ORE

Managing memory allocation for Oracle R Enterprise Embedded Execution

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When working with Oracle R Enterprise and particularly when you are using the ORE functions that can spawn multiple R processes, on the DB Server, you need to be very aware of the amount of memory that will be consumed for each call of the ORE function.

ORE has two sets of parallel functions for running your user defined R scripts stored in the database, as part of the Embedded R Execution feature of ORE. The R functions are called ore.groupApply, ore.rowApply and ore.indexApply. When using SQL there are “rqGroupApply” and rqRowApply. (There is no SQL function equivalent of the R function ore.indexApply)

For each parallel R process that is spawned on the DB server a certain amount of memory (RAM) will be allocated to this R process. The default size of memory to be allocated can be found by using the following query.

select name, value from sys.rq_config;

NAME                                VALUE
----------------------------------- -----------------------------------
VERSION                             1.5
MIN_VSIZE                           32M
MAX_VSIZE                           4G
MIN_NSIZE                           2M
MAX_NSIZE                           20M

The memory allocation is broken out into the amount of memory allocated for Cells and NCells for each R process.

If your parallel ORE function create a large number of parallel R processes then you can see that the amount of overall memory consumed can be significant. I’ve seen a few customers who very quickly run out of memory on their DB servers. Now that is something you do not want to happen.

How can you prevent this from happening ?

There are a few things you need to keep in mind when using the parallel enabled ORE functions. The first one is, how many R processes will be spawned. For most cases this can be estimated or calculated to a high degree of accuracy. Secondly, how much memory will be used to process each of the R processes. Thirdly, how memory do you have available on the DB server. Fourthly, how many other people will be running parallel R processes at the same time?

Examining and answering each of these may look to be a relatively trivial task, but the complexity behind these can increase dramatically depending on the answer to the fourth point/question above.

To calculate the amount of memory used during the ORE user defined R script, you can use the R garbage function to calculate the memory usage at the start and at the end of the R script, and then return the calculated amount. Yes you need to add this extra code to your R script and then remove it when you have calculated the memory usage.

gc.start <- gc(reset=TRUE)
...
gc.end <- gc()
gc.used <- gc.end[,7] - gc.start[,7] # amount consumed by the processing

Using this information and the answers to the points/questions I listed above you can now look at calculating how much memory you need to allocated to the R processes. You can set this to be static for all R processes or you can use some code to allocate the amount of memory that is needed for each R process. But this starts to become messy. The following gives some examples (using R) of changing the R memory allocations in the Oracle Database. Similar commands can be issued using SQL.

> sys.rqconfigset('MIN_VSIZE', '10M') -- min heap 10MB, default 32MB
> sys.rqconfigset('MAX_VSIZE', '100M') -- max heap 100MB, default 4GB
> sys.rqconfigset('MIN_NSIZE', '500K') -- min number cons cells 500x1024, default 1M
> sys.rqconfigset('MAX_NSIZE', '2M') -- max number cons cells 2M, default 20M

Some guidelines – as with all guidelines you have to consider all the other requirements for the Database, and in reality you will have to try to find a balance between what is listed here and what is actually possible.

  • Set parallel_degree_policy to MANUAL.
  • Set parallel_min_servers to the number of parallel slave processes to be started when the database instances start, this avoids start up time for the R processes. This is not a problem for long running processes. But can save time with processes running for 10s seconds
  • To avoid overloading the CPUs if the parallel_max_servers limit is reached, set the hidden parameter _parallel_statement_queuing to TRUE. Avoids overloading and lets processes wait.
  • Set application tables and their indexes to DOP 1 to reinforce the ability of ORE to determine when to use parallelism.

Understanding the memory requirements for your ORE processes can be tricky business and can take some time to work out the right balance between what is needed by the spawned parallel R processes and everything else that is going on in the Database. There will be a lot of trial and error in working this out and it is always good to reach out for some help. If you have a similar scenario and need some help or guidance let me know.

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Formatting results from ORE script in a SELECT statement

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This blog post looks at how to format the output or the returned returns from an Oracle R Enterprise (ORE), user defined R function, that is run using a SELECT statement in SQL.

Sometimes this can be a bit of a challenge to work out, but it can be relatively easy once you have figured out how to do it. The following examples works through some scenarios of different results sets from a user defined R function that is stored in the Oracle Database.

To run that user defined R function using a SELECT statement I can use one of the following ORE SQL functions.

  • rqEval
  • rqTableEval
  • rqGroupEval
  • rqRowEval

For simplicity we will just use the first of these ORE SQL functions to illustrate the problem and how to go about solving it. The rqEval ORE SQL function is a generate purpose function to call a user defined R script stored in the database. The function does not require any input data set and but it will return some data. You could use this to generate some dummy/test data or to find some information in the database. Here is noddy example that returns my name.

BEGIN
   --sys.rqScriptDrop('GET_NAME');
   sys.rqScriptCreate('GET_NAME',
      'function() {
         res<-data.frame("Brendan")
         res
         } ');
END;

To call this user defined R function I can use the following SQL.

select *
from table(rqEval(null,
                  'select cast(''a'' as varchar2(50))  from dual',
                  'GET_NAME') );  

For text strings returned you need to cast the returned value giving a size.

If we have a numeric value being returned we can don’t have to use the cast and instead use ‘1’ as shown in the following example. This second example extends our user defined R function to return my name and a number.

BEGIN
   sys.rqScriptDrop('GET_NAME');
   sys.rqScriptCreate('GET_NAME',
      'function() {
         res<-data.frame(NAME="Brendan", YEAR=2017)
         res
         } ');
END;

To call the updated GET_NAME function we now have to process two returned columns. The first is the character string and the second is a numeric.

select *
from table(rqEval(null,
                  'select cast(''a'' as varchar2(50)) as "NAME", 1 AS YEAR  from dual',
                  'GET_NAME') );                  

These example illustrate how you can process character strings and numerics being returned by the user defined R script.

The key to setting up the format of the returned values is knowing the structure of the data frame being returned by the user defined R script. Once you know that the rest is (in theory) easy.

How to get ORE to work with APEX

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This blog post will bring you through the steps of how to get Oracle R Enterprise (ORE) to work with APEX.

The reason for this blog posts is that since ORE 1.4+ the security model has changed for how you access and run in-database user defined R scripts using the ORE SQL API functions.

I have a series of blog posts going out on using Oracle Text, Oracle R Enterprise and Oracle Data Mining. It was during one of these posts I wanted to show how easy it was to display an R chart using ORE in APEX. Up to now my APEX environment consisted of APEX 4 and ORE 1.3. Everything worked, nice and easy. But in my new APEX environment (APEX 5 and ORE 1.5), it didn’t work. This is the calling of an in-database user defined R script using the SQL API functions didn’t work. Here is the error message that is displayed.

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So something extra was needed with using ORE 1.5. The security model around the use of in-database user defined R scripts has changed. Extra functions are now available to allow you who can run these scripts. For example we have an ore.grant function where you can grant another user the privilege to run the script.

But the problem was, when I was in APEX, the application was defined on the same schema that the r script was created in (this was the RQUSER schema). When I connect to the RQUSER schema using ORE and SQL, I was able to see and run this R script (see my previous blog post for these details). But when I was in APEX I wasn’t able to see the R script. For example, when using the SQL Workshop in APEX, I just couldn’t see the R script.

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Something strange is going on. It turns out that the view definitions for the in-database ORE scripts are defined with

owner=SYS_CONTEXT('USERENV', 'SESSION_USER');

(Thanks to the Oracle ORE team and the Oracle APEX team for their help in working out what needed to be done)

This means when I’m connected to APEX, using my schema (RQUSER), I’m not able to see any of my ORE objects.

How do you overcome this problem ?

To fix this problem, I needed to grant the APEX_PUBLIC_USER access to my ORE script.

ore.grant(name = "prepare_tm_data_2", type = "rqscript", user = "APEX_PUBLIC_USER")

Now when I query the ALL_RQ_SCRIPTS view again, using the APEX SQL Workshop, I now get the following.

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Great. Now I can see the ORE script in my schema.

Now when I run my APEX application I now get graphic produced by R, running on my DB server, and delivered to my APEX application using SQL (via a BLOB object), displayed on my screen.

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Change the size of ORE PNG graphics using in-database R functions

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In a previous blog post I showed you how create and display a ggplot2 R graphic using SQL. Make sure to check it out before reading the rest of this blog post.

In my previous blog post, I showed and mentioned that the PNG graphic returned by the embedded R execution SQL statement was not the same as what was produced if you created the graphic in an R session.

Here is the same ggplot2 graphic. The first one is what is produced in an R session and the section is what is produced by SQL query and the embedded R execution in Oracle.

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As you can see the second image (produced using the embedded R execution) gives a very square image.

The reason for this is that Oracle R Enterprise (ORE) creates the graphic image in PNG format. The default setting from this is 480 x 480. You will find this information when you go digging in the R documentation and not in the Oracle documentation.

So, how can I get my ORE produced graphic to appear like what is produced in R?

What you need to do is to change the height and width of the PNG image produced by ORE. You can do this by passing parameters in the SQL statement used to call the user defined R function, that in turn produces the ggplot2 image.

In my previous post, I gave the SQL statement to call and produce the graphic (shown above). One of the parameters to the rqTableEval function was set to null. This was because we didn’t have any parameters to pass, apart from the data set.

We can replace this null with any parameters we want to pass to the user defined R function (demo_ggpplot). To pass the parameters we need to define them using a SELECT statement.

cursor(select 500 as "ore.png.height", 850 as "ore.png.width" from dual),

The full SELECT statement now becomes

select *
from table(rqTableEval( cursor(select * from claims),
                        cursor(select 500 as "ore.png.height", 850 as "ore.png.width" from dual),
                        'PNG',
                        'demo_ggpplot'));

When you view the graphic in SQL Developer, you will get something that looks a bit more like what you would expect or want to see.

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For each graphic image you want to produce using ORE you will need to figure out that are the best PNG height and width settings to use. Plus it also depends on what tool or application you are going to use to display the images (eg. APEX etc)

Oracle Text, Oracle R Enterprise and Oracle Data Mining – Part 1

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A project that I’ve been working on for a while now involves the use of Oracle Text, Oracle R Enterprise and Oracle Data Mining. Oracle Text comes with your Oracle Database licence. Oracle R Enterprise and Oracle Data Mining are part of the Oracle Advanced Analytics (extra cost) option.

What I will be doing over the course of 4 or maybe 5 blog posts is how these products can work together to help you gain a grater insight into your data, and part of your data being large text items like free format text, documents (in various forms e.g. html, xml, pdf, ms word), etc.

Unfortunately I cannot show you examples from the actual project I’ve been working on (and still am, from time to time). But what I can do is to show you how products and components can work together.

In this blog post I will just do some data setup. As with all project scenarios there can be many ways of performing the same tasks. Some might be better than others. But what I will be showing you is for demonstration purposes.

The scenario: The scenario for this blog post is that I want to extract text from some webpages and store them in a table in my schema. I then want to use Oracle Text to search the text from these webpages.

Schema setup: We need to create a table that will store the text from the webpages. We also want to create an Oracle Text index so that this text is searchable.

drop sequence my_doc_seq;
create sequence my_doc_seq;

drop table my_documents;

create table my_documents (
doc_pk number(10) primary key, 
doc_title varchar2(100), 
doc_extracted date, 
data_source varchar2(200), 
doc_text clob);

create index my_documents_ot_idx on my_documents(doc_text) 
indextype is CTXSYS.CONTEXT;

In the table we have a number of descriptive attributes and then a club for storing the website text. We will only be storing the website text and not the html document (More on that later). In order to make the website text searchable in the DOC_TEXT attribute we need to create an Oracle Text index of type CONTEXT.

There are a few challenges with using this type of index. For example when you insert a new record or update the DOC_TEXT attribute, the new values/text will not be reflected instantly, just like we are use to with traditional indexes. Instead you have to decide when you want to index to be updated. For example, if you would like the index to be updated after each commit then you can create the index using the following.

create index my_documents_ot_idx on my_documents(doc_text) 
indextype is CTXSYS.CONTEXT
parameters ('sync (on commit)');

Depending on the number of documents you have being committed to the DB, this might not be for you. You need to find the balance. Alternatively you could schedule the index to be updated by passing an interval to the ‘sync’ in the above command. Alternatively you might want to use DBMS_JOB to schedule the update.

To manually sync (or via DBMS_JOB) the index, assuming we used the first ‘create index’ statement, we would need to run the following.

EXEC CTX_DDL.SYNC_INDEX('my_documents_ot_idx');

This function just adds the new documents to the index. This can, over time, lead to some fragmentation of the index, and will require it to the re-organised on a semi-regular basis. Perhaps you can schedule this to happen every night, or once a week, or whatever makes sense to you.

BEGIN
  CTX_DDL.OPTIMIZE_INDEX('my_documents_ot_idx','FULL');
END;

(I could talk a lot more about setting up some basics of Oracle Text, the indexes, etc. But I’ll leave that for another day or you can read some of the many blog posts that already exist on the topic.)

Extracting text from a webpage using R: Some time ago I wrote a blog post on using some of the text mining features and packages in R to produce a word cloud based on some of the Oracle Advanced Analytics webpages.

I’m going to use the same webpages and some of the same code/functions/packages here.

The first task you need to do is to get your hands on the ‘htmlToText function. You can download the htmlToText function on github. This function requires the ‘Curl’ and ‘XML’ R packages. So you may need to install these.

I also use the str_replace_all function (“stringer’ R package) to remove some of the html that remains, to remove some special quotes and to replace and occurrences of ‘&’ with ‘and’.

# Load the function and required R packages
source(“c:/app/htmltotext.R”)
library(stringr)

data1 <- str_replace_all(htmlToText("http://www.oracle.com/technetwork/database/options/advanced-analytics/overview/index.html"), "[\r\n\t\"\'\u201C\u201D]" , "")
data1 <- str_replace_all(data1, "&", "and")
data2 <- str_replace_all(str_replace_all(htmlToText("http://www.oracle.com/technetwork/database/options/advanced-analytics/odm/index.html"), "[\r\n\t\"\'\u201C\u201D]" , ""), "&", "and")
data2 <- str_replace_all(data2, "&", "and")
data3 <- str_replace_all(str_replace_all(htmlToText("http://www.oracle.com/technetwork/database/database-technologies/r/r-technologies/overview/index.html"), "[\r\n\t\"\'\u201C\u201D]" , ""), "&", "and")
data3 <- str_replace_all(data3, "&", "and")
data4 <- str_replace_all(str_replace_all(htmlToText("http://www.oracle.com/technetwork/database/database-technologies/r/r-enterprise/overview/index.html"), "[\r\n\t\"\'\u201C\u201D]" , ""), "&", "and")
data4 <- str_replace_all(data4, "&", "and")

We now have the text extracted and cleaned up.

Create a data frame to contain all our data: Now that we have the text extracted, we can prepare the other data items we need to insert the data into our table (‘my_documents’). The first stept is to construct a data frame to contain all the data.

data_source = c("http://www.oracle.com/technetwork/database/options/advanced-analytics/overview/index.html",
                 "http://www.oracle.com/technetwork/database/options/advanced-analytics/odm/index.html",
                 "http://www.oracle.com/technetwork/database/database-technologies/r/r-technologies/overview/index.html",
                 "http://www.oracle.com/technetwork/database/database-technologies/r/r-enterprise/overview/index.html")
doc_title = c("OAA_OVERVIEW", "OAA_ODM", "R_TECHNOLOGIES", "OAA_ORE")
doc_extracted = Sys.Date()
data_text <- c(data1, data2, data3, data4)

my_docs <- data.frame(doc_title, doc_extracted, data_source, data_text)

Insert the data into our database table: With the data in our data fram (my_docs) we can now use this data to insert into our database table. There are a number of ways of doing this in R. What I’m going to show you here is how to do it using Oracle R Enterprise (ORE). The thing with ORE is that there is no explicit functionality for inserting and updating records in a database table. What you need to do is to construct, in my case, the insert statement and then use ore.exec to execute this statement in the database.

Accessing the R datasets in ORE and SQL

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When you install R you also get a set of pre-compiled datasets. These are great for trying out many of the features that are available with R and all the new packages that are being produced on an almost daily basis.

The exact list of data sets available will depend on the version of R that you are using.

To get the list of available data sets in R you can run the following.

> library(help="datasets")

This command will list all the data sets that you can reference and start using immediately.

I’m currently running the latest version of Oracle R Distribution version 3.2. See the listing at the end of this blog post for the available data sets.

But are these data sets available to you if you are using Oracle R Enterprise (ORE)? The answer is Yes of course they are.

But are these accessible on the Oracle Database server? Yes they are, as you have R installed there and you can use ORE to access and use the data sets.

But how? how can I list what is on the Oracle Database server using R? Simple use the following ORE code to run an embedded R execution function using the ORE R API.

What? What does that mean? Using the R on your client machine, you can use ORE to send some R code to the Oracle Database server. The R code will be run on the Oracle Database server and the results will be returned to the client. The results contain the results from the server. Try the following code.

ore.doEval(function() library(help="datasets")) 

# let us create a functions for this code
myFn <- function() {library(help="datasets")}

# Now send this function to the DB server and run it there.
ore.doEval(myFn)

# create an R script in the Oracle Database that contains our R code
ore.scriptDrop("inDB_R_DemoData")
ore.scriptCreate("inDB_R_DemoData", myFn)
# Now run the R script, stored in the Oracle Database, on the Database server
#   and return the results to my client
ore.doEval(FUN.NAME="inDB_R_DemoData")

Simple, Right!

Yes it is. You have shown us how to do this in R using the ORE package. But what if I’m a SQL developer. Can I do this in SQL? Yes you can. Connect you your schema using SQL Developer/SQL*Plus/SQLcl or whatever tool you will be using to run SQL. Then run the following SQL.

select * 
from table(rqEval(null, 'XML', 'inDB_R_DemoData'));

This SQL code will return the results in XML format. You can parse this to extract and display the results and when you do you will get something like the following listing, which is exactly the same that is produced when you run the R code that I gave above.

So what this means is that evening if you have an empty schema with no data in it, and as long as you have the privileges to run embedded R execution, you actually have access to all these different data sets. You can use these to try our R using the ORE SQL APIs too.

		Information on package ‘datasets’

Description:

Package:       datasets
Version:       3.2.0
Priority:      base
Title:         The R Datasets Package
Author:        R Core Team and contributors worldwide
Maintainer:    R Core Team 
Description:   Base R datasets.
License:       Part of R 3.2.0
Built:         R 3.2.0; ; 2015-08-07 02:20:26 UTC; windows

Index:

AirPassengers           Monthly Airline Passenger Numbers 1949-1960
BJsales                 Sales Data with Leading Indicator
BOD                     Biochemical Oxygen Demand
CO2                     Carbon Dioxide Uptake in Grass Plants
ChickWeight             Weight versus age of chicks on different diets
DNase                   Elisa assay of DNase
EuStockMarkets          Daily Closing Prices of Major European Stock
                        Indices, 1991-1998
Formaldehyde            Determination of Formaldehyde
HairEyeColor            Hair and Eye Color of Statistics Students
Harman23.cor            Harman Example 2.3
Harman74.cor            Harman Example 7.4
Indometh                Pharmacokinetics of Indomethacin
InsectSprays            Effectiveness of Insect Sprays
JohnsonJohnson          Quarterly Earnings per Johnson & Johnson Share
LakeHuron               Level of Lake Huron 1875-1972
LifeCycleSavings        Intercountry Life-Cycle Savings Data
Loblolly                Growth of Loblolly pine trees
Nile                    Flow of the River Nile
Orange                  Growth of Orange Trees
OrchardSprays           Potency of Orchard Sprays
PlantGrowth             Results from an Experiment on Plant Growth
Puromycin               Reaction Velocity of an Enzymatic Reaction
Theoph                  Pharmacokinetics of Theophylline
Titanic                 Survival of passengers on the Titanic
ToothGrowth             The Effect of Vitamin C on Tooth Growth in
                        Guinea Pigs
UCBAdmissions           Student Admissions at UC Berkeley
UKDriverDeaths          Road Casualties in Great Britain 1969-84
UKLungDeaths            Monthly Deaths from Lung Diseases in the UK
UKgas                   UK Quarterly Gas Consumption
USAccDeaths             Accidental Deaths in the US 1973-1978
USArrests               Violent Crime Rates by US State
USJudgeRatings          Lawyers' Ratings of State Judges in the US
                        Superior Court
USPersonalExpenditure   Personal Expenditure Data
VADeaths                Death Rates in Virginia (1940)
WWWusage                Internet Usage per Minute
WorldPhones             The World's Telephones
ability.cov             Ability and Intelligence Tests
airmiles                Passenger Miles on Commercial US Airlines,
                        1937-1960
airquality              New York Air Quality Measurements
anscombe                Anscombe's Quartet of 'Identical' Simple Linear
                        Regressions
attenu                  The Joyner-Boore Attenuation Data
attitude                The Chatterjee-Price Attitude Data
austres                 Quarterly Time Series of the Number of
                        Australian Residents
beavers                 Body Temperature Series of Two Beavers
cars                    Speed and Stopping Distances of Cars
chickwts                Chicken Weights by Feed Type
co2                     Mauna Loa Atmospheric CO2 Concentration
crimtab                 Student's 3000 Criminals Data
datasets-package        The R Datasets Package
discoveries             Yearly Numbers of Important Discoveries
esoph                   Smoking, Alcohol and (O)esophageal Cancer
euro                    Conversion Rates of Euro Currencies
eurodist                Distances Between European Cities and Between
                        US Cities
faithful                Old Faithful Geyser Data
freeny                  Freeny's Revenue Data
infert                  Infertility after Spontaneous and Induced
                        Abortion
iris                    Edgar Anderson's Iris Data
islands                 Areas of the World's Major Landmasses
lh                      Luteinizing Hormone in Blood Samples
longley                 Longley's Economic Regression Data
lynx                    Annual Canadian Lynx trappings 1821-1934
morley                  Michelson Speed of Light Data
mtcars                  Motor Trend Car Road Tests
nhtemp                  Average Yearly Temperatures in New Haven
nottem                  Average Monthly Temperatures at Nottingham,
                        1920-1939
npk                     Classical N, P, K Factorial Experiment
occupationalStatus      Occupational Status of Fathers and their Sons
precip                  Annual Precipitation in US Cities
presidents              Quarterly Approval Ratings of US Presidents
pressure                Vapor Pressure of Mercury as a Function of
                        Temperature
quakes                  Locations of Earthquakes off Fiji
randu                   Random Numbers from Congruential Generator
                        RANDU
rivers                  Lengths of Major North American Rivers
rock                    Measurements on Petroleum Rock Samples
sleep                   Student's Sleep Data
stackloss               Brownlee's Stack Loss Plant Data
state                   US State Facts and Figures
sunspot.month           Monthly Sunspot Data, from 1749 to "Present"
sunspot.year            Yearly Sunspot Data, 1700-1988
sunspots                Monthly Sunspot Numbers, 1749-1983
swiss                   Swiss Fertility and Socioeconomic Indicators
                        (1888) Data
treering                Yearly Treering Data, -6000-1979
trees                   Girth, Height and Volume for Black Cherry Trees
uspop                   Populations Recorded by the US Census
volcano                 Topographic Information on Auckland's Maunga
                        Whau Volcano
warpbreaks              The Number of Breaks in Yarn during Weaving
women                   Average Heights and Weights for American Women

Oracle Advanced Analytics in the Oracle Cloud

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You have heard about the cloud? Right? Even the Oracle Cloud?

If you haven’t, then maybe we need to look at how you can learn more about the Oracle Cloud.

Over the past while, and in the past few weeks in particular, Oracle has been advertising about how you can get a trail Oracle cloud service setup for FREE. Well it is free for one month when you set it up on the Oracle website (cloud.oracle.com).

As I like to talk about and use the Oracle Advanced Analytics (OAA) option (a lot), I thought I’d just give you some pointers on how to use OAA on the Oracle cloud.

To do this you need to set up an account on the Oracle cloud website (your Oracle single sign on should help with making that process a lot quicker). There are lots of websites and blog that will talk/show you through the process. Then you need to select what Database as a Service that you want to setup

OAA is not available on the Database Schema Service just yet (maybe one day they will)

Although Oracle Advanced Analytics comes pre-installed in the Oracle Enterprise Edition database (yes it is a separately priced option) when you install it on your own servers, but for the Enterprise Edition DaaS OAA is not part of it.

DaaS has the following versions

  • Standard Edition Service 
  • Enterprise Edition Service 
  • High Performance Service 
  • Extreme Performance Service

OAA is only available for these last two versions of the DaaS.

High Performance DaaS: Multitenant, Partitioning, Real Application Testing, Advanced Compression, Advanced Security, Label Security, Database Vault, OLAP, Advanced Analytics, Spatial and Graph, Diagnostics Pack, Tuning Pack, Database Lifecycle Management Pack, Data Masking & Subsetting Pack and Cloud Management Pack for Oracle Database.

Extreme Performance DaaS: In-Memory Database, RAC (Real Application Clusters), Active Data Guard, Multitenant, Partitioning, Real Application Testing, Advanced Compression, Advanced Security, Label Security, Database Vault, OLAP, Advanced Analytics, Spatial and Graph, Diagnostics Pack, Tuning Pack, Database Lifecycle Management Pack, Data Masking & Subsetting Pack and Cloud Management Pack for Oracle Database.

Oracle Advanced Analytics has two main products or components. The first is the in-database Oracle Data Mining features. This are part of the High Performance and Extreme Performance DaaS offerings. But Oracle R Enterprise is not installed on these DaaS. Well if kind of is if you can get an 11g DaaS, but at time of writing this post ORE is not part of the 12c DaaS images. So you will need to factor in some time to go and install ORE, if you need to use it.

I’ve been lucky to have one of these DaaS with OAA trials and with thanks to Thomas Kurian he has extended these trials to 12 months for all Oracle ACE Directors. Thank you Thomas.

When you get your DaaS setup you just need to configure your connection privileges, ssh, etc and away you go. All you need to do is to move your data across the internet to your own Oracle DaaS, and once it is in the DaaS all your OAA and other analytics is performed on the Database Server. Only the results are returned to you and displayed in your tool. This significantly reduces the processing time for your data and removes the need to constantly extract your data (in whole or in parts) to feed into other advanced analytics tools.

So if you haven’t tried Oracle Advanced Analytics yet, then go ahead and setup your free trial of Oracle DaaS and try it out. You never know what you might discover by using Oracle Advanced Analytics (in the cloud)

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