├── 2016 └── README.md ├── README.md ├── articles ├── MDB_DN_2016_03_Eclipse_BiConnector.pdf ├── MDB_DN_2016_04_CheckConstraints.pdf ├── MDB_DN_2016_04_CheckConstraints.pptx ├── MDB_DN_2016_05_IndexTuning.pdf ├── MDB_DN_2016_05_IndexTuning.pptx ├── MDB_DN_2016_05_IndexTuning.pptx.pdf ├── MDB_DN_2016_06_QueryPrimer.pdf ├── MDB_DN_2016_06_QueryPrimer.pptx ├── MDB_DN_2016_06_QueryPrimer.pptx.pdf ├── MDB_DN_2016_07_ElasticSearch.pdf ├── MDB_DN_2016_08_Tools.pdf ├── MDB_DN_2016_09_34Features.pdf ├── MDB_DN_2016_09_34Features.pptx ├── MDB_DN_2016_09_34Features_ResourceKit.tar ├── MDB_DN_2016_10_Compass.pdf ├── MDB_DN_2016_11_OpsMgr.pdf ├── MDB_DN_2016_12_SingleView.pdf ├── MDB_DN_2016_12_SingleView.tar ├── MDB_DN_2017_13_SingleView.pdf ├── MDB_DN_2017_13_SingleView.tar ├── MDB_DN_2017_14b_CloudFoundry.pdf ├── MDB_DN_2017_15_Monitoringb.pdf ├── MDB_DN_2017_16_IOT.pdf ├── MDB_DN_2017_16_IOT.tar ├── MDB_DN_2017_17_BiConnector2.pdf ├── MDB_DN_2017_18_QueryHackathon.pdf ├── MDB_DN_2017_18_QueryHackathon_slides.pdf └── OracleVirtualBoxToRunAVMDK.pdf └── data_download ├── README.md ├── companies.json.zip ├── twitter.json.zip └── zips.json /2016/README.md: -------------------------------------------------------------------------------- 1 | MongoDB Developer's Notebook - Monthly Articles 2016 2 | =================== 3 | 4 | | **[Monthly Articles - 2017](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/README.md)**| **[Monthly Articles - 2016](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/2016/README.md)**| **[Data Downloads](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/data_download/README.md)** | 5 | |-------------------------|--------------------------|-----------------| 6 | 7 | This is a personal blog where we answer one or more questions each month from MongoDB customers in a non-official, non-warranted, non much of anything forum. 8 | 9 | 2016 December - - 10 | 11 | >Customer: 12 | >Through acquisition of several competitors, my company has numerous redundant order 13 | >entry, processing and fulfillment systems. When a customer contacts us via phone or 14 | >via the Website, any order the customer may have placed could reside in any of these 15 | >systems, causing confusion, delay and error. We are looking at a master-data-management 16 | >solution, with a 24 month design and delivery cycle that we find prohibitive. mongoDB 17 | >lists this type of problem as single-view, and as one of the problems that your 18 | >database server and its ecosystem help address. What can you tell me ? 19 | > 20 | >Daniel: 21 | >In this article we deliver CDC from MySQL and Redis into mongoDB with a Web UI. 22 | > 23 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_12_SingleView.pdf) 24 | > 25 | >[Resource Kit](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_12_SingleView.tar), all of the programs used in this edition in Tar format. 26 | > 27 | >This became part-one of a two part document. Part-two appears under January/2017. 28 | 29 | 2016 November - - 30 | 31 | >Customer: 32 | >After our initial (and hugely successful) project writing a mobile application that 33 | >uses MongoDB, I have been tasked with determining our run time environment for the 34 | >MongoDB database server. I am a little confused with the differences between MongoDB 35 | >Operations Manager, MongoDB Cloud Manager, and such. We will be running this system 36 | >on our multi-national private cloud. What can you tell me ? 37 | > 38 | >Daniel: 39 | >In this article we overview mongoDB Ops Mgr, Cloud Mgr, and Atlas. We also install, 40 | >configure and use mongoDB Ops Mgr version (3.2) from scratch. 41 | > 42 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_11_OpsMgr.pdf) 43 | 44 | 2016 October - - 45 | 46 | >Customer: 47 | >I am new to MongoDB, and am wondering what tools you offer for developers. Specfically, 48 | >I am used to using a graphical tool for query plans, SQL style DML execution, data 49 | >dictionary exploration (How do you deal with a jagged row table store ?), etcetera. 50 | >What can you tell me ? 51 | > 52 | >Daniel: 53 | >In this article we install, configure and use mongoDB Compass. 54 | > 55 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_10_Compass.pdf) 56 | 57 | 2016 September - - 58 | 59 | >Customer: 60 | >It looks as though the new version 3.4 release of MongoDB has a number of cool business 61 | >intelligence related features. What can you tell me ? 62 | > 63 | >Daniel: 64 | >In this document we create 30+ demonstration programs that use the new features of mongoDB 65 | >release 3.4 database server. 66 | > 67 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_09_34Features.pdf) 68 | > 69 | >[Resource Kit](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_09_34Features_ResourceKit.tar), all of the programs used in this edition of this document in Tar format. 70 | 71 | 2016 August - - 72 | 73 | >Customer: 74 | >I recently took the MongoDB DBA certification exam, and was caught unprepared by questions 75 | >related to mongofiles, mongoperf, and more. What have I been missing by not using these utilities ? 76 | > 77 | >Daniel: 78 | >In this document we detail all/most of the mongoDB command line utilities for backup, 79 | >data migration, and more. 80 | > 81 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_08_Tools.pdf) 82 | 83 | 2016 July - - 84 | 85 | >Customer: 86 | >I’ve been asked to support a project that wants to deliver a fuzzy search capability 87 | >for customer entitlement. E.g., you contact our customer call center and we find your 88 | >record whether your name is David, Dave, Davey, you get the idea. I’ve read some on 89 | >line postings that imply integration between ElasticSearch and MongoDB. What can you 90 | >tell me ? 91 | > 92 | >Daniel: 93 | >In this document we detail the mongoDB Connector with connectivity to ElasticSearch 94 | >and other mongoDB database servers. 95 | > 96 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_07_ElasticSearch.pdf) 97 | 98 | 2016 June - - 99 | 100 | >Customer: 101 | >My team and I have been tasked with learning MongoDB, writing queries and getting 102 | >a new project out of the door in 3 weeks. I know how to write most queries in SQL, 103 | >and have been doing that for years. Can you detail for me when to use MongoDB find ( ), 104 | >versus aggregate ( ); pretty much give me a primer on the whole subject area ? 105 | > 106 | >Daniel: 107 | >In this document we take the TPC.org TPC/C, TPC/D, and TPC/H industry standard benchmarks 108 | >and write all of these queries in mongoDB. At the end of this document, we feel confident 109 | >stating your can write 98% or more of the queries you will ever need to write. 110 | > 111 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_06_QueryPrimer.pdf) 112 | > 113 | >[Related Powerpoint](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_06_QueryPrimer.pptx.pdf), from SLC Mug 114 | 115 | 2016 May - - 116 | 117 | >Customer: 118 | >I inherited a MongoDB database server with 60 collections and 100 or so indexes. 119 | >The business users are complaining about slow report completion times. What can 120 | >I do to improve performance ? 121 | > 122 | >Daniel: 123 | >In this document we do a full primer on query optimizers, index negation, and 124 | >more for mongoDB and other relational database servers. 125 | > 126 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_05_IndexTuning.pdf) 127 | > 128 | >[Related Powerpoint](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_05_IndexTuning.pptx.pdf), from SLC and Denver MUG 129 | > 130 | >[Posted here](https://www.mongodb.com/presentations/webinar-index-tuning-and-evaluation-using-mongodb?p=5721041a1b55a234bf9566de), this is also a Webinar. 131 | 132 | 2016 April - - 133 | 134 | >Customer: 135 | >I get that MongoDB offers a polymorphic schema, and that part of the 136 | >appeal of MongoDB is the no schema migration capability as new application 137 | >requirements roll in. Still, I come from SQL and really miss SQL style 138 | >check constraints. What can I do ? 139 | > 140 | >Daniel: 141 | >In this article we detail the construction and use of mongoDB document 142 | >validation. 143 | > 144 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_04_CheckConstraints.pdf) 145 | > 146 | >[Related PowerPoint](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_04_CheckConstraints.pptx), from SLC MUG 147 | 148 | 2016 March - - 149 | 150 | >Customer: 151 | >I prefer using Eclipse as my interactive development environment 152 | >(IDE). How do I interface with MongoDB when using Eclipse ? Can I run 153 | >structured query language select (SQL SELECT) statements against 154 | >MongoDB ? 155 | > 156 | >Daniel: 157 | >In this article we install, configure and use mongoDB Business Intelligence 158 | >Connector version 1.x. 159 | > 160 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2016_03_Eclipse_BiConnector.pdf) 161 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | MongoDB Developer's Notebook - Monthly Articles 2017 2 | =================== 3 | 4 | | **[Monthly Articles - 2017](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/README.md)**| **[Monthly Articles - 2016](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/2016/README.md)**| **[Data Downloads](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/data_download/README.md)** | 5 | |-------------------------|--------------------------|-----------------| 6 | 7 | This is a personal blog where we answer one or more questions each month from MongoDB customers in a non-official, non-warranted, non much of anything forum. 8 | 9 | This site is no longer updated with new articles. 10 | 11 | 2017 June - - 12 | 13 | >Customer: 14 | >I need a hackathon for my mongoDB Meetup.com group, a skill set everyone can use like 15 | >queries. Can you help ? 16 | > 17 | >Daniel: 18 | >The June/2016 edition of this document offers a primer on writing mongoDB queries; take 19 | >the SQL TPC-C, TPC-D, and TPC-H benchmark queries and replicate these in mongoDB. And 20 | >the May/2016 edition of this document details query tuning, and related. We write queries 21 | >regularly in this series of documents, but have never had a hackathon proper. The 22 | >September/2016 edition of this document detailed (new) version 3.4 features to mongoDB, 23 | >including 15+ new aggregate query stages. 24 | > 25 | >In this edition of MDB-DN we give you two data sets, US Postal codes and US airline 26 | >performance data, and ask you to answer these questions: 27 | > 28 | >• Which US city name is most common across states/provinces ? 29 | > 30 | >• Which US state has the highest number of unique city names ? 31 | > 32 | >• And, my airline is advertising they have improved on time performance. Are they being entirely truthful? 33 | > 34 | >We’ll do a bit of a primer, but as a hackathon; you need to discover the answers on your 35 | >own. (We’re kidding; answers are provided if you’re willing to cheat.) 36 | > 37 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2017_18_QueryHackathon.pdf). 38 | > 39 | >[Resource kit](https://drive.google.com/open?id=0B37pFF1dJ894aURseGFjNE11MWM), all of 40 | >the data programs used in this edition in Tar format. About 100 MB. 41 | > 42 | >[Related Powerpoint](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2017_18_QueryHackathon_slides.pdf). 43 | 44 | 45 | 2017 May - - 46 | 47 | >Customer: 48 | >I’m coming from Oracle and MySQL, and can not possibly believe that you guys do not 49 | >support running SQL- As a developer or even operations person I need to run SQL to 50 | >understand my data, not to mention giving access to the data to my business intelligence 51 | >users. What can you tell me ? 52 | > 53 | >Daniel: 54 | >Excellent question ! mongoDB has supported running SQL for some time, and with the 55 | >recent 2.1 release of our mongoDB Connector for Business Intelligence (BI Connector), 56 | >mongoDB’s capabilities in this area are even more performant. 57 | > 58 | >In this document we install, configure and use the version 2.1 BI Connector, and 59 | >install and use Eclipse, the Toad plugin for Eclipse, MySQL Workbench, a MySQL JDBC 60 | >driver, Apache Drill, and more. 61 | > 62 | >In addition to running SQL, the October 2016 edition of this document details install, 63 | >configuration and use of the graphical query user interface to mongoDB, named mongoDB 64 | >Compass. 65 | > 66 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2017_17_BiConnector2.pdf). 67 | 68 | 2017 April - - 69 | 70 | >Customer: 71 | >My company has a corporate campus with over 120 buildings, and an even larger number 72 | >of real time sensors; badge entry systems for doors, point of sale systems which we 73 | >can read, and more. One of the analytics routines we need to run associates a given 74 | >event with any other event at the same location and within a given amount of time. 75 | >Can you show me how to tie real time events to analytics routines inside mongoDB ? 76 | > 77 | >Daniel: 78 | >In this document we deliver a Web form with real time streaming data from a finger 79 | >pulse meter. We use Python to read from the device, deliver the Web form, yadda. 80 | >Honestly we used this device because it was the first we found with source code to 81 | >read and graph the output in real time. (We didn't have a readily available door sensor.) 82 | >If you choose not to purchase this device, we also simulate and output a data stream 83 | >from a pre-recorded ECG/EKG. (Net/net, this whole thing works whether you have a device 84 | >or not.) 85 | > 86 | >Analytics- We found a number of articles describing analytics that can be performed 87 | >on an ECG/EKG, in effect can I identify you from your ECG/EKG, but none of any consequence 88 | >for a pulse meter. So, we moved the analytics you requested to a new data set that we 89 | >provide and detail. 90 | > 91 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2017_16_IOT.pdf). 92 | > 93 | >[Resource Kit](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2017_16_IOT.tar), 94 | >all of the programs used in this edition in Tar format. 95 | 96 | 2017 March - - 97 | 98 | >Customer: 99 | >My company is committing to mongoDB in a large way. Over the years and with 100 | >prior technologies, we have built our own single pane of glass to monitor all of our 101 | >server systems; reporting server events, significant performance statistics, items to 102 | >watch for tuning, faults, and more. What does mongoDB offer in this area ? 103 | > 104 | >Daniel: 105 | >We can’t promise we will cover every topic in this area as the basic question you ask 106 | >is essentially; tell me everything I need to know. We will, however, list many of our 107 | >favorites in the area of monitoring, statistics, tuning, and more. We detail; the mongoDB 108 | >Ops Mgr API, db.serverStatus(), audit logging, the message log file, the OpLog, query 109 | >plans, index usage, and more. 110 | > 111 | >Where it makes sense we offer sample code. And in each case, we list the documentation 112 | >Url for further research as your specific needs and intents may suggest. 113 | > 114 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2017_15_Monitoringb.pdf). 115 | 116 | 2017 February - - 117 | 118 | >Customer: 119 | >While we claim we sprint and scrum, my company wants to move from a traditional deployment 120 | >monolithic application architecture to a microservices architecture. We’ve looked at Docker 121 | >and others, and currently plan to go all in on Cloud Foundry. What can you tell me ? 122 | > 123 | >Daniel: 124 | >In this article we overview the following topics; model-view-controller, stateful/stateless 125 | >applications, IaaS, PaaS, containers, application packaging (Gradle, Maven), Cloud Foundry, 126 | >services, user defined services, Cloud Foundry marketplace, and micro-services. We also 127 | >detail all of the mongoDB themed Cloud Foundry marketplace tiles. 128 | > 129 | >Then we install pcfdev, a laptop capable Cloud Foundry runtime, and deploy and scale a Python 130 | >Web application. 131 | > 132 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2017_14b_CloudFoundry.pdf). 133 | 134 | 2017 January - - 135 | 136 | >Customer: 137 | >I really enjoyed the December/2016 edition of this document where you detailed the single 138 | >view use case, however; you left off Kafka as a topic, and while you ingested data from 139 | >multiple sources, you failed to detail how to get data out of mongoDB. What can you tell 140 | >me ? 141 | > 142 | >Daniel: 143 | >In this article we continue our work from the December/2016 document, and add Kafka as a 144 | >data source and target. And we read from the mongoDB OpLog to do that push into Kafka. The 145 | >July/2016 edition of this document detailed the mongoDB Connector, which does this work 146 | >and more, but we wanted to demonstrate writing this code from scratch. 147 | > 148 | >[Download here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2017_13_SingleView.pdf). 149 | > 150 | >[Resource Kit](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/MDB_DN_2017_13_SingleView.tar), all of the programs used in this edition in Tar format. 151 | > 152 | >This became part-two of a two part document. Part-one appears under December/2016. 153 | 154 | If you are attending a Meetup group workshop on mongoDB Single View - - 155 | 156 | >We are distributing a virtual machine; (Username/password, root password ). On Windows or 157 | >Linux you will need the free VMWare Player, available 158 | >[Download here](http://www.vmware.com/products/player/playerpro-evaluation.html). VMWare's Player product 159 | >is not time-bombed, runs forever, yadda. There is no free VMWare Player for MAC, only a 160 | >for-charge product titled, VMWare Fusion. We've never tested it, however; Oracle VirtualBox 161 | >is supposed to be able to run this virtual machine. See this document for instructions on 162 | >the topic running VMDKs from VirtualBox, 163 | >[Click here](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/articles/OracleVirtualBoxToRunAVMDK.pdf). 164 | >Or, click this link for the source article, 165 | >[Click here](http://techathlon.com/how-to-run-a-vmdk-file-in-oracle-virtualbox/). 166 | > 167 | >Download the mongoDB Single View virtual machine here 168 | >[Click here (6 GB)](https://drive.google.com/file/d/0B37pFF1dJ894aEtRdEpCTnVKWTg). 169 | >This image was zipped up on Windows using 7-Zip. 170 | 171 | -------------------------------------------------------------------------------- /articles/MDB_DN_2016_03_Eclipse_BiConnector.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/farrell0/MongoDB-Developers-Notebook/06ee13aeeb3661c1e477b988a613389f3842310f/articles/MDB_DN_2016_03_Eclipse_BiConnector.pdf 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localhost:28001, localhost:28002") 12 | db = cn.test_db9 13 | 14 | db.my_coll.drop() 15 | # 16 | db.my_coll.insert( { "k1" : 1, "k2" : 2 } ) 17 | 18 | 19 | sss = list ( db.my_coll.find( {}, { "_id" : 0 } ) ) 20 | for s in sss: 21 | print s 22 | 23 | sss = list ( db.my_coll.aggregate( 24 | [ 25 | { "$project" : { "_id" : 0 } } 26 | ] 27 | ) ) 28 | for s in sss: 29 | print s 30 | 31 | 32 | db.my_coll.drop() 33 | 34 | 35 | # 36 | # Returns 37 | # 38 | # {u'k2': 2, u'k1': 1} 39 | # {u'k2': 2, u'k1': 1} 40 | # 41 | 42 | 43 | 11_StepUpSetUp.sh0000777000076500000240000000325312757111523012065 0ustar games#!/bin/bash 44 | 45 | 46 | # 47 | # Script to test the new stepUp() command. 48 | # 49 | 50 | 51 | # 52 | # Make the directories .. 53 | # 54 | export DB_DIR=/data/rs_manual9 55 | # 56 | rm -rf $DB_DIR 57 | mkdir -p $DB_DIR/node0 $DB_DIR/node1 $DB_DIR/node2 58 | touch $DB_DIR/pids 59 | # 60 | chmod -R 777 $DB_DIR 61 | 62 | 63 | # 64 | # Make 3 servers 65 | # 66 | echo " " 67 | echo "Make 3 servers." 68 | echo " " 69 | mongod --dbpath $DB_DIR/node0 \ 70 | --logpath $DB_DIR/node0/logfile \ 71 | --port 28000 --fork --noauth \ 72 | --replSet "abc" --smallfiles --wiredTigerCacheSizeGB 1 73 | ps -ef | grep mongod | grep 28000 | awk -F " " '{print $2}' >> $DB_DIR/pids 74 | mongod --dbpath $DB_DIR/node1 \ 75 | --logpath $DB_DIR/node1/logfile \ 76 | --port 28001 --fork --noauth \ 77 | --replSet "abc" --smallfiles --wiredTigerCacheSizeGB 1 78 | ps -ef | grep mongod | grep 28001 | awk -F " " '{print $2}' >> $DB_DIR/pids 79 | mongod --dbpath $DB_DIR/node2 \ 80 | --logpath $DB_DIR/node2/logfile \ 81 | --port 28002 --fork --noauth \ 82 | --replSet "abc" --smallfiles --wiredTigerCacheSizeGB 1 83 | ps -ef | grep mongod | grep 28002 | awk -F " " '{print $2}' >> $DB_DIR/pids 84 | 85 | sleep 10 86 | 87 | 88 | # 89 | # Initiate a replica set 90 | # 91 | echo " " 92 | echo "Initiate a replica set." 93 | echo " " 94 | mongo --port 28000 < /dev/null` 201 | do 202 | kill -9 $t 203 | done 204 | 205 | rm -fr $DB_DIR 2> /dev/null 206 | 207 | 208 | 209 | 210 | 211 | 14_ReadPreference.sh0000777000076500000240000000140712757671275012557 0ustar games#!/bin/bash 212 | 213 | 214 | # 215 | # Script to test the new read preference options. 216 | # 217 | 218 | 219 | # 220 | # From the previous step, we expect a 3 node 221 | # replica set with 28000 being the primary. 222 | # 223 | # 224 | echo " " 225 | echo "Setting up a 3 node replica set with 28000" 226 | echo "being the primary." 227 | echo " " 228 | 13_CleanUpFrom11.sh 2> /dev/null 229 | 11_StepUpSetUp.sh 2> /dev/null 230 | 231 | 232 | echo " " 233 | echo "Run a Python client requesting a secondary." 234 | echo " " 235 | 236 | python <> /data/withAuth/pids 1716 | 1717 | 1718 | ###################################################### 1719 | 1720 | 1721 | mongo --port 27000 --authenticationDatabase "admin" < /dev/null` 1768 | do 1769 | kill -9 $t 1770 | done 1771 | 1772 | rm -fr $DB_DIR 2> /dev/null 1773 | 1774 | 1775 | 1776 | 1777 | 1778 | 54_ReadViews.py0000777000076500000240000000214412761574225011607 0ustar games 1779 | 1780 | import pymongo 1781 | from pymongo import MongoClient 1782 | 1783 | cn = MongoClient("mongodb://localhost:27000") 1784 | 1785 | db = cn.test_db9 1786 | # 1787 | db.authenticate("my_user1", "password" ) 1788 | 1789 | print " " 1790 | print "Example (my_user1): find() from base collection." 1791 | print " " 1792 | try: 1793 | sss = list ( db.my_coll.find( {}, { "_id" : 0 } ) ) 1794 | for s in sss: 1795 | print s 1796 | except: 1797 | print "Can not read from base collection." 1798 | 1799 | 1800 | print " " 1801 | print "Example (my_user1): find() from view." 1802 | print " " 1803 | try: 1804 | sss = list ( db.my_view.find( { "favColor" : "Blue" }, { "_id" : 0 } ) ) 1805 | for s in sss: 1806 | print s 1807 | except: 1808 | print "Can not read from view." 1809 | 1810 | 1811 | db.logout() 1812 | # 1813 | db.authenticate("my_user2", "password" ) 1814 | 1815 | print " " 1816 | print "Example (my_user2): find() from base collection." 1817 | print " " 1818 | try: 1819 | sss = list ( db.my_coll.find( {}, { "_id" : 0 } ) ) 1820 | for s in sss: 1821 | print s 1822 | except: 1823 | print "Can not read from base collection." 1824 | 1825 | 1826 | print " " 1827 | print "Example (my_user2): find() from view." 1828 | print " " 1829 | try: 1830 | sss = list ( db.my_view.find( { "favColor" : "Blue" }, { "_id" : 0 } ) ) 1831 | for s in sss: 1832 | print s 1833 | except: 1834 | print "Can not read from view." 1835 | 1836 | 1837 | 1838 | 1839 | 70_GraphLookup.py0000777000076500000240000000444512761572261012153 0ustar games 1840 | 1841 | import pymongo 1842 | # 1843 | from pymongo import MongoClient 1844 | 1845 | 1846 | ###################################################### 1847 | 1848 | 1849 | cn = MongoClient("localhost:28000, localhost:28001, localhost:28002") 1850 | db = cn.test_db9 1851 | 1852 | db.my_persons.drop() 1853 | db.my_airports.drop() 1854 | 1855 | db.my_persons.insert( { "_id" : 1, "name" : "Shawn", "startAirport" : "JFK" } ) 1856 | 1857 | db.my_airports.insert( { "_id" : 2, "airport" : "JFK", "connects" : [ "BOS", "ORD" ] } ) 1858 | db.my_airports.insert( { "_id" : 3, "airport" : "BOS", "connects" : [ "JFK", "PWM" ] } ) 1859 | db.my_airports.insert( { "_id" : 4, "airport" : "ORD", "connects" : [ "JFK" ] } ) 1860 | db.my_airports.insert( { "_id" : 5, "airport" : "PWM", "connects" : [ "BOS", "LHR" ] } ) 1861 | db.my_airports.insert( { "_id" : 6, "airport" : "LHR", "connects" : [ "PWM" ] } ) 1862 | db.my_airports.insert( { "_id" : 7, "airport" : "LHR", "connects" : [ "JJJ" ] } ) 1863 | 1864 | 1865 | ###################################################### 1866 | 1867 | 1868 | print " " 1869 | print "Example: $graphLookup operator in aggregate." 1870 | print " " 1871 | sss = list ( db.my_persons.aggregate( 1872 | [ 1873 | { 1874 | "$match" : { "name" : "Shawn" } 1875 | }, 1876 | { 1877 | "$graphLookup" : 1878 | { 1879 | "from" : "my_airports", 1880 | "startWith" : "$startAirport", 1881 | "connectFromField" : "connects", 1882 | "connectToField" : "airport", 1883 | # 1884 | "maxDepth" : 6, 1885 | "depthField" : "numConns", 1886 | "as" : "dest" 1887 | } 1888 | }, 1889 | { 1890 | "$unwind" : "$dest" 1891 | }, 1892 | { 1893 | "$project" : 1894 | { 1895 | "_id" : 0, 1896 | "name" : 0, 1897 | "startAirport" : 0 # , 1898 | # 1899 | # "dest._id" : 1 1900 | } 1901 | }, 1902 | { 1903 | "$match" : 1904 | { 1905 | "dest.numConns" : { "$gte" : 1 } 1906 | } 1907 | } 1908 | ] ) ) 1909 | for s in sss: 1910 | print s 1911 | 1912 | 1913 | 1914 | 1915 | # 1916 | # Outputs, 1917 | # 1918 | # Example: $graphLookup operator in aggregate. 1919 | # 1920 | # {u'dest': {u'airport': u'BOS', u'_id': 3, u'connects': [u'JFK', u'PWM'], u'numConns': 1L}} 1921 | # {u'dest': {u'airport': u'ORD', u'_id': 4, u'connects': [u'JFK'], u'numConns': 1L}} 1922 | # 1923 | # {u'dest': {u'airport': u'PWM', u'_id': 5, u'connects': [u'BOS', u'LHR'], u'numConns': 2L}} 1924 | # 1925 | # {u'dest': {u'airport': u'LHR', u'_id': 7, u'connects': [u'JJJ'], u'numConns': 3L}} 1926 | # {u'dest': {u'airport': u'LHR', u'_id': 6, u'connects': [u'PWM'], u'numConns': 3L}} 1927 | 1928 | 1929 | 1930 | 1931 | 74_GraphLookup2.py0000777000076500000240000000616112762107115012227 0ustar games 1932 | 1933 | import pymongo 1934 | # 1935 | from pymongo import MongoClient 1936 | 1937 | 1938 | ###################################################### 1939 | 1940 | 1941 | cn = MongoClient("localhost:28000, localhost:28001, localhost:28002") 1942 | db = cn.test_db9 1943 | 1944 | db.my_employees.drop() 1945 | 1946 | db.my_employees.insert( { "_id" : 1, "name" : "Dev" , "title" : "CEO" } ) 1947 | db.my_employees.insert( { "_id" : 2, "name" : "Eliot" , "title" : "CTO" , "boss" : 1 } ) 1948 | db.my_employees.insert( { "_id" : 3, "name" : "Meagan" , "title" : "CMO" , "boss" : 1 } ) 1949 | db.my_employees.insert( { "_id" : 4, "name" : "Carlos" , "title" : "CRO" , "boss" : 1 } ) 1950 | db.my_employees.insert( { "_id" : 5, "name" : "Andrew" , "title" : "VP Eng" , "boss" : 2 } ) 1951 | db.my_employees.insert( { "_id" : 6, "name" : "Ron" , "title" : "VP PM" , "boss" : 2 } ) 1952 | db.my_employees.insert( { "_id" : 7, "name" : "Elyse" , "title" : "COO" , "boss" : 2 } ) 1953 | db.my_employees.insert( { "_id" : 8, "name" : "Richard" , "title" : "VP PS" , "boss" : 1 } ) 1954 | db.my_employees.insert( { "_id" : 9, "name" : "Kirsten" , "title" : "VP People", "boss" : 1 } ) 1955 | db.my_employees.insert( { "_id" : 10, "name" : "Daniel" , "title" : "Serf" , "boss" : 6 } ) 1956 | 1957 | 1958 | ###################################################### 1959 | 1960 | 1961 | print " " 1962 | print "Example: $graphLookup operator in aggregate." 1963 | print " For every employee, list all of their bosses." 1964 | print " " 1965 | sss = list ( db.my_employees.aggregate( 1966 | [ 1967 | { 1968 | "$graphLookup" : 1969 | { 1970 | "from" : "my_employees", 1971 | "startWith" : "$boss", 1972 | "connectFromField" : "boss", 1973 | "connectToField" : "_id", 1974 | # 1975 | # "maxDepth" : 6, 1976 | "depthField" : "level", 1977 | "as" : "allBosses" 1978 | } 1979 | } 1980 | ] ) ) 1981 | for s in sss: 1982 | print s 1983 | 1984 | 1985 | ###################################################### 1986 | 1987 | 1988 | print " " 1989 | print "Example: $graphLookup operator in aggregate." 1990 | print " For every employee, list all of their reports." 1991 | print " " 1992 | sss = list ( db.my_employees.aggregate( 1993 | [ 1994 | { 1995 | "$graphLookup" : 1996 | { 1997 | "from" : "my_employees", 1998 | "startWith" : "$_id", 1999 | "connectFromField" : "_id", 2000 | "connectToField" : "boss", 2001 | # 2002 | # "maxDepth" : 6, 2003 | "depthField" : "level", 2004 | "as" : "allReports" 2005 | } 2006 | }, 2007 | { 2008 | "$match" : 2009 | { 2010 | "allReports" : { "$ne" : [] } 2011 | } 2012 | } 2013 | ] ) ) 2014 | for s in sss: 2015 | print s 2016 | 2017 | 2018 | ###################################################### 2019 | 2020 | 2021 | print " " 2022 | print "Example: $graphLookup operator in aggregate." 2023 | print " Using maxDepth, listing only direct reports." 2024 | print " " 2025 | sss = list ( db.my_employees.aggregate( 2026 | [ 2027 | { 2028 | "$graphLookup" : 2029 | { 2030 | "from" : "my_employees", 2031 | "startWith" : "$_id", 2032 | "connectFromField" : "_id", 2033 | "connectToField" : "boss", 2034 | # 2035 | "maxDepth" : 0, 2036 | "depthField" : "level", 2037 | "as" : "allReports" 2038 | } 2039 | }, 2040 | { 2041 | "$match" : 2042 | { 2043 | "allReports" : { "$ne" : [] } 2044 | } 2045 | } 2046 | ] ) ) 2047 | for s in sss: 2048 | print s 2049 | 2050 | 2051 | 2052 | 75_OutputFrom74.txt0000777000076500000240000001066312762112015012376 0ustar games 2053 | 2054 | Example: $graphLookup operator in aggregate. 2055 | For every employee, list all of their bosses. 2056 | 2057 | {u'_id': 1, u'allBosses': 2058 | [ 2059 | ], u'name': u'Dev', u'title': u'CEO'} 2060 | 2061 | {u'title': u'CTO', u'_id': 2, u'allBosses': 2062 | [ 2063 | {u'level': 0L, u'_id': 1, u'name': u'Dev', u'title': u'CEO'} 2064 | ] u'name': u'Eliot', u'boss': 1} 2065 | 2066 | {u'title': u'CMO', u'_id': 3, u'allBosses': 2067 | [ 2068 | {u'level': 0L, u'_id': 1, u'name': u'Dev', u'title': u'CEO'} 2069 | ], u'name': u'Meagan', u'boss': 1} 2070 | 2071 | {u'title': u'CRO', u'_id': 4, u'allBosses': 2072 | [ 2073 | {u'level': 0L, u'_id': 1, u'name': u'Dev', u'title': u'CEO'} 2074 | ], u'name': u'Carlos', u'boss': 1} 2075 | 2076 | {u'title': u'VP Eng', u'_id': 5, u'allBosses': 2077 | [ 2078 | {u'level': 1L, u'_id': 1, u'name': u'Dev', u'title': u'CEO'}, 2079 | {u'title': u'CTO', u'level': 0L, u'_id': 2, u'name': u'Eliot', u'boss': 1} 2080 | ], u'name': u'Andrew', u'boss': 2} 2081 | 2082 | {u'title': u'VP PM', u'_id': 6, u'allBosses': 2083 | [ 2084 | {u'level': 1L, u'_id': 1, u'name': u'Dev', u'title': u'CEO'}, 2085 | {u'title': u'CTO', u'level': 0L, u'_id': 2, u'name': u'Eliot', u'boss': 1} 2086 | ], u'name': u'Ron', u'boss': 2} 2087 | 2088 | {u'title': u'COO', u'_id': 7, u'allBosses': 2089 | [ 2090 | {u'level': 1L, u'_id': 1, u'name': u'Dev', u'title': u'CEO'}, 2091 | {u'title': u'CTO', u'level': 0L, u'_id': 2, u'name': u'Eliot', u'boss': 1} 2092 | ], u'name': u'Elyse', u'boss': 2} 2093 | 2094 | {u'title': u'VP PS', u'_id': 8, u'allBosses': 2095 | [ 2096 | {u'level': 0L, u'_id': 1, u'name': u'Dev', u'title': u'CEO'} 2097 | ], u'name': u'Richard', u'boss': 1} 2098 | 2099 | {u'title': u'VP People', u'_id': 9, u'allBosses': 2100 | [ 2101 | {u'level': 0L, u'_id': 1, u'name': u'Dev', u'title': u'CEO'} 2102 | ], u'name': u'Kirsten', u'boss': 1} 2103 | 2104 | {u'title': u'Serf', u'_id': 10, u'allBosses': 2105 | [ 2106 | {u'title': u'CTO', u'level': 1L, u'_id': 2, u'name': u'Eliot', u'boss': 1}, 2107 | {u'level': 2L, u'_id': 1, u'name': u'Dev', u'title': u'CEO'}, 2108 | {u'title': u'VP PM', u'level': 0L, u'_id': 6, u'name': u'Ron', u'boss': 2} 2109 | ], u'name': u'Daniel', u'boss': 6} 2110 | 2111 | 2112 | Example: $graphLookup operator in aggregate. 2113 | For every employee, list all of their reports. 2114 | 2115 | {u'_id': 1, u'allReports': 2116 | [ 2117 | {u'title': u'Serf', u'level': 2L, u'_id': 10, u'name': u'Daniel', u'boss': 6}, 2118 | {u'title': u'COO', u'level': 1L, u'_id': 7, u'name': u'Elyse', u'boss': 2}, 2119 | {u'title': u'VP PM', u'level': 1L, u'_id': 6, u'name': u'Ron', u'boss': 2}, 2120 | {u'title': u'VP Eng', u'level': 1L, u'_id': 5, u'name': u'Andrew', u'boss': 2}, 2121 | {u'title': u'VP People', u'level': 0L, u'_id': 9, u'name': u'Kirsten', u'boss': 1}, 2122 | {u'title': u'CTO', u'level': 0L, u'_id': 2, u'name': u'Eliot', u'boss': 1}, 2123 | {u'title': u'CMO', u'level': 0L, u'_id': 3, u'name': u'Meagan', u'boss': 1}, 2124 | {u'title': u'CRO', u'level': 0L, u'_id': 4, u'name': u'Carlos', u'boss': 1}, 2125 | {u'title': u'VP PS', u'level': 0L, u'_id': 8, u'name': u'Richard', u'boss': 1} 2126 | ], u'name': u'Dev', u'title': u'CEO'} 2127 | 2128 | {u'title': u'CTO', u'_id': 2, u'allReports': 2129 | [ 2130 | {u'title': u'Serf', u'level': 1L, u'_id': 10, u'name': u'Daniel', u'boss': 6}, 2131 | {u'title': u'COO', u'level': 0L, u'_id': 7, u'name': u'Elyse', u'boss': 2}, 2132 | {u'title': u'VP PM', u'level': 0L, u'_id': 6, u'name': u'Ron', u'boss': 2}, 2133 | {u'title': u'VP Eng', u'level': 0L, u'_id': 5, u'name': u'Andrew', u'boss': 2} 2134 | ], u'name': u'Eliot', u'boss': 1} 2135 | 2136 | {u'title': u'VP PM', u'_id': 6, u'allReports': 2137 | [ 2138 | {u'title': u'Serf', u'level': 0L, u'_id': 10, u'name': u'Daniel', u'boss': 6} 2139 | ], u'name': u'Ron', u'boss': 2} 2140 | 2141 | 2142 | 2143 | Example: $graphLookup operator in aggregate. 2144 | Using maxDepth, listing only direct reports. 2145 | 2146 | {u'_id': 1, u'allReports': 2147 | [ 2148 | {u'title': u'VP People', u'level': 0L, u'_id': 9, u'name': u'Kirsten', u'boss': 1}, 2149 | {u'title': u'CTO', u'level': 0L, u'_id': 2, u'name': u'Eliot', u'boss': 1}, 2150 | {u'title': u'CMO', u'level': 0L, u'_id': 3, u'name': u'Meagan', u'boss': 1}, 2151 | {u'title': u'CRO', u'level': 0L, u'_id': 4, u'name': u'Carlos', u'boss': 1}, 2152 | {u'title': u'VP PS', u'level': 0L, u'_id': 8, u'name': u'Richard', u'boss': 1} 2153 | ], u'name': u'Dev', u'title': u'CEO'} 2154 | 2155 | {u'title': u'CTO', u'_id': 2, u'allReports': 2156 | [ 2157 | {u'title': u'COO', u'level': 0L, u'_id': 7, u'name': u'Elyse', u'boss': 2}, 2158 | {u'title': u'VP PM', u'level': 0L, u'_id': 6, u'name': u'Ron', u'boss': 2}, 2159 | {u'title': u'VP Eng', u'level': 0L, u'_id': 5, u'name': u'Andrew', u'boss': 2} 2160 | ], u'name': u'Eliot', u'boss': 1} 2161 | 2162 | {u'title': u'VP PM', u'_id': 6, u'allReports': 2163 | [ 2164 | {u'title': u'Serf', u'level': 0L, u'_id': 10, u'name': u'Daniel', u'boss': 6} 2165 | ], u'name': u'Ron', u'boss': 2} 2166 | 2167 | 2168 | 2169 | 2170 | 2171 | 2172 | -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/farrell0/MongoDB-Developers-Notebook/06ee13aeeb3661c1e477b988a613389f3842310f/articles/OracleVirtualBoxToRunAVMDK.pdf -------------------------------------------------------------------------------- /data_download/README.md: -------------------------------------------------------------------------------- 1 | MongoDB Developer's Notebook - Data Downloads 2 | =================== 3 | 4 | | **[Monthly Articles - 2017](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/README.md)**| **[Monthly Articles - 2016](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/2016/README.md)**| **[Data Downloads](https://github.com/farrell0/MongoDB-Developers-Notebook/blob/master/data_download/README.md)** | 5 | |-------------------------|--------------------------|-----------------| 6 | 7 | This is a personal blog where we answer one or more questions each month from MongoDB customers in a non-official, non-warranted, non much of anything forum. 8 | 9 | Content from [University.MongoDB.com](http://university.mongodb.com) - - 10 | 11 | >This blog often uses the sample data set from the University.MongoDB.com courses. 12 | > 13 | >[Zips.json](https://raw.githubusercontent.com/farrell0/MongoDB-Developers-Notebook/master/data_download/zips.json) 14 | > 15 | >[Companies.json.zip](https://raw.githubusercontent.com/farrell0/MongoDB-Developers-Notebook/master/data_download/companies.json.zip) 16 | > 17 | >[Twitter.json.zip](https://raw.githubusercontent.com/farrell0/MongoDB-Developers-Notebook/master/data_download/twitter.json.zip) 18 | 19 | USA Airline Schedule and Equipment data, and USA Historical Weather related data - - 20 | 21 | >I'm still munging this data (fixing date formats so they join, yadda), but here 22 | >are some of my favoriate Urls for pulling data on this topic- 23 | > 24 | >[USA Airline Schedule data](http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236) 25 | > 26 | >[USA Airline Equipment data](http://registry.faa.gov/aircraftinquiry/NNum_Inquiry.aspx 27 | ) 28 | > 29 | >[USA Historical Weather data](https://www.wunderground.com/history/airport) 30 | -------------------------------------------------------------------------------- /data_download/companies.json.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/farrell0/MongoDB-Developers-Notebook/06ee13aeeb3661c1e477b988a613389f3842310f/data_download/companies.json.zip -------------------------------------------------------------------------------- /data_download/twitter.json.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/farrell0/MongoDB-Developers-Notebook/06ee13aeeb3661c1e477b988a613389f3842310f/data_download/twitter.json.zip --------------------------------------------------------------------------------