SQL vs NoSQL, an experiment with MongoDB

SQL vs. NoSQL
An experiment (for dummies) with MongoDB
Marco Segato // v20170621

SUMMARY
★ What?
★ When?
★ Why (MongoDB)?
★ How?
★ :)

What?
{
The Big Data Landscape (2017),
Start from a definition,
Some NoSQL databases,
SQL vs. NoSQL differences
}

The Big Data Landscape (2017) http://mattturck.com/bigdata2017/

Start from a definition
A NoSQL (originally referring to "non SQL", "non
relational" or "not only SQL") database provides
a mechanism for storage and retrieval of data
which is modeled in means other than the
tabular relations used in relational databases.
[ https://en.wikipedia.org/wiki/NoSQL ]

SQL vs. NoSQL differences / 1
SQL
One type (SQL database) with minor variations.
Developed in 1970s to deal with first wave of
data storage applications.
MySQL, Postgres, Oracle Database.
To store information about a new data item, the
entire database must be altered, during which
time the database must be taken offline.
NoSQL
Different types including key-value stores,
document databases, wide-column stores,
and graph databases.
Developed in 2000s to deal with limitations of
SQL databases, concerning scale, replication
and unstructured data storage.
MongoDB, Cassandra, HBase, Neo4j.
Records can add new information on the fly,
and unlike SQL table rows, dissimilar data can
be stored together as necessary.
Examples
Schemas
History
Types

SQL vs. NoSQL differences / 2
SQL
Mix of open-source (e.g., Postgres, MySQL)
and closed-source (e.g., Oracle Database).
Yes, updates can be configured to
complete entirely or not at all.
Specific language using Select, Insert, and
Update statements.
NoSQL
Open-source.
In certain circumstances and at certain
levels (e.g., document level vs. database
level).
Through object-oriented APIs.Data
Manipulation
Supports
Transactions
Development
Model

When?
{
Size vs. Complexity,
Big Data,
Use cases,
NoSQL Pros and Cons
}

Big Data
One of the first reasons to use NoSQL is because you have a Big Data project to
tackle. A Big Data project is normally typified by:
● High data velocity – lots of data coming in very quickly, possibly from different locations.
● Data variety – storage of data that is structured, semi-structured and unstructured.
● Data volume – data that involves many terabytes or petabytes in size.
● Data complexity – data that is stored and managed in different locations or data centers.

Use cases
LARGE DATA VOLUMES
EXTREME QUERY WORKLOAD
SCHEMA EVOLUTION
We are storing more data now than we ever
have before.
Connections between our data are growing all
the time.
We don’t make things knowing the structure from
day 1.
Server architecture is now at a stage where we
can take advantage of it.

NoSQL Pros and Cons
PROS
MASSIVE SCALABILITY
HIGH AVAILABILITY
LOWER COST
SCHEMA FLEXIBILITY
SPARSE AND SEMI STRUCTURED
DATA
CONS
LIMITED QUERY CAPABILITIES
NOT STANDARDISED
(PORTABILITY MAY BE AN ISSUE)
STILL A DEVELOPING
TECHNOLOGY
INSTALLATION, MANAGEMENT
AND TOOLSETS STILL MATURING

Why (MongoDB)?
{
Some notes,
The leading NoSQL Database,
Who’s using MongoDB,
Main features,
TCO Comparison MongoDB & Oracle,
MongoDB University
}

Some notes
History: The software company “10gen” began developing MongoDB in 2007 as a
component of a planned platform as a service product. In 2009, the company
shifted to an open source development model, with the company offering
commercial support and other services. In 2013, “10gen” changed its name to
MongoDB Inc.
Licensing: MongoDB is available at no cost under the
GNU Affero General Public License, version 3. The
language drivers are available under an Apache
License. In addition, MongoDB Inc. offers proprietary
licenses for MongoDB.

MongoDB – The Leading NoSQL Database
NoSQL adoption (based
on Google Trends) *
LinkedIn job skills * Job trends (2015)
* https://www.mongodb.com/leading-nosql-database

Main features
Ad hoc queries - MongoDB supports field, range queries, regular expression searches.
Indexing - Fields in a MongoDB document can be indexed with primary and secondary indices.
Replication - MongoDB provides high availability with replica sets. A replica set consists of two or more copies of the data.
Load balancing - MongoDB scales horizontally using sharding. The user chooses a shard key, which determines how the data in a
collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. MongoDB can
run over multiple servers, balancing the load or duplicating data to keep the system up and running in case of hardware failure.
File storage - MongoDB can be used as a file system with load balancing and data replication features over multiple machines.
Aggregation - MapReduce can be used for batch processing of data and aggregation operations. The aggregation framework enables
users to obtain the kind of results for which the SQL GROUP BY clause is used. The aggregation framework includes the $lookup
operator which can join documents from multiple documents, as well as statistical operators such as standard deviation.
Others - In-memory Storage Engine, Native Graph Processing, Optimized Connectors for BI & Spark, Database as a Cloud Service

TCO Comparison of MongoDB & Oracle (aug-15)
Small Enterprise Project Large Enterprise Project
MongoDB Oracle MongoDB Oracle
Initial Developer Effort $ 120.000 $ 240.000 $ 360.000 $ 720.000
Initial Administrative Effort $ 10.000 $ 20.000 $ 30.000 $ 60.000
Software Licenses $ 0 $ 423.000 $ 0 $ 4.230.000
Server Hardware $ 12.000 $ 12.000 $ 120.000 $ 120.000
Storage Hardware $ 24.000 $ 125.000 $ 240.000 $ 500.000
Total Upfront Costs $ 166.000 $ 820.000 $ 750.000 $ 5.630.000
https://www.mongodb.com/collateral/total-cost-ownership-comparison-mongodb-oracle

MongoDB University
MongoDB University offers free online courses to teach you how to build and
deploy apps on MongoDB. Over 400,000 of your peers have already signed up.
https://university.mongodb.com/
M101P: MongoDB for Developers
Learn everything you need to know to get
started building a MongoDB-based app
(7 weeks).

How?
{
Battlefield and opponents,
Install & run,
Contest,
A doubt,
Tools,
Comparison,
And the winner is...
}

Battlefield and opponents
Red Hat Enterprise Linux Server v5.5
RAM 8 GB
V-CPU 1
11g Enterprise Edition 64 bit 2.6.3 Community Edition 64 bit
(current release: 12c) (current release: 3.4.6)

Install & run
Install MongoDB on linux and start the database service:
# tar -zxvf mongodb-linux-x86_64-x.y.z.tgz
# mkdir -p /data/db
# cd mongodb-linux-x86_64-x.y.z
# mongod --dbpath /data/db
JDBC connection string:
mongodb://[username:password@]host1[:port1][/[database][?options]]
Note: the port is optional, the default value is :27017 if not specified.

Contest
Table of daily sales:
INDEXES:
IDX1 C_PROD
IDX2 C_PROD, DATA
IDX3 C_ENTE, C_PROD
IDX4 DATA, C_TIPO_DOC, C_ENTE
IDX5 FLG_FIDELITY, C_PROD, C_ENTE, DATA
IDX6 TRIM("C_ENTE"), TRIM("C_PROD")
IDX7 NUM
$ mongoimport -d mydb -c sales --type csv --file
mydb_sales.csv --headerline
≈ 3.000.000 record
2’30’’ to complete the import
No index defined

A doubt
Is it correct/useful to compare them working with a typical
RDBMS' object?
● If you work in a standard legacy environment, you could even not to be
interested on databases other than RDBMS
● If you work in a futuristic start-up, you surely already moved your data
aggregation to a new strategy
but… what if your company has to manage a transitional period in which data
structure can’t be modified, but you need to move on anyway? (e.g.: due to costs,
customer requirements, warranty on data safety before final migration, etc...)

Tools
mongo shell
Robo 3T (formerly Robomongo)
the free lightweight GUI for MongoDB.
https://robomongo.org/
DBeaver, universal SQL client.
http://dbeaver.jkiss.org/

Comparison / COUNT
select count (*)
from mydb;
db.mydb.aggregate( [
{
$group: {
_id: null,
count: { $sum: 1 }
}
}
] );
19.000’’ 2.960’’

Comparison / WHERE
select data, c_prod
from mydb
where data =
to_date('26/09/2011','DD/MM/YYYY');
db.mydb.find({
"DATA": "26/09/2011"
}, {
"DATA": 1,
"C_PROD": 1
}).pretty();
0.116’’ 0.006’’

Comparison / COUNT + GROUP BY
select data, c_prod, count(c_prod)
from mydb
group by data, c_prod;
{
$group: {
_id: {data: "$DATA", c_prod: "$C_PROD"},
count: { $sum: 1 }
}
}
],
{ allowDiskUse: true }
);
2’32’’ 0’15’’

Comparison / COUNT + GROUP BY + WHERE
select data, c_prod, count(c_prod)
from mydb
where data =
to_date('26/09/2011','DD/MM/YYYY')
group by data, c_prod;
{ $match: { DATA: "26/09/2011" } },
{
$group: {
_id: {data: "$DATA", c_prod: "$C_PROD"},
count: { $sum: 1 }
}
}
],
{ allowDiskUse: true }
);
1’14’’ 0’01’’

Comparison / DISTINCT
select distinct data
from mydb;
db.mydb.distinct("DATA");
37.000’’ 2.306’’

Comparison / INSERT
insert into mydb
(NUM, C_ENTE, C_TIPO_DOC, DATA...)
Values
(-1, '67335 ', '12', TO_DATE('01/22/2015
00:00:00', 'MM/DD/YYYY HH24:MI:SS')...);
db.mydb.insert({
"NUM" : -1,
"C_ENTE" : "67335 ",
"C_TIPO_DOC" : "12",
"DATA" : "01/22/2015",
...
});
0.539’’ 0.003’’

Comparison / UPDATE
update mydb
set VALORE_01 = 5.5
where NUM = -1;
db.mydb.update(
{"NUM" : -1},
{ $set : { "VALORE_01" : 5.5}
});
0.063’’ 0.642’’

And now…
… it’s up to you!
:)
MongoDB official site: https://www.mongodb.com/
MongoDB Tools: http://mongodb-tools.com/
MongoDB Tutorial: http://www.w3resource.com/mongodb/introduction-mongodb.php

Marco Segato
Project Manager at TESISQUARE®
https://www.linkedin.com/in/marcosegato/
@machms
Passionate with #linux #opensource #innovation
My interests: #rock #reading #photo #cinema #theatre

SQL vs NoSQL, an experiment with MongoDB

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