程序代写代做代考 data structure database chain SQL Microsoft PowerPoint – Spatial Data Management – Week 9 – Advanced Topics 3 002

Microsoft PowerPoint – Spatial Data Management – Week 9 – Advanced Topics 3 002

Spatial Data Management –
Advanced Topics 3 – NoSQL and

Blockchain

• Dr Claire Ellul

• c.ellul@ucl.ac.uk

Big Data

• There is much more data – and lots of it is
spatial!

– Twitter, Facebook

– Sensors e.g. Crossrail vibration sensors for
tunnelling, EveryAware Air Quality sensors

– Traffic/Congestion cameras

– Online shopping/delivery services

– Bicycle hire

Big Data

• What is Big Data …

• https://www.youtube.com/watch?v=Hv397
JnNWYc (from IBM but covers the basic
principles well)

Overview

• Managing Big Data in a Relational Database

– Distributing the Data and Replication

– Adjusting Block Size

• Beyond Relational Databases

– NoSQL

– NoSQL and Spatial Data

– Blockchain

Managing Big Spatial Data

• The slowest operation in any computer is
the time taken to read data from a hard
drive and to write data to a hard drive

• Therefore, a good part of optimising
database performance focusses on
minimising the number of reads/writes
that take place

Managing Big Spatial Data

• One way to do this is to use multiple disks,
so that the read/write operations can take
place in parallel (at the same time).

• If you use one disk for the data and
another for the indexes, the system can be
reading data at the same time as it is
reading indexes, which is more efficient

Managing Big Spatial Data

• You can even distribute your data onto
more than two disks if you have them.

– Think about which tables are read more often
and put them on separate disks.

Managing Big Spatial Data

• “Partitioning allows a table, index, or index-organized table to
be subdivided into smaller pieces, where each piece of such a
database object is called a partition. Each partition has its
own name, and may optionally have its own storage
characteristics.” (https://docs.oracle.com/cd/B28359_01/server.111/b32024/partition.htm)

Managing Big Spatial Data

https://docs.oracle.com/cd/B28359_01/server.111/b32024/partition.htm

Managing Big Spatial Data

• Horizontal partitioning
– Put different rows from the same table onto different disks as

different tables:
• for example customers with postcodes beginning with A-H are stored in one table and those from I

onwards in another

– A view (which creates a ‘fake’ table by joining two or more sub
tables) can then be created to make the two tables

– A view appears to SQL as a table
• However it runs at RUN TIME so the join query to merge the two tables may take a little more time,

although the data retrieval will be quicker as the two hard drives can operate in parallel

• https://www.itprotoday.com/sql-server/horizontal-and-vertical-partitioning

Managing Big Spatial Data

• Horizontal partitioning
– Put all the temperature data from Pearson sensors in 1 table and

from Chadwick in another table
create table assets.temperature_values_pearson (

Temperature_value_id serial,

Temperature_sensor_id integer

Date_and_time date,

Value_degrees_c numeric (5,2));

create table assets.temperature_values_chadwick (

Temperature_value_id serial,

Temperature_sensor_id integer

Date_and_time date,

Value_degrees_c numeric (5,2));

Managing Big Spatial Data

• Horizontal partitioning
– Then create a view to link the two (a view is a ‘stored’ SQL

statement that can be treated like a table

CREATE VIEW assets.temperature_values AS

SELECT * FROM assets.temperature_values_pearson

UNION ALL

SELECT * FROM assets.temperature_values_chadwick;

SELECT * FROM assets.temperature_values;

Managing Big Spatial Data

• Vertical partitioning
– Split a table into two or more tables, by placing some of the columns on

one disk and the remainder on another.

– This goes beyond normalisation (which splits tables into smaller ones to
reduce data duplication) and actually splits the fully normalised data
again.

– You could chose to put rapidly changing columns on a faster hard drive
than columns where data does not change often.

– A view can then be used to re-unite the split tables

• http://cloudgirl.tech/data-partitioning-vertical-horizontal-hybrid-partitioning/

Managing Big Spatial Data

• Replication

– Rather than just split the data/tables, you make
COPIES of the entire database on different servers

– It is sometimes difficult to keep all the data
synchronised, in particular where you have a very high
level of inserts/updates/deletes per second

– However this is very useful when there is high demand
for the data

• And also serves as a backup

Managing Big Spatial Data

• Types of Replication:

– Active replication is performed by processing
the same request at every replica

• i.e. if you delete a row it is deleted simultaneously
on all servers

• Useful if you want to maintain a high level of
service as if one replica fails the others still exist

• However, needs more resources

Managing Big Spatial Data

• Types of Replication:

– Passive replication involves processing each
single request on a single replica and then
transferring its resultant state to the other
replicas

• This is sometimes known as mirroring

• In a basic system, one server receives all the
requests and then transmits changes to the backups

• In a more advanced system, each server receives
requests and transmits changes to all the others

Managing Big Spatial Data

• Distributed load systems

– A consequence of replication is a distributed
database in which users can access data
relevant to their tasks without interfering with
the work of others.

Overview

• Managing Big Data in a Relational Database

– Distributing the Data and Replication

– Adjusting Block Size

• Beyond Relational Databases

– NoSQL

– NoSQL and Spatial Data

– Blockchain

Managing Big Spatial Data

• Block Size

– Is the size on disk of one ‘block’ of data – i.e.
the amount of data that is read by one ‘read’
operation of the hard disk

Managing Big Spatial Data

https://www.usenix.org/legacy/event/fast05/tech/schlosser/schlosser_html/disk-adjacent-blocks.png

Managing Big Spatial Data

• Block Size

– Differs between different databases

• In PostgreSQL, block size is set up when you install
the software.

• In Oracle you can set it up when you create a
database and different parts of the database can
have different block sizes.

Managing Big Spatial Data

• Why is block size important?

– As mentioned above, the slowest part of any
computer operation is the time taken to read
data from a hard drive into the computer’s
memory (where it can then be used for
querying).

– The larger the block the fewer of these read
operations are needed …

Managing Big Spatial Data

• Block size

– The standard block size in PostgreSQL is 8kB
(kilo-bytes). (A maximum of 32kB can be set)

– This is particularly important for spatial data,
because some spatial objects may be quite
large

– 1 double number takes 8 bytes of storage ->
i.e. one x, y or z coordinate requires 8 bytes ..

Managing Big Spatial Data

• Block Size

– So you can only have 1000 coordinates in your
8KB disk read operation

• That is 500 coordinate pairs, which for many
geometry objects is low.

– Of course, this is assuming that you don’t also
want to see the attributes: each character
also requires 1 byte of storage

Managing Big Spatial Data

• Block Size

– However, there are some situations – e.g.
financial transactions on a bank account –
where the data required to be read is very
small, and a 2KB block size would be more
appropriate

• You can have 250 numbers in your 2KB disk read
operation which is still too many for one trasaction

– .

Managing Big Spatial Data

• Block Size

– A balance needs to be found between differing
uses of your database:

• OLTP databases – on line transaction processing –
such as banks have many read/write operations
onto disk per second

– Thus, block size should be smaller

• Decision Support Systems – DSS – read large
quantities of data and then perform analysis on the
data.

– This requires larger block sizes so that several rows of
data can be read at the same time.

Managing Big Spatial Data

• Block Size

– NB: Be careful with spatial data, as ‘row
chaining’ – where a single row of data does not
fit into one block – can happen.

• If it does, you require more than one hard disk read
operation to get the data into memory, which can
be very slow!

Overview

• Managing Big Data in a Relational Database

– Distributing the Data and Replication

– Adjusting Block Size

• Beyond Relational Databases

– NoSQL

– NoSQL and Spatial Data

– Blockchain

Managing Big Spatial Data

• Some data is structured in traditional
relational databases

– Most of this module has been about how to do
that

• But some data – e.g. documents, videos,
pictures and so forth is unstructured
– E.g. web pages can have text and images anywhere,

word documents don’t all have the same headers and
sub sections

Managing Big Spatial Data

• Why did NoSQL evolve

– You are a search engine company and you
realise that you have access to huge reams of
data – all the web pages on the internet

– These are unstructured so difficult to manage
and monetize (i.e. make profit for your
company)

– So you need new approaches to doing this

Managing Big Spatial Data

• NoSQL databases have evolved to help with this
challenge

• NoSQL = ‘not only SQL’

• NoSQL databases are still DBMS – so
authentication, backup, security etc still valid

Managing Big Spatial Data

• NoSQL Databases

– Don’t structure data in a relational format, the
way we have seen so far

– Rather, items are grouped into more useful
groupings

Managing Big Spatial Data

• NoSQL Databases

– Are designed for maximum access and speed of
response

– Are able to run on very large clusters of low-
powered computers

– Do not adhere to ACID principles (see next
slides)

Managing Big Spatial Data

• Terminology – ACID

– Atomicity

• if one part of a transaction fails, it all fails

– Consistency

• Any change to data will adhere to all the rules in
the database (primary keys, foreign keys,
constraints)

Managing Big Spatial Data

• Terminology – ACID

– Isolation

• If two transactions are executed at the same time,
the result would be the same as if they are
executed one after the other

– Durability

• Once the transaction is complete it doesn’t change
even if power is lost

What is NoSQL

• https://www.youtube.com/watch?v=qUV2j
3XBRHc

Managing Big Spatial Data

• Types of NoSQL Databases

– Key Value – use a hash table to store a key
with a pointer to a particular item of data

– Simple to implement

• Very simple data structure, always 2 columns

• http://rebelic.nl/2011/05/28/the-four-categories-of-nosql-databases/

Key Value Database

What is a Key-Value Database?

Key Value Database

https://stackshare.io/amazon-dynamodb/in-stacks

Managing Big Spatial Data

• Types of NoSQL Databases

– Document Databases

• Similar to key/value stores but consist of
hierarchies of key/value pairs (nested key/value
pairs)

• The semi-structured documents are stored in
formats such as JSON

• Support more efficient querying than key/value
pairs

– You can drill down through the structure
– http://rebelic.nl/2011/05/28/the-four-categories-of-nosql-databases/

Managing Big Spatial Data

JSON Example
{

“myName”: “Fred”,

“lastName”: “Sanger”,

“address”: {

“streetAddress”: “25 Hinxton Hall”,

“city”: “London”,

“Country”: “GB”,

“postalCode”: W2 1PG

},

“phoneNumbers”: [

“44 0208 3345456”,

“44 0207 876789”

]

}

Document Databases

https://www.mongodb.com/who-uses-mongodb

Managing Big Spatial Data

• Types of NoSQL Databases

– Graph Databases

• A graph model is used (like a road network) to link
data together

• Used to store information about networks, such as
social connections

– http://rebelic.nl/2011/05/28/the-four-categories-of-nosql-databases/

Graph
Databases

https://neo4j.com/customers/

Managing Big Spatial Data

• Types of NoSQL Databases

– Column Family Store

• Also uses keys, but the keys point to multiple
columns of data, which may be arranged across
multiple machines

– So, 1 key, multiple values

• Like a database table, but each row can have a
different number of columns

– http://rebelic.nl/2011/05/28/the-four-categories-of-nosql-databases/

Column Store

Overview

• Managing Big Data in a Relational Database

– Distributing the Data and Replication

– Adjusting Block Size

• Beyond Relational Databases

– NoSQL

– NoSQL and Spatial Data

– Blockchain

Managing Big Spatial Data

• NoSQL Databases

– Don’t offer a consistent SQL-type query interface –
each query interface is different

– Relatively new to market, so fewer tools such as
PGAdmin 4 or FME to handle the data

• Most of the work is done at command line

– However, if GeoJSON can be created (served) then it
is possible to visualise spatial data

• GeoJSON is a de-facto industry standard for sharing spatial
data

Geospatial in MongoDB

• Document database

• Offers 2D coordinate handling and spatial
indexing

– Allows storage of spatial objects as geoJSON

• Also allows some spatial queries
• No 3D

https://docs.mongodb.com/manual/reference/geojson/#multipolygon, 5th November 2018

https://docs.mongodb.com/manual/geospatial-queries/#id1, 5th November 2018

Geospatial in MongoDB

• Coordinate reference systems are sort-of
handled – but not the wide range you find
in RDBMS

– Geometry can either be ‘on the plane’ (i.e.
flat, projected) or ‘on the sphere’ (using
latitude/longitude)

Geospatial in Neo4J

• Graph database

• Spatial supported via an
add-on library (only point
data in the core
installation)
– Not clear if being updated

frequently but last release
on 7th August this year

Geospatial in Neo4J

https://neo4j-contrib.github.io/spatial/0.24-neo4j-3.1/index.html#spatial-indexing, 5th November 2018

• Data types – points and WKT

Geospatial in Neo4J

https://neo4j-contrib.github.io/spatial/0.24-neo4j-3.1/index.html#_jts_queries, 5th November 2018

Geospatial in Neo4J

• Core system seems to support WGS84 and
projected data

• Import functionality for shapefiles and OSM
data

• Also links to GeoServer (spatial data
management and publication software)

Overview

• Managing Big Data in a Relational Database

– Distributing the Data and Replication

– Adjusting Block Size

• Beyond Relational Databases

– NoSQL

– NoSQL and Spatial Data

– Blockchain

Blockchain

• “The blockchain is an incorruptible digital
ledger of economic transactions that can
be programmed to record not just financial
transactions but virtually everything of
value.”

Don & Alex Tapscott, authors
Blockchain Revolution (2016)

https://blockgeeks.com/guides/what-is-blockchain-technology/

Blockchain

• “Blockchain is a public electronic ledger
that can be openly shared among disparate
users and that creates an unchangeable
record of their transactions, each one
time-stamped and linked to the previous
one.”

https://www.computerworld.com/article/3191077/security/what-is-blockchain-the-most-disruptive-tech-in-decades.html

• “A block chain is a type of database that takes a number
of records and puts them in a block (rather like collating
them on to a single sheet of paper). Each block is then
‘chained’ to the next block, using a cryptographic
signature.

• This allows block chains to be used like a ledger, which
can be shared and corroborated by anyone with the
appropriate permissions.”
– A ledger is a record of transactions – e.g. in accounts

Blockchain

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/492972/gs-16-1-
distributed-ledger-technology.pdf

Blockchain Features

• Decentralized data – more than one copy
of the data

• Distributed ledger

– No middle man

– Tamperproof (very difficult to hack)

• Write once, append only (no delete unless
you control the network)

• The real novelty of block chain technology is that it is more than just
a database — it can also set rules about a transaction (business logic)
that are tied to the transaction itself.

• This contrasts with conventional databases, in which rules are often
set at the entire database level
– In an RDBMS one set of rules applies for the entire schema

• Accuracy of the ledger can be checked by consensus (the term
‘mining’ is used for a variant of this process in the cryptocurrency
Bitcoin)

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/492972/gs-16-1-
distributed-ledger-technology.pdf

Blockchain

• If the consensus process is open to everyone, the ledger
is unpermissioned
– Unpermissioned ledgers such as Bitcoin have no single owner

• The purpose of an unpermissioned ledger is to allow
anyone to contribute data to the ledger and for everyone
in possession of the ledger to have identical copies.

• No-one can prevent a transaction from being added to
the ledger
– Full, open, consensus process

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/492972/gs-16-1-
distributed-ledger-technology.pdf

Blockchain

• If the consensus process is not open to everyone, the ledger is
permissioned

– Permissioned ledgers may have one or many owners.

• For a permissioned ledger, entries are checked by trusted authorities
— government departments or banks

• Permissioned block chains provide highly-verifable data sets because
the consensus process creates a digital signature, which can be seen
by all parties.

• Requiring government departments to validate a record could give a
high degree of confidence in the record’s security

– Current data sharing using paper is very open to forgery

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/492972/gs-16-1-
distributed-ledger-technology.pdf

Blockchain

https://www.g2crowd.com/categories/blockchain

Blockchain

• https://www.youtube.com/watch?v=4sm5L
NqL5j0&feature=youtu.be (5 mins 14
seconds)

Overview

• Managing Big Data in a Relational Database

– Distributing the Data and Replication

– Adjusting Block Size

• Beyond Relational Databases

– NoSQL

– NoSQL and Spatial Data

– Blockchain

Other Sources of Information

• https://www.youtube.com/watch?v=h_xINowGU1
4

• http://www.youtube.com/watch?v=a1tc0tPY7oE

• https://www.linkedin.com/learning/advanced-
nosql-for-data-science/the-limits-of-relational-
databases

• https://www.linkedin.com/learning/advanced-
nosql-for-data-science/types-of-nosql-databases