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  • How do I do MongoDB console-style queries in PHP?

    - by Zoe Boles
    I'm trying to get a MongoDB query from the javascript console into my PHP app. What I'm trying to avoid is having to translate the query into the PHP "native driver"'s format... I don't want to hand build arrays and hand-chain functions any more than I want to manually build an array of MySQL's internal query structure just to get data. I already have a string producing the exact content I want in the Mongo console: db.intake.find({"processed": {"$exists": "false"}}).sort({"insert_date": "1"}).limit(10); The question is, is there a way for me to hand this string, as is, to MongoDB and have it return a cursor with the dataset I request? Right now I'm at the "write your own parser because it's not valid json to kinda turn a subset of valid Mongo queries into the format the PHP native driver wants" state, which isn't very fun. I don't want an ORM or a massive wrapper library; I just want to give a function my query string as it exists in the console and get an Iterator back that I can work with. I know there are a couple of PHP-based Mongo manager applications that apparently take console-style queries and handle them, but initial browsing through their code, I'm not sure how they handle the translation. I absolutely love working with mongo in the console, but I'm rapidly starting to loathe the thought of converting every query into the format the native writer wants...

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  • Run a MongoDB configuration server without 3GB of journal files

    - by Thilo
    For a production sharded MongoDB installation we need 3 configuration servers. According to the documentation "the config server mongod process is fairly lightweight and can be ran on machines performing other work". However, in the default configuration, they all have journalling enabled, and with preallocation this takes up 3 GB of disk space. I assume that the actual data and transaction volume of a config server is quite small, so that this seems a bit too much. Is there a way to (safely!) run these config servers with much less disk use for the journal? Do I need journalling at all on config servers? Can I set the journal size to be smaller?

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  • MongoDB ReplicaSet Elections when some nodes are down

    - by SecondThought
    I'm trying to get into ReplicaSet concept, and found something weird in mongoDB Documentation: For a node to be elected primary, it must receive a majority of votes. This is a majority of all votes in the set: if you have a 5-member set and 4 members are down, a majority of the set is still 3 members (floor(5/2)+1). Each member of the set receives a single vote and knows the total number of available votes. If no node can reach a majority, then no primary can be elected and no data can be written to that replica set (although reads to secondaries are still possible). (taken from here) So, If I got that right, in the 5-member case mentioned there the one node that's still standing WILL NOT be chosen as primary and the whole set will not get any writes? and that's even if this single node was the last primary before the elections? If it's true there can be many less-radical cases which will end up with a degenerated set. How can we avoid this?

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  • Login authentication vanished from MongoDB install

    - by Robert Oschler
    A few months ago I enabled password protection on my MongoDB install. Today I ran the Mongo client and forgot to use my login details. Instead of rejecting nearly everything I try to do from the shell, like it should, I had complete access to all the databases and collections. Fortunately this instance is only running a few test apps, so I quickly shutdown the MongoD instance until I figure this out. Has anybody ever seen this kind of behavior before and knows what is going on? The MongoD instance is running on a Linux VM hosted by Azure. The only thing I can think of is that perhaps Azure restored an old copy of the VM, but I received no E-mails to that effect and everything else on the server seems to be proper, including new daemon processes that I added after I enabled password protection on MongoD.

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  • Is this a valid backup strategy for MongoDB?

    - by James Simpson
    I've got a single dedicated server with a MongoDB database of around 10GB. I need to do daily backups, but I can't have downtime with the database. Is it possible to use a replica set on a single disk (with 2 instances of mongod running on different ports), and simply take the secondary one offline and backup the data files to an offsite storage such as S3 (journaling is turned on)? Or would using master/slave be better than a replica set? Is this viable, and if so, what potential problems could I have? If not, how do I conceptualize this to work?

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  • MongoDB PHP EC2 Setup Configuration

    - by nathansizemore
    I am new to web development and server set up. I am looking for some advice or a link to a tutorial on setting up a production system up. Right now, I have a server (Ubuntu, Apache, MongoDB, and PHP). It receives a request, PHP queries Mongo, and PHP sends out the requested data. How do I make that work with more servers? I've read that you can make a cluster of a primary and two slave nodes which work as separate servers running Mongo, but do those also run PHP? Or is the primary the only one running the PHP? I have read some docs on Mongo site and a video of someone from 10gen going through it, but they are geared towards people that seem to already understand this stuff, I have no idea and need to start from a beginning stage. If anyone can help me understand where PHP (Acting as my API) lives in these clusters, that would be greatly appreciated! Thanks in advance for any help!

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  • mongodb eating 48G in 1min

    - by ledy
    In mongodb i work with this collection: Size 55.93g Data Size 39.82g Storage Size 41.08g Extents 53 Indexes 4 Index Size 9.64g It takes few seconds of mongdb being up with this single collection and all 48GB RAM on the dedicated server are gone. That's worse because there is also a mysqld+nginx/fcgi on this machine which should be allowed to use at least 24GB together. I.e. remaining 24GB, enough for the mongod! However, it does not share in a fair way. Everybody says that the memory for mongod is managed by OS and releases unneccessary space for other processes if they demand RAM. On my machine it is not releasing RAM. What's wrong? free total used free shared buffers cached` Mem: 49559136 49403908 155228 0 57284 47247564 -/+ buffers/cache: 2099060 47460076 Swap: 8008392 164 8008228

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  • mongodb segmentation fault(11) macosx

    - by Wish
    I have problem, i cant figure out, how to fix.. So i am on MacOSX machine, running php 5.3.15 version, using mongo 1.3.1 version. When i try to execute php script, in which i try to connect to remote mongodb server, I get segmentation fault(11).. I installed php driver with sudo pecl install mongo I have seen, that this problem is quite popular, but havent found real solution yet.. I dont know if I am asking this question in correct stack site.. If you need anything else, just ask.

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  • Enforcing a query in MySql to use a specific index

    - by Hossein
    Hi, I have large table. consisting of only 3 columns (id(INT),bookmarkID(INT),tagID(INT)).I have two BTREE indexes one for each bookmarkID and tagID columns.This table has about 21 Million records. I am trying to run this query: SELECT bookmarkID,COUNT(bookmarkID) AS count FROM bookmark_tag_map GROUP BY tagID,bookmarkID HAVING tagID IN (-----"tagIDList"-----) AND count >= N which takes ages to return the results.I read somewhere that if make an index in which it has tagID,bookmarkID together, i will get a much faster result. I created the index after some time. Tried the query again, but it seems that this query is not using the new index that I have made.I ran EXPLAIN and saw that it is actually true. My question now is that how I can enforce a query to use a specific index? also comments on other ways to make the query faster are welcome. Thanks

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  • Mongodb: why is my mongo server using two PID's?

    - by Lucas
    I started my mongo with the following command: [lucas@ecoinstance]~/node/nodetest2$ sudo mongod --dbpath /home/lucas/node/nodetest2/data 2014-06-07T08:46:30.507+0000 [initandlisten] MongoDB starting : pid=6409 port=27017 dbpat h=/home/lucas/node/nodetest2/data 64-bit host=ecoinstance 2014-06-07T08:46:30.508+0000 [initandlisten] db version v2.6.1 2014-06-07T08:46:30.508+0000 [initandlisten] git version: 4b95b086d2374bdcfcdf2249272fb55 2c9c726e8 2014-06-07T08:46:30.508+0000 [initandlisten] build info: Linux build14.nj1.10gen.cc 2.6.3 2-431.3.1.el6.x86_64 #1 SMP Fri Jan 3 21:39:27 UTC 2014 x86_64 BOOST_LIB_VERSION=1_49 2014-06-07T08:46:30.509+0000 [initandlisten] allocator: tcmalloc 2014-06-07T08:46:30.509+0000 [initandlisten] options: { storage: { dbPath: "/home/lucas/n ode/nodetest2/data" } } 2014-06-07T08:46:30.520+0000 [initandlisten] journal dir=/home/lucas/node/nodetest2/data/ journal 2014-06-07T08:46:30.520+0000 [initandlisten] recover : no journal files present, no recov ery needed 2014-06-07T08:46:30.527+0000 [initandlisten] waiting for connections on port 27017 It appears to be working, as I can execute mongo and access the server. However, here are the process running mongo: [lucas@ecoinstance]~/node/testSite$ ps aux | grep mongo root 6540 0.0 0.2 33424 1664 pts/3 S+ 08:52 0:00 sudo mongod --dbpath /ho me/lucas/node/nodetest2/data root 6541 0.6 8.6 522140 52512 pts/3 Sl+ 08:52 0:00 mongod --dbpath /home/lu cas/node/nodetest2/data lucas 6554 0.0 0.1 7836 876 pts/4 S+ 08:52 0:00 grep mongo As you can see, there are two PID's for mongo. Before I ran sudo mongod --dbpath /home/lucas/node/nodetest2/data, there were none (besides the grep of course). How did my command spawn two PID's, and should I be concerned? Any suggestions or tips would be great. Additional Info In addition, I may have other issues that might suggest a cause. I tried running mongo with --fork --logpath /home/lucas..., but it did not work. More information below: [lucas@ecoinstance]~/node/nodetest2$ sudo mongod --dbpath /home/lucas/node/nodetest2/data --fork --logpath /home/lucas/node/nodetest2/data/ about to fork child process, waiting until server is ready for connections. forked process: 6578 ERROR: child process failed, exited with error number 1 [lucas@ecoinstance]~/node/nodetest2$ ls -l data/ total 163852 drwxr-xr-x 2 mongodb nogroup 4096 Jun 7 08:54 journal -rw------- 1 mongodb nogroup 67108864 Jun 7 08:52 local.0 -rw------- 1 mongodb nogroup 16777216 Jun 7 08:52 local.ns -rwxr-xr-x 1 mongodb nogroup 0 Jun 7 08:54 mongod.lock -rw------- 1 mongodb nogroup 67108864 Jun 7 02:08 nodetest1.0 -rw------- 1 mongodb nogroup 16777216 Jun 7 02:08 nodetest1.ns Also, my db path folder is not the original location. It was originally created under the default /var/lib/mongodb/ and moved to my local data folder. This was done after shutting down the server via /etc/init.d/mongod stop. I have a Debian Wheezy server, if it matters.

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  • Using MongoDB with Ruby On Rails and the Mongomapper plugin

    - by Micke
    Hello, i am currently trying to learn Ruby On Rails as i am a long-time PHP developer so i am building my own community like page. I have came pritty far and have made the user models and suchs using MySQL. But then i heard of MongoDB and looked in to it a little bit more and i find it kinda nice. So i have set it up and i am using mongomapper for the connection between rails and MongoDB. And i am now using it for the News page on the site. I also have a profile page for every User which includes their own guestbook so other users can come to their profile and write a little message to them. My thought now is to change the User models from using MySQL to start using MongoDB. I can start by showing how the models for each User is set up. The user model: class User < ActiveRecord::Base has_one :guestbook, :class_name => "User::Guestbook" The Guestbook model model: class User::Guestbook < ActiveRecord::Base belongs_to :user has_many :posts, :class_name => "User::Guestbook::Posts", :foreign_key => "user_id" And then the Guestbook posts model: class User::Guestbook::Posts < ActiveRecord::Base belongs_to :guestbook, :class_name => "User::Guestbook" I have divided it like this for my own convenience but now when i am going to try to migrate to MongoDB i dont know how to make the tables. I would like to have one table for each user and in that table a "column" for all the guestbook entries since MongoDB can have a EmbeddedDocument. I would like to do this so i just have one Table for each user and not like now when i have three tables just to be able to have a guestbook. So my thought is to have it like this: The user model: class User include MongoMapper::Document one :guestbook, :class_name => "User::Guestbook" The Guestbook model model: class User::Guestbook include MongoMapper::EmbeddedDocument belongs_to :user many :posts, :class_name => "User::Guestbook::Posts", :foreign_key => "user_id" And then the Guestbook posts model: class User::Guestbook::Posts include MongoMapper::EmbeddedDocument belongs_to :guestbook, :class_name => "User::Guestbook" But then i can think of one problem.. That when i just want to fetch the user information like a nickname and a birthdate then it will have to fetch all the users guestbook posts. And if each user has like a thousand posts in the guestbook it will get really much to fetch for the system. Or am i wrong? Do you think i should do it any other way? Thanks in advance and sorry if i am hard to understand but i am not so educated in the english language :)

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  • Replicated MongoDB server slower than simple shards

    - by displayName
    I tried to compare the performance of a sharded configuration against a sharded and replicated configuration. The sharded configuration consists of 8 shards each running on three different machines thereby constituting a total of 24 shards. All 8 of these shards run in the same partition on each machine. The sharded and replicated version is 8 shards again just like plain sharding, and all 8 mongods run on the same partition in each machine. But apart from this, each of these three machine now run additional 16 threads on another partition which serve as the secondary for the 8 mongods running on other machines. This is the way I prepared a sharded and replicated configuration with data chunks having replication factor of 3. Important point to note is that once the data has been loaded, it is not modified. So after primary and secondaries have synchronized then it doesn't matter which one i read from. To run the queries, I use an entirely different machine (let's call it config) which runs mongos and this machine's only purpose is to receive queries and run them on the cluster. Contrary to my expectations, plain sharding of 8 threads on each machine (total = 3 * 8 = 24) is performing better for queries than the sharded + replicated configuration. I have a script written to perform the query. So in order to time the scripts, I use time ./testScript and see the result. I tried changing the reading preference for replicated cluster by logging to mongo of config and run db.getMongo().setReadPref('secondary') and then exit the shell and run the queries like time ./testScript. The questions are: Where am i going wrong in the replication? Why is it slower than its plain sharding version? Does the db.getMongo().ReadPref('secondary') persist when i leave the shell and try to perform the query? All the four machines are running Linux and i have already increased the ulimit -n to 2048 from initial value of 1024 to allow more connections. The collections are properly distributed and all the mongods have equal number of chunks. Goes without saying that indices in both configurations are the same.

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  • php/mongodb: how does references work in php?

    - by harald
    hello, i asked this in the mongodb user-group, but was not satisfied with the answer, so -- maybe someone at stackoverflow can enlighten me: the question was: $b = array('x' => 1); $ref = &$b; $collection->insert($ref); var_dump($ref); $ref does not contain '_id', because it's a reference to $b, the handbook states. (the code snippet is taken from the php mongo documentation) i should add, that: $b = array('x' => 1); $ref = $b; $collection->insert($ref); var_dump($ref); in this case $ref contains the _id -- for those, who do not know, what the insert method of mongodb-php-driver does -- because $ref is passed by reference (note the $b with and without referencing '&'). on the other hand ... function test(&$data) { $data['_id'] = time(); } $b = array('x' => 1); $ref =& $b; test($ref); var_dump($ref); $ref contains _id, when i call a userland function. my question is: how does the references in these cases differ? my question is probably not mongodb specific -- i thought i would know how references in php work, but apparently i do not: the answer in the mongodb user-group was, that this was the way, how references in php work. so ... how do they work -- explained with these two code-snippets? thanks in advance!!!

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  • MERGE Bug with Filtered Indexes

    - by Paul White
    A MERGE statement can fail, and incorrectly report a unique key violation when: The target table uses a unique filtered index; and No key column of the filtered index is updated; and A column from the filtering condition is updated; and Transient key violations are possible Example Tables Say we have two tables, one that is the target of a MERGE statement, and another that contains updates to be applied to the target.  The target table contains three columns, an integer primary key, a single character alternate key, and a status code column.  A filtered unique index exists on the alternate key, but is only enforced where the status code is ‘a’: CREATE TABLE #Target ( pk integer NOT NULL, ak character(1) NOT NULL, status_code character(1) NOT NULL,   PRIMARY KEY (pk) );   CREATE UNIQUE INDEX uq1 ON #Target (ak) INCLUDE (status_code) WHERE status_code = 'a'; The changes table contains just an integer primary key (to identify the target row to change) and the new status code: CREATE TABLE #Changes ( pk integer NOT NULL, status_code character(1) NOT NULL,   PRIMARY KEY (pk) ); Sample Data The sample data for the example is: INSERT #Target (pk, ak, status_code) VALUES (1, 'A', 'a'), (2, 'B', 'a'), (3, 'C', 'a'), (4, 'A', 'd');   INSERT #Changes (pk, status_code) VALUES (1, 'd'), (4, 'a');          Target                     Changes +-----------------------+    +------------------+ ¦ pk ¦ ak ¦ status_code ¦    ¦ pk ¦ status_code ¦ ¦----+----+-------------¦    ¦----+-------------¦ ¦  1 ¦ A  ¦ a           ¦    ¦  1 ¦ d           ¦ ¦  2 ¦ B  ¦ a           ¦    ¦  4 ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦    +------------------+ ¦  4 ¦ A  ¦ d           ¦ +-----------------------+ The target table’s alternate key (ak) column is unique, for rows where status_code = ‘a’.  Applying the changes to the target will change row 1 from status ‘a’ to status ‘d’, and row 4 from status ‘d’ to status ‘a’.  The result of applying all the changes will still satisfy the filtered unique index, because the ‘A’ in row 1 will be deleted from the index and the ‘A’ in row 4 will be added. Merge Test One Let’s now execute a MERGE statement to apply the changes: MERGE #Target AS t USING #Changes AS c ON c.pk = t.pk WHEN MATCHED AND c.status_code <> t.status_code THEN UPDATE SET status_code = c.status_code; The MERGE changes the two target rows as expected.  The updated target table now contains: +-----------------------+ ¦ pk ¦ ak ¦ status_code ¦ ¦----+----+-------------¦ ¦  1 ¦ A  ¦ d           ¦ <—changed from ‘a’ ¦  2 ¦ B  ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦ ¦  4 ¦ A  ¦ a           ¦ <—changed from ‘d’ +-----------------------+ Merge Test Two Now let’s repopulate the changes table to reverse the updates we just performed: TRUNCATE TABLE #Changes;   INSERT #Changes (pk, status_code) VALUES (1, 'a'), (4, 'd'); This will change row 1 back to status ‘a’ and row 4 back to status ‘d’.  As a reminder, the current state of the tables is:          Target                        Changes +-----------------------+    +------------------+ ¦ pk ¦ ak ¦ status_code ¦    ¦ pk ¦ status_code ¦ ¦----+----+-------------¦    ¦----+-------------¦ ¦  1 ¦ A  ¦ d           ¦    ¦  1 ¦ a           ¦ ¦  2 ¦ B  ¦ a           ¦    ¦  4 ¦ d           ¦ ¦  3 ¦ C  ¦ a           ¦    +------------------+ ¦  4 ¦ A  ¦ a           ¦ +-----------------------+ We execute the same MERGE statement: MERGE #Target AS t USING #Changes AS c ON c.pk = t.pk WHEN MATCHED AND c.status_code <> t.status_code THEN UPDATE SET status_code = c.status_code; However this time we receive the following message: Msg 2601, Level 14, State 1, Line 1 Cannot insert duplicate key row in object 'dbo.#Target' with unique index 'uq1'. The duplicate key value is (A). The statement has been terminated. Applying the changes using UPDATE Let’s now rewrite the MERGE to use UPDATE instead: UPDATE t SET status_code = c.status_code FROM #Target AS t JOIN #Changes AS c ON t.pk = c.pk WHERE c.status_code <> t.status_code; This query succeeds where the MERGE failed.  The two rows are updated as expected: +-----------------------+ ¦ pk ¦ ak ¦ status_code ¦ ¦----+----+-------------¦ ¦  1 ¦ A  ¦ a           ¦ <—changed back to ‘a’ ¦  2 ¦ B  ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦ ¦  4 ¦ A  ¦ d           ¦ <—changed back to ‘d’ +-----------------------+ What went wrong with the MERGE? In this test, the MERGE query execution happens to apply the changes in the order of the ‘pk’ column. In test one, this was not a problem: row 1 is removed from the unique filtered index by changing status_code from ‘a’ to ‘d’ before row 4 is added.  At no point does the table contain two rows where ak = ‘A’ and status_code = ‘a’. In test two, however, the first change was to change row 1 from status ‘d’ to status ‘a’.  This change means there would be two rows in the filtered unique index where ak = ‘A’ (both row 1 and row 4 meet the index filtering criteria ‘status_code = a’). The storage engine does not allow the query processor to violate a unique key (unless IGNORE_DUP_KEY is ON, but that is a different story, and doesn’t apply to MERGE in any case).  This strict rule applies regardless of the fact that if all changes were applied, there would be no unique key violation (row 4 would eventually be changed from ‘a’ to ‘d’, removing it from the filtered unique index, and resolving the key violation). Why it went wrong The query optimizer usually detects when this sort of temporary uniqueness violation could occur, and builds a plan that avoids the issue.  I wrote about this a couple of years ago in my post Beware Sneaky Reads with Unique Indexes (you can read more about the details on pages 495-497 of Microsoft SQL Server 2008 Internals or in Craig Freedman’s blog post on maintaining unique indexes).  To summarize though, the optimizer introduces Split, Filter, Sort, and Collapse operators into the query plan to: Split each row update into delete followed by an inserts Filter out rows that would not change the index (due to the filter on the index, or a non-updating update) Sort the resulting stream by index key, with deletes before inserts Collapse delete/insert pairs on the same index key back into an update The effect of all this is that only net changes are applied to an index (as one or more insert, update, and/or delete operations).  In this case, the net effect is a single update of the filtered unique index: changing the row for ak = ‘A’ from pk = 4 to pk = 1.  In case that is less than 100% clear, let’s look at the operation in test two again:          Target                     Changes                   Result +-----------------------+    +------------------+    +-----------------------+ ¦ pk ¦ ak ¦ status_code ¦    ¦ pk ¦ status_code ¦    ¦ pk ¦ ak ¦ status_code ¦ ¦----+----+-------------¦    ¦----+-------------¦    ¦----+----+-------------¦ ¦  1 ¦ A  ¦ d           ¦    ¦  1 ¦ d           ¦    ¦  1 ¦ A  ¦ a           ¦ ¦  2 ¦ B  ¦ a           ¦    ¦  4 ¦ a           ¦    ¦  2 ¦ B  ¦ a           ¦ ¦  3 ¦ C  ¦ a           ¦    +------------------+    ¦  3 ¦ C  ¦ a           ¦ ¦  4 ¦ A  ¦ a           ¦                            ¦  4 ¦ A  ¦ d           ¦ +-----------------------+                            +-----------------------+ From the filtered index’s point of view (filtered for status_code = ‘a’ and shown in nonclustered index key order) the overall effect of the query is:   Before           After +---------+    +---------+ ¦ pk ¦ ak ¦    ¦ pk ¦ ak ¦ ¦----+----¦    ¦----+----¦ ¦  4 ¦ A  ¦    ¦  1 ¦ A  ¦ ¦  2 ¦ B  ¦    ¦  2 ¦ B  ¦ ¦  3 ¦ C  ¦    ¦  3 ¦ C  ¦ +---------+    +---------+ The single net change there is a change of pk from 4 to 1 for the nonclustered index entry ak = ‘A’.  This is the magic performed by the split, sort, and collapse.  Notice in particular how the original changes to the index key (on the ‘ak’ column) have been transformed into an update of a non-key column (pk is included in the nonclustered index).  By not updating any nonclustered index keys, we are guaranteed to avoid transient key violations. The Execution Plans The estimated MERGE execution plan that produces the incorrect key-violation error looks like this (click to enlarge in a new window): The successful UPDATE execution plan is (click to enlarge in a new window): The MERGE execution plan is a narrow (per-row) update.  The single Clustered Index Merge operator maintains both the clustered index and the filtered nonclustered index.  The UPDATE plan is a wide (per-index) update.  The clustered index is maintained first, then the Split, Filter, Sort, Collapse sequence is applied before the nonclustered index is separately maintained. There is always a wide update plan for any query that modifies the database. The narrow form is a performance optimization where the number of rows is expected to be relatively small, and is not available for all operations.  One of the operations that should disallow a narrow plan is maintaining a unique index where intermediate key violations could occur. Workarounds The MERGE can be made to work (producing a wide update plan with split, sort, and collapse) by: Adding all columns referenced in the filtered index’s WHERE clause to the index key (INCLUDE is not sufficient); or Executing the query with trace flag 8790 set e.g. OPTION (QUERYTRACEON 8790). Undocumented trace flag 8790 forces a wide update plan for any data-changing query (remember that a wide update plan is always possible).  Either change will produce a successfully-executing wide update plan for the MERGE that failed previously. Conclusion The optimizer fails to spot the possibility of transient unique key violations with MERGE under the conditions listed at the start of this post.  It incorrectly chooses a narrow plan for the MERGE, which cannot provide the protection of a split/sort/collapse sequence for the nonclustered index maintenance. The MERGE plan may fail at execution time depending on the order in which rows are processed, and the distribution of data in the database.  Worse, a previously solid MERGE query may suddenly start to fail unpredictably if a filtered unique index is added to the merge target table at any point. Connect bug filed here Tests performed on SQL Server 2012 SP1 CUI (build 11.0.3321) x64 Developer Edition © 2012 Paul White – All Rights Reserved Twitter: @SQL_Kiwi Email: [email protected]

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  • MongoDB and datasets that don't fit in RAM no matter how hard you shove

    - by sysadmin1138
    This is very system dependent, but chances are near certain we'll scale past some arbitrary cliff and get into Real Trouble. I'm curious what kind of rules-of-thumb exist for a good RAM to Disk-space ratio. We're planning our next round of systems, and need to make some choices regarding RAM, SSDs, and how much of each the new nodes will get. But now for some performance details! During normal workflow of a single project-run, MongoDB is hit with a very high percentage of writes (70-80%). Once the second stage of the processing pipeline hits, it's extremely high read as it needs to deduplicate records identified in the first half of processing. This is the workflow for which "keep your working set in RAM" is made for, and we're designing around that assumption. The entire dataset is continually hit with random queries from end-user derived sources; though the frequency is irregular, the size is usually pretty small (groups of 10 documents). Since this is user-facing, the replies need to be under the "bored-now" threshold of 3 seconds. This access pattern is much less likely to be in cache, so will be very likely to incur disk hits. A secondary processing workflow is high read of previous processing runs that may be days, weeks, or even months old, and is run infrequently but still needs to be zippy. Up to 100% of the documents in the previous processing run will be accessed. No amount of cache-warming can help with this, I suspect. Finished document sizes vary widely, but the median size is about 8K. The high-read portion of the normal project processing strongly suggests the use of Replicas to help distribute the Read traffic. I have read elsewhere that a 1:10 RAM-GB to HD-GB is a good rule-of-thumb for slow disks, As we are seriously considering using much faster SSDs, I'd like to know if there is a similar rule of thumb for fast disks. I know we're using Mongo in a way where cache-everything really isn't going to fly, which is why I'm looking at ways to engineer a system that can survive such usage. The entire dataset will likely be most of a TB within half a year and keep growing.

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  • DNS Query.log - Multiple query’s for ripe.net

    - by Christopher Wilson
    Currently I run a DNS server (bind9) that handles queries from clients over the internet lately I have noticed hundreds of queries from all different address's that look like this (Server IP removed) client 216.59.33.210#53: query: ripe.net IN ANY +ED (0.0.0.0) client 216.59.33.204#53: query: ripe.net IN ANY +ED (0.0.0.0) client 208.64.127.5#53: query: ripe.net IN ANY +ED (0.0.0.0) client 184.107.255.202#53: query: ripe.net IN ANY +ED (0.0.0.0) client 208.64.127.5#53: query: ripe.net IN ANY +ED (0.0.0.0) client 208.64.127.5#53: query: ripe.net IN ANY +ED (0.0.0.0) client 205.204.65.83#53: query: ripe.net IN ANY +ED (0.0.0.0) client 69.162.110.106#53: query: ripe.net IN ANY +ED (0.0.0.0) client 216.59.33.210#53: query: ripe.net IN ANY +ED (0.0.0.0) client 69.162.110.106#53: query: ripe.net IN ANY +ED (0.0.0.0) client 216.59.33.204#53: query: ripe.net IN ANY +ED (0.0.0.0) client 208.64.127.5#53: query: ripe.net IN ANY +ED (0.0.0.0) Can someone please explain why there are so many clients querying for ripe.net ?

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  • How to scale MongoDB

    - by terence410
    I know that MongoDB can scale vertically. What about if I running out of disk? I am currently using EC2 with EBS. As you know, I have to assign EBS for a fixed size. What if the mongodb growth bigger than the EBS size? Do I have to create a larger EBS and Copy & Paste the files? Or shall we start more MongoDB instance and each connect to different EBS disk? In such case, I could connect to a different instance for different databases.

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  • Looking for a generic handler/service for mongodb and asp.net / c#

    - by JohnAgan
    I am new to MongoDB and have a perfect place in mind to use it. However, it's only worth it if I can make the queries from JavaScript and return JSON. I read another post on here of someone asking a similar question, but not specific to C#. What's the easiest way I can implement a generic service/handler in asp.net/c# that would allow me to interact with mongodb via JavaScript? I understand JavaScript can't call mongodb directly, so the next best thing is what I'm looking for.

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  • Mongodb Slave replication lag

    - by Leonid Bugaev
    We using standard mongo setup: 2 replicas + 1 arbiter. Both replica servers use same AWS m1.medium with RAID10 EBS. We experiencing constantly growing replication lag on secondary replica. I tried to do full-resync, you can see it on graph, but it helped only for some hours. Our mongo usage is really low now, and frankly i can't understan why it can be. iostat 1 for secondary: avg-cpu: %user %nice %system %iowait %steal %idle 80.39 0.00 2.94 0.00 16.67 0.00 Device: tps kB_read/s kB_wrtn/s kB_read kB_wrtn xvdap1 0.00 0.00 0.00 0 0 xvdb 0.00 0.00 0.00 0 0 xvdfp4 12.75 0.00 189.22 0 193 xvdfp3 12.75 0.00 189.22 0 193 xvdfp2 7.84 0.00 40.20 0 41 xvdfp1 7.84 0.00 40.20 0 41 md127 19.61 0.00 219.61 0 224 mongostat for secondary (why 100% locks? i guess its the problem): insert query update delete getmore command flushes mapped vsize res faults locked % idx miss % qr|qw ar|aw netIn netOut conn set repl time *10 *0 *16 *0 0 2|4 0 30.9g 62.4g 1.65g 0 107 0 0|0 0|0 198b 1k 16 replset-01 SEC 06:55:37 *4 *0 *8 *0 0 12|0 0 30.9g 62.4g 1.65g 0 91.7 0 0|0 0|0 837b 5k 16 replset-01 SEC 06:55:38 *4 *0 *7 *0 0 3|0 0 30.9g 62.4g 1.64g 0 110 0 0|0 0|0 342b 1k 16 replset-01 SEC 06:55:39 *4 *0 *8 *0 0 1|0 0 30.9g 62.4g 1.64g 0 82.9 0 0|0 0|0 62b 1k 16 replset-01 SEC 06:55:40 *3 *0 *7 *0 0 5|0 0 30.9g 62.4g 1.6g 0 75.2 0 0|0 0|0 466b 2k 16 replset-01 SEC 06:55:41 *4 *0 *7 *0 0 1|0 0 30.9g 62.4g 1.64g 0 138 0 0|0 0|1 62b 1k 16 replset-01 SEC 06:55:42 *7 *0 *15 *0 0 3|0 0 30.9g 62.4g 1.64g 0 95.4 0 0|0 0|0 342b 1k 16 replset-01 SEC 06:55:43 *7 *0 *14 *0 0 1|0 0 30.9g 62.4g 1.64g 0 98 0 0|0 0|0 62b 1k 16 replset-01 SEC 06:55:44 *8 *0 *17 *0 0 3|0 0 30.9g 62.4g 1.64g 0 96.3 0 0|0 0|0 342b 1k 16 replset-01 SEC 06:55:45 *7 *0 *14 *0 0 3|0 0 30.9g 62.4g 1.64g 0 96.1 0 0|0 0|0 186b 2k 16 replset-01 SEC 06:55:46 mongostat for primary insert query update delete getmore command flushes mapped vsize res faults locked % idx miss % qr|qw ar|aw netIn netOut conn set repl time 12 30 20 0 0 3 0 30.9g 62.6g 641m 0 0.9 0 0|0 0|0 212k 619k 48 replset-01 M 06:56:41 5 17 10 0 0 2 0 30.9g 62.6g 641m 0 0.5 0 0|0 0|0 159k 429k 48 replset-01 M 06:56:42 9 22 16 0 0 3 0 30.9g 62.6g 642m 0 0.7 0 0|0 0|0 158k 276k 48 replset-01 M 06:56:43 6 18 12 0 0 2 0 30.9g 62.6g 640m 0 0.7 0 0|0 0|0 93k 231k 48 replset-01 M 06:56:44 6 12 8 0 0 3 0 30.9g 62.6g 640m 0 0.3 0 0|0 0|0 80k 125k 48 replset-01 M 06:56:45 8 21 14 0 0 9 0 30.9g 62.6g 641m 0 0.6 0 0|0 0|0 118k 419k 48 replset-01 M 06:56:46 10 34 20 0 0 6 0 30.9g 62.6g 640m 0 1.3 0 0|0 0|0 164k 527k 48 replset-01 M 06:56:47 6 21 13 0 0 2 0 30.9g 62.6g 641m 0 0.7 0 0|0 0|0 111k 477k 48 replset-01 M 06:56:48 8 21 15 0 0 2 0 30.9g 62.6g 641m 0 0.7 0 0|0 0|0 204k 336k 48 replset-01 M 06:56:49 4 12 8 0 0 8 0 30.9g 62.6g 641m 0 0.5 0 0|0 0|0 156k 530k 48 replset-01 M 06:56:50 Mongo version: 2.0.6

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  • Embedded MongoDB when running integration tests

    - by seanhodges
    My question is a variation of this one. Since my Java Web-app project requires a lot of read filters/queries and interfaces with tools like GridFS, I'm struggling to think of a sensible way to simulate MongoDB in the way the above solution suggests. Therefore, I'm considering running an embedded instance of MongoDB alongside my integration tests. I'd like it to start up automatically (either for each test or the whole suite), flush the database for every test, and shut down at the end. These tests might be run on development machines as well as the CI server, so my solution will also need to be portable. Can anyone with more knowledge on MongoDB help me get idea of the feasibility of this approach, and/or perhaps suggest any reading material that might help me get started? I'm also open to other suggestions people might have on how I could approach this problem...

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  • Java EE 6 and NoSQL/MongoDB on GlassFish using JPA and EclipseLink 2.4 (TOTD #175)

    - by arungupta
    TOTD #166 explained how to use MongoDB in your Java EE 6 applications. The code in that tip used the APIs exposed by the MongoDB Java driver and so requires you to learn a new API. However if you are building Java EE 6 applications then you are already familiar with Java Persistence API (JPA). Eclipse Link 2.4, scheduled to release as part of Eclipse Juno, provides support for NoSQL databases by mapping a JPA entity to a document. Their wiki provides complete explanation of how the mapping is done. This Tip Of The Day (TOTD) will show how you can leverage that support in your Java EE 6 applications deployed on GlassFish 3.1.2. Before we dig into the code, here are the key concepts ... A POJO is mapped to a NoSQL data source using @NoSQL or <no-sql> element in "persistence.xml". A subset of JPQL and Criteria query are supported, based upon the underlying data store Connection properties are defined in "persistence.xml" Now, lets lets take a look at the code ... Download the latest EclipseLink 2.4 Nightly Bundle. There is a Installer, Source, and Bundle - make sure to download the Bundle link (20120410) and unzip. Download GlassFish 3.1.2 zip and unzip. Install the Eclipse Link 2.4 JARs in GlassFish Remove the following JARs from "glassfish/modules": org.eclipse.persistence.antlr.jar org.eclipse.persistence.asm.jar org.eclipse.persistence.core.jar org.eclipse.persistence.jpa.jar org.eclipse.persistence.jpa.modelgen.jar org.eclipse.persistence.moxy.jar org.eclipse.persistence.oracle.jar Add the following JARs from Eclipse Link 2.4 nightly build to "glassfish/modules": org.eclipse.persistence.antlr_3.2.0.v201107111232.jar org.eclipse.persistence.asm_3.3.1.v201107111215.jar org.eclipse.persistence.core.jpql_2.4.0.v20120407-r11132.jar org.eclipse.persistence.core_2.4.0.v20120407-r11132.jar org.eclipse.persistence.jpa.jpql_2.0.0.v20120407-r11132.jar org.eclipse.persistence.jpa.modelgen_2.4.0.v20120407-r11132.jar org.eclipse.persistence.jpa_2.4.0.v20120407-r11132.jar org.eclipse.persistence.moxy_2.4.0.v20120407-r11132.jar org.eclipse.persistence.nosql_2.4.0.v20120407-r11132.jar org.eclipse.persistence.oracle_2.4.0.v20120407-r11132.jar Start MongoDB Download latest MongoDB from here (2.0.4 as of this writing). Create the default data directory for MongoDB as: sudo mkdir -p /data/db/sudo chown `id -u` /data/db Refer to Quickstart for more details. Start MongoDB as: arungup-mac:mongodb-osx-x86_64-2.0.4 <arungup> ->./bin/mongod./bin/mongod --help for help and startup optionsMon Apr  9 12:56:02 [initandlisten] MongoDB starting : pid=3124 port=27017 dbpath=/data/db/ 64-bit host=arungup-mac.localMon Apr  9 12:56:02 [initandlisten] db version v2.0.4, pdfile version 4.5Mon Apr  9 12:56:02 [initandlisten] git version: 329f3c47fe8136c03392c8f0e548506cb21f8ebfMon Apr  9 12:56:02 [initandlisten] build info: Darwin erh2.10gen.cc 9.8.0 Darwin Kernel Version 9.8.0: Wed Jul 15 16:55:01 PDT 2009; root:xnu-1228.15.4~1/RELEASE_I386 i386 BOOST_LIB_VERSION=1_40Mon Apr  9 12:56:02 [initandlisten] options: {}Mon Apr  9 12:56:02 [initandlisten] journal dir=/data/db/journalMon Apr  9 12:56:02 [initandlisten] recover : no journal files present, no recovery neededMon Apr  9 12:56:02 [websvr] admin web console waiting for connections on port 28017Mon Apr  9 12:56:02 [initandlisten] waiting for connections on port 27017 Check out the JPA/NoSQL sample from SVN repository. The complete source code built in this TOTD can be downloaded here. Create Java EE 6 web app Create a Java EE 6 Maven web app as: mvn archetype:generate -DarchetypeGroupId=org.codehaus.mojo.archetypes -DarchetypeArtifactId=webapp-javaee6 -DgroupId=model -DartifactId=javaee-nosql -DarchetypeVersion=1.5 -DinteractiveMode=false Copy the model files from the checked out workspace to the generated project as: cd javaee-nosqlcp -r ~/code/workspaces/org.eclipse.persistence.example.jpa.nosql.mongo/src/model src/main/java Copy "persistence.xml" mkdir src/main/resources cp -r ~/code/workspaces/org.eclipse.persistence.example.jpa.nosql.mongo/src/META-INF ./src/main/resources Add the following dependencies: <dependency> <groupId>org.eclipse.persistence</groupId> <artifactId>org.eclipse.persistence.jpa</artifactId> <version>2.4.0-SNAPSHOT</version> <scope>provided</scope></dependency><dependency> <groupId>org.eclipse.persistence</groupId> <artifactId>org.eclipse.persistence.nosql</artifactId> <version>2.4.0-SNAPSHOT</version></dependency><dependency> <groupId>org.mongodb</groupId> <artifactId>mongo-java-driver</artifactId> <version>2.7.3</version></dependency> The first one is for the EclipseLink latest APIs, the second one is for EclipseLink/NoSQL support, and the last one is the MongoDB Java driver. And the following repository: <repositories> <repository> <id>EclipseLink Repo</id> <url>http://www.eclipse.org/downloads/download.php?r=1&amp;nf=1&amp;file=/rt/eclipselink/maven.repo</url> <snapshots> <enabled>true</enabled> </snapshots> </repository>  </repositories> Copy the "Test.java" to the generated project: mkdir src/main/java/examplecp -r ~/code/workspaces/org.eclipse.persistence.example.jpa.nosql.mongo/src/example/Test.java ./src/main/java/example/ This file contains the source code to CRUD the JPA entity to MongoDB. This sample is explained in detail on EclipseLink wiki. Create a new Servlet in "example" directory as: package example;import java.io.IOException;import java.io.PrintWriter;import javax.servlet.ServletException;import javax.servlet.annotation.WebServlet;import javax.servlet.http.HttpServlet;import javax.servlet.http.HttpServletRequest;import javax.servlet.http.HttpServletResponse;/** * @author Arun Gupta */@WebServlet(name = "TestServlet", urlPatterns = {"/TestServlet"})public class TestServlet extends HttpServlet { protected void processRequest(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { response.setContentType("text/html;charset=UTF-8"); PrintWriter out = response.getWriter(); try { out.println("<html>"); out.println("<head>"); out.println("<title>Servlet TestServlet</title>"); out.println("</head>"); out.println("<body>"); out.println("<h1>Servlet TestServlet at " + request.getContextPath() + "</h1>"); try { Test.main(null); } catch (Exception ex) { ex.printStackTrace(); } out.println("</body>"); out.println("</html>"); } finally { out.close(); } } @Override protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { processRequest(request, response); } @Override protected void doPost(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { processRequest(request, response); }} Build the project and deploy it as: mvn clean packageglassfish3/bin/asadmin deploy --force=true target/javaee-nosql-1.0-SNAPSHOT.war Accessing http://localhost:8080/javaee-nosql/TestServlet shows the following messages in the server.log: connecting(EISLogin( platform=> MongoPlatform user name=> "" MongoConnectionSpec())) . . .Connected: User: Database: 2.7  Version: 2.7 . . .Executing MappedInteraction() spec => null properties => {mongo.collection=CUSTOMER, mongo.operation=INSERT} input => [DatabaseRecord( CUSTOMER._id => 4F848E2BDA0670307E2A8FA4 CUSTOMER.NAME => AMCE)]. . .Data access result: [{TOTALCOST=757.0, ORDERLINES=[{DESCRIPTION=table, LINENUMBER=1, COST=300.0}, {DESCRIPTION=balls, LINENUMBER=2, COST=5.0}, {DESCRIPTION=rackets, LINENUMBER=3, COST=15.0}, {DESCRIPTION=net, LINENUMBER=4, COST=2.0}, {DESCRIPTION=shipping, LINENUMBER=5, COST=80.0}, {DESCRIPTION=handling, LINENUMBER=6, COST=55.0},{DESCRIPTION=tax, LINENUMBER=7, COST=300.0}], SHIPPINGADDRESS=[{POSTALCODE=L5J1H7, PROVINCE=ON, COUNTRY=Canada, CITY=Ottawa,STREET=17 Jane St.}], VERSION=2, _id=4F848E2BDA0670307E2A8FA8,DESCRIPTION=Pingpong table, CUSTOMER__id=4F848E2BDA0670307E2A8FA7, BILLINGADDRESS=[{POSTALCODE=L5J1H8, PROVINCE=ON, COUNTRY=Canada, CITY=Ottawa, STREET=7 Bank St.}]}] You'll not see any output in the browser, just the output in the console. But the code can be easily modified to do so. Once again, the complete Maven project can be downloaded here. Do you want to try accessing relational and non-relational (aka NoSQL) databases in the same PU ?

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  • How to query through a DBRef in MongoDB/pymongo?

    - by Soviut
    Is it possible to query through a DBRef using a single find spec? user collection { 'age': 30 } post collection { 'user': DBRef('user', ...) } Is it possible to query for all post who's users are 30 in a single find step? If not, would it be wise to create a javascript function to handle the multi-stage operation or will that cause blocking problems?

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  • SQLAuthority News – Download Whitepaper – Understanding and Controlling Parallel Query Processing in SQL Server

    - by pinaldave
    My recently article SQL SERVER – Reducing CXPACKET Wait Stats for High Transactional Database has received many good comments regarding MAXDOP 1 and MAXDOP 0. I really enjoyed reading the comments as the comments are received from industry leaders and gurus. I was further researching on the subject and I end up on following white paper written by Microsoft. Understanding and Controlling Parallel Query Processing in SQL Server Data warehousing and general reporting applications tend to be CPU intensive because they need to read and process a large number of rows. To facilitate quick data processing for queries that touch a large amount of data, Microsoft SQL Server exploits the power of multiple logical processors to provide parallel query processing operations such as parallel scans. Through extensive testing, we have learned that, for most large queries that are executed in a parallel fashion, SQL Server can deliver linear or nearly linear response time speedup as the number of logical processors increases. However, some queries in high parallelism scenarios perform suboptimally. There are also some parallelism issues that can occur in a multi-user parallel query workload. This white paper describes parallel performance problems you might encounter when you run such queries and workloads, and it explains why these issues occur. In addition, it presents how data warehouse developers can detect these issues, and how they can work around them or mitigate them. To review the document, please download the Understanding and Controlling Parallel Query Processing in SQL Server Word document. Note: Above abstract has been taken from here. The real question is what does the parallel queries has made life of DBA much simpler or is it looked at with potential issue related to degradation of the performance? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL White Papers, SQLAuthority News, T SQL, Technology

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  • SQL SERVER – Quiz and Video – Introduction to Hierarchical Query using a Recursive CTE

    - by pinaldave
    This blog post is inspired from SQL Queries Joes 2 Pros: SQL Query Techniques For Microsoft SQL Server 2008 – SQL Exam Prep Series 70-433 – Volume 2.[Amazon] | [Flipkart] | [Kindle] | [IndiaPlaza] This is follow up blog post of my earlier blog post on the same subject - SQL SERVER – Introduction to Hierarchical Query using a Recursive CTE – A Primer. In the article we discussed various basics terminology of the CTE. The article further covers following important concepts of common table expression. What is a Common Table Expression (CTE) Building a Recursive CTE Identify the Anchor and Recursive Query Add the Anchor and Recursive query to a CTE Add an expression to track hierarchical level Add a self-referencing INNER JOIN statement Above six are the most important concepts related to CTE and SQL Server.  There are many more things one has to learn but without beginners fundamentals one can’t learn the advanced  concepts. Let us have small quiz and check how many of you get the fundamentals right. Quiz 1) You have an employee table with the following data. EmpID FirstName LastName MgrID 1 David Kennson 11 2 Eric Bender 11 3 Lisa Kendall 4 4 David Lonning 11 5 John Marshbank 4 6 James Newton 3 7 Sally Smith NULL You need to write a recursive CTE that shows the EmpID, FirstName, LastName, MgrID, and employee level. The CEO should be listed at Level 1. All people who work for the CEO will be listed at Level 2. All of the people who work for those people will be listed at Level 3. Which CTE code will achieve this result? WITH EmpList AS (SELECT Boss.EmpID, Boss.FName, Boss.LName, Boss.MgrID, 1 AS Lvl FROM Employee AS Boss WHERE Boss.MgrID IS NULL UNION ALL SELECT E.EmpID, E.FirstName, E.LastName, E.MgrID, EmpList.Lvl + 1 FROM Employee AS E INNER JOIN EmpList ON E.MgrID = EmpList.EmpID) SELECT * FROM EmpList WITH EmpListAS (SELECT EmpID, FirstName, LastName, MgrID, 1 as Lvl FROM Employee WHERE MgrID IS NULL UNION ALL SELECT EmpID, FirstName, LastName, MgrID, 2 as Lvl ) SELECT * FROM BossList WITH EmpList AS (SELECT EmpID, FirstName, LastName, MgrID, 1 as Lvl FROM Employee WHERE MgrID is NOT NULL UNION SELECT EmpID, FirstName, LastName, MgrID, BossList.Lvl + 1 FROM Employee INNER JOIN EmpList BossList ON Employee.MgrID = BossList.EmpID) SELECT * FROM EmpList 2) You have a table named Employee. The EmployeeID of each employee’s manager is in the ManagerID column. You need to write a recursive query that produces a list of employees and their manager. The query must also include the employee’s level in the hierarchy. You write the following code segment: WITH EmployeeList (EmployeeID, FullName, ManagerName, Level) AS ( –PICK ANSWER CODE HERE ) SELECT EmployeeID, FullName, ” AS [ManagerID], 1 AS [Level] FROM Employee WHERE ManagerID IS NULL UNION ALL SELECT emp.EmployeeID, emp.FullName mgr.FullName, 1 + 1 AS [Level] FROM Employee emp JOIN Employee mgr ON emp.ManagerID = mgr.EmployeeId SELECT EmployeeID, FullName, ” AS [ManagerID], 1 AS [Level] FROM Employee WHERE ManagerID IS NULL UNION ALL SELECT emp.EmployeeID, emp.FullName, mgr.FullName, mgr.Level + 1 FROM EmployeeList mgr JOIN Employee emp ON emp.ManagerID = mgr.EmployeeId Now make sure that you write down all the answers on the piece of paper. Watch following video and read earlier article over here. If you want to change the answer you still have chance. Solution 1) 1 2) 2 Now compare let us check the answers and compare your answers to following answers. I am very confident you will get them correct. Available at USA: Amazon India: Flipkart | IndiaPlaza Volume: 1, 2, 3, 4, 5 Please leave your feedback in the comment area for the quiz and video. Did you know all the answers of the quiz? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Joes 2 Pros, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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