----------------+----------+ | Variable_name | Value | +-----------------+----------+ | long_query_time | 1.000000 | +-----------------+----------+ 1 row in set (0.00 sec)

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  • JPA and NoSQL using EclipseLink - MongoDB supported

    - by arungupta
    EclipseLink 2.4 has added JPA support for NoSQL databases, MongoDB and Oracle NoSQL are the first ones to make the cut. The support to other NoSQL database can be extended by adding a EclipseLink EISPlatform class and a JCA adapter. A Java class can be mapped to a NoSQL datasource using the @NoSQL annotation or <no-sql> XML element. Even a subset of JPQL and the Criteria API are supported, dependent on the NoSQL database's query support. The connection properties are specified in "persistence.xml". A complete sample showing how JPA annotations are mapping and using @NoSQL is explained here. The MongoDB-version of the source code can also be checked out from the SVN repository. EclipseLink 2.4 is scheduled to be released with Eclipse Juno in June 2012 and the complete set of supported features is described on their wiki. The milestone and nightly builds are already available. Do you want to try with GlassFish and let us know ?

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  • Random MongoDb Syntax: Updates

    - by Liam McLennan
    I have a MongoDb collection called tweets. Each document has a property system_classification. If the value of system_classification is ‘+’ I want to change it to ‘positive’. For a regular relational database the query would be: update tweets set system_classification = 'positive' where system_classification = '+' the MongoDb equivalent is: db.tweets.update({system_classification: '+'}, {$set: {system_classification:'positive'}}, false, true) Parameter Description { system_classification: '+' } the first parameter identifies the documents to select { $set: { system_classification: 'positive' } } the second parameter is an operation ($set) and the parameter to that operation {system_classification: ‘positive’} false the third parameter indicates if this is a regular update or an upsert (true for upsert) true the final parameter indicates if the operation should be applied to all selected documents (or just the first)

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  • How can I optimize this subqueried and Joined MySQL Query?

    - by kevzettler
    I'm pretty green on mysql and I need some tips on cleaning up a query. It is used in several variations through out a site. Its got some subquerys derived tables and fun going on. Heres the query: # Query_time: 2 Lock_time: 0 Rows_sent: 0 Rows_examined: 0 SELECT * FROM ( SELECT products . *, categories.category_name AS category, ( SELECT COUNT( * ) FROM distros WHERE distros.product_id = products.product_id) AS distro_count, (SELECT COUNT(*) FROM downloads WHERE downloads.product_id = products.product_id AND WEEK(downloads.date) = WEEK(curdate())) AS true_downloads, (SELECT COUNT(*) FROM views WHERE views.product_id = products.product_id AND WEEK(views.date) = WEEK(curdate())) AS true_views FROM products INNER JOIN categories ON products.category_id = categories.category_id ORDER BY created_date DESC, true_views DESC ) AS count_table WHERE count_table.distro_count > 0 AND count_table.status = 'published' AND count_table.active = 1 LIMIT 0, 8 Heres the explain: +----+--------------------+------------+-------+---------------+-------------+---------+------------------------------------+------+----------------------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+--------------------+------------+-------+---------------+-------------+---------+------------------------------------+------+----------------------------------------------+ | 1 | PRIMARY | <derived2> | ALL | NULL | NULL | NULL | NULL | 232 | Using where | | 2 | DERIVED | categories | index | PRIMARY | idx_name | 47 | NULL | 13 | Using index; Using temporary; Using filesort | | 2 | DERIVED | products | ref | category_id | category_id | 4 | digizald_db.categories.category_id | 9 | | | 5 | DEPENDENT SUBQUERY | views | ref | product_id | product_id | 4 | digizald_db.products.product_id | 46 | Using where | | 4 | DEPENDENT SUBQUERY | downloads | ref | product_id | product_id | 4 | digizald_db.products.product_id | 14 | Using where | | 3 | DEPENDENT SUBQUERY | distros | ref | product_id | product_id | 4 | digizald_db.products.product_id | 1 | Using index | +----+--------------------+------------+-------+---------------+-------------+---------+------------------------------------+------+----------------------------------------------+ 6 rows in set (0.04 sec) And the Tables: mysql> describe products; +---------------+--------------------------------------------------+------+-----+-------------------+----------------+ | Field | Type | Null | Key | Default | Extra | +---------------+--------------------------------------------------+------+-----+-------------------+----------------+ | product_id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_key | char(32) | NO | | NULL | | | title | varchar(150) | NO | | NULL | | | company | varchar(150) | NO | | NULL | | | user_id | int(10) unsigned | NO | MUL | NULL | | | description | text | NO | | NULL | | | video_code | text | NO | | NULL | | | category_id | int(10) unsigned | NO | MUL | NULL | | | price | decimal(10,2) | NO | | NULL | | | quantity | int(10) unsigned | NO | | NULL | | | downloads | int(10) unsigned | NO | | NULL | | | views | int(10) unsigned | NO | | NULL | | | status | enum('pending','published','rejected','removed') | NO | | NULL | | | active | tinyint(1) | NO | | NULL | | | deleted | tinyint(1) | NO | | NULL | | | created_date | datetime | NO | | NULL | | | modified_date | timestamp | NO | | CURRENT_TIMESTAMP | | | scrape_source | varchar(215) | YES | | NULL | | +---------------+--------------------------------------------------+------+-----+-------------------+----------------+ 18 rows in set (0.00 sec) mysql> describe categories -> ; +------------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------------+------------------+------+-----+---------+----------------+ | category_id | int(10) unsigned | NO | PRI | NULL | auto_increment | | category_name | varchar(45) | NO | MUL | NULL | | | parent_id | int(10) unsigned | YES | MUL | NULL | | | category_type_id | int(10) unsigned | NO | | NULL | | +------------------+------------------+------+-----+---------+----------------+ 4 rows in set (0.00 sec) mysql> describe compatibilities -> ; +------------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------------+------------------+------+-----+---------+----------------+ | compatibility_id | int(10) unsigned | NO | PRI | NULL | auto_increment | | name | varchar(45) | NO | | NULL | | | code_name | varchar(45) | NO | | NULL | | | description | varchar(128) | NO | | NULL | | | position | int(10) unsigned | NO | | NULL | | +------------------+------------------+------+-----+---------+----------------+ 5 rows in set (0.01 sec) mysql> describe distros -> ; +------------------+--------------------------------------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------------+--------------------------------------------------+------+-----+---------+----------------+ | id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_id | int(10) unsigned | NO | MUL | NULL | | | compatibility_id | int(10) unsigned | NO | MUL | NULL | | | user_id | int(10) unsigned | NO | | NULL | | | status | enum('pending','published','rejected','removed') | NO | | NULL | | | distro_type | enum('file','url') | NO | | NULL | | | version | varchar(150) | NO | | NULL | | | filename | varchar(50) | YES | | NULL | | | url | varchar(250) | YES | | NULL | | | virus | enum('READY','PASS','FAIL') | YES | | NULL | | | downloads | int(10) unsigned | NO | | 0 | | +------------------+--------------------------------------------------+------+-----+---------+----------------+ 11 rows in set (0.01 sec) mysql> describe downloads; +------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+------------------+------+-----+---------+----------------+ | id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_id | int(10) unsigned | NO | MUL | NULL | | | distro_id | int(10) unsigned | NO | MUL | NULL | | | user_id | int(10) unsigned | NO | MUL | NULL | | | ip_address | varchar(15) | NO | | NULL | | | date | datetime | NO | | NULL | | +------------+------------------+------+-----+---------+----------------+ 6 rows in set (0.01 sec) mysql> describe views -> ; +------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+------------------+------+-----+---------+----------------+ | id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_id | int(10) unsigned | NO | MUL | NULL | | | user_id | int(10) unsigned | NO | MUL | NULL | | | ip_address | varchar(15) | NO | | NULL | | | date | datetime | NO | | NULL | | +------------+------------------+------+-----+---------+----------------+ 5 rows in set (0.00 sec)

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  • SQL query: Delete a entry which is not present in a join table?

    - by Mestika
    Hi, I’m going to delete all users which has no subscription but I seem to run into problems each time I try to detect the users. My schemas look like this: Users = {userid, name} Subscriptionoffering = {userid, subscriptionname} Now, what I’m going to do is to delete all users in the user table there has a count of zero in the subscriptionoffering table. Or said in other words: All users which userid is not present in the subscriptionoffering table. I’ve tried with different queries but with no result. I’ve tried to say where user.userid <> subscriptionoffering.userid, but that doesn’t seem to work. Do anyone know how to create the correct query? Thanks Mestika

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  • Mongo: Finding from multiple queries

    - by waxical
    New to Mongo here. I'm using the PHP lib and trying to work out how I can find in a collection from multiple queries. I could do this by repeating the query with a different query, but I wondered if it can be done in one. I.e. $idsToLookFor = array(2124,4241,5553); $query = $db->thisCollection->find(array('id' => $idsToLookFor)); That's what I'd like to do. However it doesn't work. What I'm trying to do is find a set of results for all the id's at one time. Possible or just do a findOne on each with a foreach/for?

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  • Slow MySQL Query not using filesort

    - by Canadaka
    I have a query on my homepage that is getting slower and slower as my database table grows larger. tablename = tweets_cache rows = 572,327 this is the query I'm currently using that is slow, over 5 seconds. SELECT * FROM tweets_cache t WHERE t.province='' AND t.mp='0' ORDER BY t.published DESC LIMIT 50; If I take out either the WHERE or the ORDER BY, then the query is super fast 0.016 seconds. I have the following indexes on the tweets_cache table. PRIMARY published mp category province author So i'm not sure why its not using the indexes since mp, provice and published all have indexes? Doing a profile of the query shows that its not using an index to sort the query and is using filesort which is really slow. possible_keys = mp,province Extra = Using where; Using filesort I tried adding a new multie-colum index with "profiles & mp". The explain shows that this new index listed under "possible_keys" and "key", but the query time is unchanged, still over 5 seconds. Here is a screenshot of the profiler info on the query. http://i355.photobucket.com/albums/r469/canadaka_bucket/slow_query_profile.png Something weird, I made a dump of my database to test on my local desktop so i don't screw up the live site. The same query on my local runs super fast, milliseconds. So I copied all the same mysql startup variables from the server to my local to make sure there wasn't some setting that might be causing this. But even after that the local query runs super fast, but the one on the live server is over 5 seconds. My database server is only using around 800MB of the 4GB it has available. here are the related my.ini settings i'm using default-storage-engine = MYISAM max_connections = 800 skip-locking key_buffer = 512M max_allowed_packet = 1M table_cache = 512 sort_buffer_size = 4M read_buffer_size = 4M read_rnd_buffer_size = 16M myisam_sort_buffer_size = 64M thread_cache_size = 8 query_cache_size = 128M # Try number of CPU's*2 for thread_concurrency thread_concurrency = 8 # Disable Federated by default skip-federated key_buffer = 512M sort_buffer_size = 256M read_buffer = 2M write_buffer = 2M key_buffer = 512M sort_buffer_size = 256M read_buffer = 2M write_buffer = 2M

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  • nodejs, mongodb - How do I operate on data from multiple queries?

    - by Ryan
    Hi all, I'm new to JS in general, but I am trying to query some data from MongoDB. Basically, my first query retrieves information for the session with the specified session id. The second query does a simple geospacial query for documents with a location near the specified location. I'm using the mongodb-native javascript driver. All of these query methods return their results in callbacks, so they're non-blocking. This is the root of my troubles. What I'm needing to do is retrieve the results of the second query, and create an Array of sessionIds of all the returned documents. Then I'm going to pass those to a function later. But, I can't generate this array and use it anywhere outside the callback. Does anyone have any idea how to properly do this? http://dpaste.org/wXiE/

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  • Is there anything else I can do to optimize this MySQL query?

    - by Legend
    I have two tables, Table A with 700,000 entries and Table B with 600,000 entries. The structure is as follows: Table A: +-----------+---------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +-----------+---------------------+------+-----+---------+----------------+ | id | bigint(20) unsigned | NO | PRI | NULL | auto_increment | | number | bigint(20) unsigned | YES | | NULL | | +-----------+---------------------+------+-----+---------+----------------+ Table B: +-------------+---------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +-------------+---------------------+------+-----+---------+----------------+ | id | bigint(20) unsigned | NO | PRI | NULL | auto_increment | | number_s | bigint(20) unsigned | YES | MUL | NULL | | | number_e | bigint(20) unsigned | YES | MUL | NULL | | | source | varchar(50) | YES | | NULL | | +-------------+---------------------+------+-----+---------+----------------+ I am trying to find if any of the values in Table A are present in Table B using the following code: $sql = "SELECT number from TableA"; $result = mysql_query($sql) or die(mysql_error()); while($row = mysql_fetch_assoc($result)) { $number = $row['number']; $sql = "SELECT source, count(source) FROM TableB WHERE number_s < $number AND number_e > $number GROUP BY source"; $re = mysql_query($sql) or die(mysql_error); while($ro = mysql_fetch_array($re)) { echo $number."\t".$ro[0]."\t".$ro[1]."\n"; } } I was hoping that the query would go fast but then for some reason, it isn't terrible fast. My explain on the select (with a particular value of "number") gives me the following: mysql> explain SELECT source, count(source) FROM TableB WHERE number_s < 1812194440 AND number_e > 1812194440 GROUP BY source; +----+-------------+------------+------+-------------------------+------+---------+------+--------+----------------------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+------------+------+-------------------------+------+---------+------+--------+----------------------------------------------+ | 1 | SIMPLE | TableB | ALL | number_s,number_e | NULL | NULL | NULL | 696325 | Using where; Using temporary; Using filesort | +----+-------------+------------+------+-------------------------+------+---------+------+--------+----------------------------------------------+ 1 row in set (0.00 sec) Is there any optimization that I can squeeze out of this? I tried writing a stored procedure for the same task but it doesn't even seem to work in the first place... It doesn't give any syntax errors... I tried running it for a day and it was still running which felt odd. CREATE PROCEDURE Filter() Begin DECLARE number BIGINT UNSIGNED; DECLARE x INT; DECLARE done INT DEFAULT 0; DECLARE cur1 CURSOR FOR SELECT number FROM TableA; DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = 1; CREATE TEMPORARY TABLE IF NOT EXISTS Flags(number bigint unsigned, count int(11)); OPEN cur1; hist_loop: LOOP FETCH cur1 INTO number; SELECT count(*) from TableB WHERE number_s < number AND number_e > number INTO x; IF done = 1 THEN LEAVE hist_loop; END IF; IF x IS NOT NULL AND x>0 THEN INSERT INTO Flags(number, count) VALUES(number, x); END IF; END LOOP hist_loop; CLOSE cur1; END

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  • Linq-to-sql Compiled Query is returning result from different DataContext

    - by Vladimir Kojic
    Compiled query: public static Func<OperationalDataContext, short, Machine> QueryMachineById = CompiledQuery.Compile((OperationalDataContext db, short machineID) => db.Machines.Where(m => m.MachineID == machineID).SingleOrDefault()); It looks like compiled query is caching Machine object and returning the same object even if query is called from new DataContext (I’m disposing DataContext in the service but I’m getting Machine from previous DataContext). I use POCOs and XML mapping. Revised: It looks like compiled query is returning result from new data context and it is not using the one that I passed in compiled-query. Therefore I can not reuse returned object and link it to another object obtained from datacontext thru non compiled queries. I’m using unit of work pattern : // First Call Using(new DataContext) { Machine from DataContext.Table == machine from cached query } // Do some work // Second Call is failing Using(new DataContext) { Machine from DataContext.Table <> machine from cached query }

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  • Using MongoDB + Redis + Apache on the same server in production?

    - by Dayson
    I intend to launch my web app using a 8 GB VPS. It uses MongoDB + Redis for storage/caching and Apache + PHP-FPM for serving requests. Could there be any issues with running Mongo + Redis + Apache on the same server? Would it make more sense to setup 2 x 4 GB VPS servers and keep Mongo on one and Redis + Apache on another? Should I just start with one server and worry about scaling horizontally later by delegating the existing server to Mongo in the future (due to its large RAM) and moving the web servers on to multiple smaller VPS'?

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