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[1Z0-238] Oracle EBS R12 Certified Specialist Everything You Must Know About

Online Apps DBA - Sun, 2020-02-16 02:29

The Oracle E-Business Suite R12 Applications Database Administrator Certified Professional Certification is designed for individuals who possess a strong foundation and expertise in implementing Oracle E-Business Suite solutions. This exam is required as part of earning the new Oracle EBS R12 Applications Database Administrator Certified Professional certification. Check Out K21 Academy’s blog post at https://k21academy.com/appsdba65 […]

The post [1Z0-238] Oracle EBS R12 Certified Specialist Everything You Must Know About appeared first on Oracle Trainings for Apps & Fusion DBA.

Categories: APPS Blogs

Average of 0 and Value - gives incorrect output. Is there a way to ignore the 0 during the average function.

Tom Kyte - Fri, 2020-02-14 21:11
Hi Tom, I am having a SQL output as follows. <code>A B C D E ---------------- ---------- ---------- ------------ ----------- 2020-02-12 221 68677 99.6...
Categories: DBA Blogs

Indexing strategy for dates in a query

Tom Kyte - Fri, 2020-02-14 21:11
Hello, Ask Tom team. I have the following query: <code>SELECT guid, sender_id, doc, status, arrived_date, register_date, last_updated_date FROM user1.table1 WHERE (sender_id=:SENDER OR :SENDER IS NULL ) AND (status=:STATUS OR :status IS NU...
Categories: DBA Blogs

Left padded String based on sub-string length

Tom Kyte - Fri, 2020-02-14 21:11
Hi Chris, I want to restrict the length of the input string to 8 characters by adjusting all the digits (after 'MFT' in below example) from Input string. Means, want to accommodate all the digits in string. Ex.1. Input String is 'MFT123456' ...
Categories: DBA Blogs

AWR records top 30 SQLs by default

Bobby Durrett's DBA Blog - Fri, 2020-02-14 16:22

I forget that Oracle’s AWR only records the top 30 SQL statements in each snapshot by default. I am not sure how long this link will last but here is a 19c manual page describing the default: 19c manual – see the topnsql setting. A lot of my query tuning assumes that the problem query is in the AWR but for very efficient queries on active systems they may mysteriously disappear or be absent from the AWR. It sometimes takes me a while to remember that the snapshots only include a fixed number of SQLs.

I use my sqlstat3.sql query to look at a history of a particular sql_id’s executions. Often it shows the query running faster on one plan_hash_value than another. Then I look at why the sql_id changed plans. But what about when the good plan does not show up at all? Several times I have looked at sqlstat3.sql output and thought that a query had not run in the past with an efficient query even though it had. It had run so efficiently that it was not on the report, so it looked like the query was a new, slow, SQL statement.

Often I will fix a query’s plan with a SQL Profile and rerun sqlstat3.sql on a busy system after manually running dbms_workload_repository.create_snapshot to capture the most recent activity and the problem query with the new plan will not show up. Usually I remember that it is not in the top 30 queries and that is why it is missing but sometimes I forget. Here is a partial sqlstat3.sql output showing a long running SQL disappearing after I fixed its plan on Wednesday:

SQL_ID        PLAN_HASH_VALUE END_INTERVAL_TIME         EXECUTIONS_DELTA Elapsed Ave ms
------------- --------------- --------------------- ---------------- ------------------
acn0557p77na2      3049654342 12-FEB-20 05.00.01 AM                1          16733.256
acn0557p77na2      3049654342 12-FEB-20 06.00.03 AM                2           49694.32
acn0557p77na2      3049654342 12-FEB-20 07.00.53 AM                6          47694.527
acn0557p77na2      3049654342 12-FEB-20 08.00.54 AM               11         50732.0651
acn0557p77na2      3049654342 12-FEB-20 09.00.33 AM               15         53416.5183
acn0557p77na2      3049654342 12-FEB-20 10.00.43 AM               21         86904.4385
acn0557p77na2      3049654342 12-FEB-20 11.00.02 AM               27          84249.859
acn0557p77na2      3049654342 12-FEB-20 12.00.20 PM               27         125287.757
acn0557p77na2      3049654342 12-FEB-20 01.00.36 PM               69         156138.176

Sometimes I query the V$ tables to verify it is currently running a good plan. Here is example output from vsqlarea.sql showing the good plan running today.

LAST_ACTIVE         SQL_ID        PLAN_HASH_VALUE Avg Elapsed ms
------------------- ------------- --------------- --------------
2020-02-14 16:11:40 acn0557p77na2       867392646             14

This is just a quick note to me as much as anyone else. A query that is missing from an AWR report or my sqlstat3.sql report may not have run at all, or it may have run so well that it is not a top 30 query.

Bobby

Categories: DBA Blogs

Char problems

Jonathan Lewis - Fri, 2020-02-14 09:25

The semantics of comparing character columns of different types can lead to some confusion, so before I get into the main body of this note here’s a little test based on a table with one row:


create table t1(c2 char(2), c3 char(3), vc2 varchar2(2), vc3 varchar2(3));

insert into t1 values ('XX','XX','XX','XX');
commit;

select count(*) c2_c3   from t1 where c2 = c3;
select count(*) c2_vc3  from t1 where c2 = vc3;
select count(*) c3_vc2  from t1 where c3 = vc2;
select count(*) c3_vc3  from t1 where c3 = vc3;

I’ve inserted one row, using the same value for every single column; then I’ve been counting the row(s) where various pairs of columns match. Which (if any) of the four queries return the value 1 and which (if any) return the value zero ?

To help you, here’s a quote from the relevant Oracle manual about character comparison semantics:

Blank-Padded and Nonpadded Comparison Semantics

With blank-padded semantics, if the two values have different lengths, then Oracle first adds blanks to the end of the shorter one so their lengths are equal. Oracle then compares the values character by character up to the first character that differs. The value with the greater character in the first differing position is considered greater. If two values have no differing characters, then they are considered equal. This rule means that two values are equal if they differ only in the number of trailing blanks. Oracle uses blank-padded comparison semantics only when both values in the comparison are either expressions of data type CHAR, NCHAR, text literals, or values returned by the USER function.

With nonpadded semantics, Oracle compares two values character by character up to the first character that differs. The value with the greater character in that position is considered greater. If two values of different length are identical up to the end of the shorter one, then the longer value is considered greater. If two values of equal length have no differing characters, then the values are considered equal. Oracle uses nonpadded comparison semantics whenever one or both values in the comparison have the data type VARCHAR2 or NVARCHAR2.

The first two queries return 1, the second two return zero.

  1. Query 1: c2 is blank padded to match c3 in length before the comparison, so the values are ‘XX {space}’
  2. Query 2: c2 is not padded, so the compared values are both ‘XX’
  3. Query 3: c3 is three characters long, vc2 is only 2 characters long and does not get padded to match c3
  4. Query 4: c3 is three characters long, vc3 is only 2 characters long and does not get padded to match c3

One interesting by-product of this example is this:

  • c3 = c2 and c2 = vc3 but c3 != vc3     whatever happened to transitive closure!

So we come to the point of the article, which is this:

Be very careful about using char() (or nchar) types in your tables – especially if you’re thinking of using columns of type [n]char() in join predicates (or predicates that don’t start life as join predicates but become join predicates through transitive closure).

Here’s an interesting bug that has appeared (I think) as a side effect of the need for blank-padded semantics. We start with two tables that I’ll be joining with a hash join – one of them will be a small table that will be used as the “build” table, the other will be (faked to look like) a large table that will be used as the “probe” table.


rem
rem     Script:         bloom_prune_char_bug.sql
rem     Author:         Jonathan Lewis
rem     Dated:          Jan 2020
rem
rem     Last tested 
rem             19.3.0.0
rem             12.2.0.1
rem

create table test_probe(status char(3)) partition by list(status) (partition st_1 values('00','BI'));

create table test_build(status char(2)); 

insert into test_build values('00');
insert into test_probe values('00');
insert into test_build values('BI');
insert into test_probe values('BI');

commit;
 
prompt  =====================================
prompt  Fake large table stats for test_probe
prompt  =====================================

exec dbms_stats.set_table_stats(null,'test_probe',numrows=>2000000);

spool bloom_prune_char_bug
set linesize 156
set pagesize 60

set serveroutput off

select  /*+ 
                gather_plan_statistics 
        */
        * 
from 
        test_build b,
        test_probe a 
where 
        a.status = b.status
;

select * from table(dbms_xplan.display_cursor(null,null,'projection partition allstats last'))
/


The two tables have a pair of matching rows – so the query should return two rows. But it doesn’t – it returns no rows, and the clue about why not is in the execution plan (which I’ve pulled from memory with lots of extra bits and pieces). Here’s the output from running this script (from the query onwards) on an instance of 12.2.0.1:


no rows selected


PLAN_TABLE_OUTPUT
-----------------------------------------------------------------
SQL_ID  2295z4p6m4557, child number 0
-------------------------------------
select /*+   gather_plan_statistics  */  * from  test_build b,
test_probe a where  a.status = b.status

Plan hash value: 177769189

--------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                | Name       | Starts | E-Rows | Pstart| Pstop | A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
--------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT         |            |      1 |        |       |       |      0 |00:00:00.01 |       7 |       |       |          |
|*  1 |  HASH JOIN               |            |      1 |   2000K|       |       |      0 |00:00:00.01 |       7 |  2078K|  2078K|  766K (0)|
|   2 |   PART JOIN FILTER CREATE| :BF0000    |      1 |      2 |       |       |      2 |00:00:00.01 |       7 |       |       |          |
|   3 |    TABLE ACCESS FULL     | TEST_BUILD |      1 |      2 |       |       |      2 |00:00:00.01 |       7 |       |       |          |
|   4 |   PARTITION LIST SINGLE  |            |      1 |   2000K|KEY(AP)|KEY(AP)|      0 |00:00:00.01 |       0 |       |       |          |
|   5 |    TABLE ACCESS FULL     | TEST_PROBE |      0 |   2000K|     1 |     1 |      0 |00:00:00.01 |       0 |       |       |          |
--------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("A"."STATUS"="B"."STATUS")

Column Projection Information (identified by operation id):
-----------------------------------------------------------
   1 - (#keys=1) "B"."STATUS"[CHARACTER,2], "A"."STATUS"[CHARACTER,3]
   2 - INTERNAL_FUNCTION("B"."STATUS")[2], INTERNAL_FUNCTION("B"."STATUS")[2], "B"."STATUS"[CHARACTER,2]
   3 - "B"."STATUS"[CHARACTER,2]
   4 - (rowset=256) "A"."STATUS"[CHARACTER,3]
   5 - (rowset=256) "A"."STATUS"[CHARACTER,3]

Note
-----
   - dynamic statistics used: dynamic sampling (level=2)

The optimizer has used a Bloom filter to do partition pruning, and while we can see operation 4 reporting a “partition list single” operation using “and pruning” (AP), we can see that operation 5 reports zero starts. This is because the Bloom filter has been used to determine that there are no relevant partitions!

Looking down at the (rarely examined) projection information we can see why – operation 2 (the “part join filter create”) has a strange “Internal Function” in its projection, and references B.STATUS as character[2]. It looks as if the Bloom filter that identifies partitions has been built using a char(2) as the input to its hashing function – which is bad news when the resulting filter is used to check the hash values returned from the partition definition that is hash a char(3).

If my thoughts about the mismatch in how the Bloom filters for the build and probe tables are built then a test that would help to confirm the hypothesis would be disable Bloom filter pruning – which you can only do by setting a hidden parameter, possibly in a hint or SQL Patch):

select 
        /*+ 
                gather_plan_statistics 
                opt_param('_bloom_pruning_enabled','false') 
        */  
        * 
from 
        test_build b,
        test_probe a 
where
        a.status = b.status;

select * from table(dbms_xplan.display_cursor(null,null,'projection partition allstats last'))
/


ST STA
-- ---
00 00
BI BI

2 rows selected.


PLAN_TABLE_OUTPUT
------------------------------------------------------------------------
SQL_ID  9whuurpkm3wpw, child number 0
-------------------------------------
select  /*+   gather_plan_statistics
opt_param('_bloom_pruning_enabled','false')   subquery_pruning(a)  */
* from  test_build b,  test_probe a where  a.status = b.status

Plan hash value: 787868928

------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation              | Name       | Starts | E-Rows | Pstart| Pstop | A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |            |      1 |        |       |       |      2 |00:00:00.01 |      45 |       |       |          |
|*  1 |  HASH JOIN             |            |      1 |   2000K|       |       |      2 |00:00:00.01 |      45 |  2078K|  2078K|  866K (0)|
|   2 |   TABLE ACCESS FULL    | TEST_BUILD |      1 |      2 |       |       |      2 |00:00:00.01 |       7 |       |       |          |
|   3 |   PARTITION LIST SINGLE|            |      1 |   2000K|     1 |     1 |      2 |00:00:00.01 |      38 |       |       |          |
|   4 |    TABLE ACCESS FULL   | TEST_PROBE |      1 |   2000K|     1 |     1 |      2 |00:00:00.01 |      38 |       |       |          |
------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("A"."STATUS"="B"."STATUS")

Column Projection Information (identified by operation id):
-----------------------------------------------------------
   1 - (#keys=1) "B"."STATUS"[CHARACTER,2], "A"."STATUS"[CHARACTER,3]
   2 - (rowset=256) "B"."STATUS"[CHARACTER,2]
   3 - (rowset=256) "A"."STATUS"[CHARACTER,3]
   4 - (rowset=256) "A"."STATUS"[CHARACTER,3]

Note
-----
   - dynamic statistics used: dynamic sampling (level=2)

Adding the hint opt_param(‘_bloom_pruning_enabled’,’false’) to the query we get the right results and, of course, we can see that there is no operation in the execution plan to generate and use the Bloom filter that is probably causing the problem.

Conclusion

If you are going to use char() types in your tables, and if you are going to compare columns of type char() make sure that the columns are defined to be exactly the same length – or that you include an explicit cast() to guarantee that the shorter column appears to be the same length as the longer column.

Footnote

This bug appeared in my MOS “hot topics”email a couple of days ago as

Bug 27661222: WRONG RESULTS WITH PARTITION PRUNING COMPARING CHAR COLUMNS OF DIFFERENT LENGTH

Reading the bug note the problem is described as a bug in “AND pruning” with a workaround of setting the hidden parameter “_and_pruning_enabled” to false (possibly through the opt_param() hint). I suspect that the underlying problem may be the Bloom filter itself and that disabling Bloom filter pruning for the query may be a slightly less aggressive workaround.

The bug is reported as fixed in 20.1 – but you don’t need to upgrade just yet because, apart from the workarounds, there are various patches available back to 19.x and 12.2.

The sample script above is basically the example in the bug note with a few minor changes.

 

 

 

Why commit/rollback or any DDL command not allowed in trigger or function?

Tom Kyte - Thu, 2020-02-13 21:10
Hi Tom, Theoretically I know that commit/rollback/DDL or anything that causes transaction to end are not allowed in a trigger and function if calling function in SQL statement. To use any of those in trigger/function we can use PRAGMA AUTONOMOUS T...
Categories: DBA Blogs

Want to replace a particular string with a null value

Tom Kyte - Thu, 2020-02-13 21:10
We have 2 types of record_data format in table speedwing table 1st type --> <code>..NTP ID MT20190125 - NTP PRODUCT META BASIC FIX 2019 - TRAVEL START DATE 24/01/2019 - TRAVEL END DATE 31/12/20' ..NTP ID MT20190125 - NTP PRODUCT META BASIC FIX 2...
Categories: DBA Blogs

Roles granted to other roles

Tom Kyte - Thu, 2020-02-13 21:10
Is it true that roles can not be granted to other roles anymore? I am unable to find documentation of this, but was informed that this was taken away in 12c. If this is true, will you please post the document?
Categories: DBA Blogs

DBMS_UTILITY.FORMAT_CALL_STACK Change in 12.2 and later

Bobby Durrett's DBA Blog - Thu, 2020-02-13 15:47

Quick note. During my 11.2.0.4 to 19c upgrade that I have been writing about we found a difference in behavior of DBMS_UTILITY.FORMAT_CALL_STACK. I tested it on several versions, and it switched in 12.2. Now it puts the procedure name within the package in the stack.

Old output:

----- PL/SQL CALL STACK -----
  OBJECT      LINE  OBJECT
  HANDLE    NUMBER  NAME
0X15BFA6930         9  PACKAGE BODY MYUSER.MYPKG
0X10C988058         1  ANONYMOUS BLOCK

New output:

----- PL/SQL CALL STACK -----
  OBJECT      LINE  OBJECT
  HANDLE    NUMBER  NAME
0XA796DF28         9  PACKAGE BODY MYUSER.MYPKG.MYPROC
0X7ADFEEB8         1  ANONYMOUS BLOCK

Test code:

select * from v$version;

CREATE OR REPLACE PACKAGE MYPKG
AS

PROCEDURE MYPROC;

END MYPKG;
/
SHOW ERRORS

CREATE OR REPLACE PACKAGE BODY MYPKG
AS

PROCEDURE MYPROC
IS

BEGIN

DBMS_OUTPUT.PUT_LINE(UPPER(dbms_utility.format_call_stack));

END MYPROC;

END MYPKG;
/
SHOW ERRORS;

execute mypkg.myproc;
show errors;

Might be useful to someone else. We had some code that depended on the package name being the last thing on its line, but the new version includes the name of the procedure after the package name.

Bobby

Categories: DBA Blogs

Centra Health Delivers Superior Patient Care with Oracle Cloud Applications

Oracle Press Releases - Thu, 2020-02-13 08:00
Press Release
Centra Health Delivers Superior Patient Care with Oracle Cloud Applications Prominent hospital network in Virginia moves finance, procurement, human resources, and supply chain applications to the cloud to increase efficiency and improve business insights

Redwood Shores, Calif.—Feb 13, 2020

To advance its mission of providing excellent care for life, Centra Health has selected Oracle Cloud Applications. With a complete and integrated suite of applications to manage its finance, procurement, HR and supply chain, Centra Health will be able to increase productivity, improve controls, drive down costs and enhance overall business insights.

Founded in 1987, Centra Health is a regional nonprofit healthcare system with 8,400 staff that serves more than 500,000 people in 70 locations in central and southern Virginia. To continue to deliver on its mission and provide the best possible services for its patients, Centra Health needed to replace its aging and disparate on-premises applications with an integrated suite of applications that would meet its functional needs while driving down costs. After extensive review, Centra selected Oracle Enterprise Resource Planning (ERP) Cloud, Oracle Human Capital Management (HCM) Cloud, and Oracle Supply Chain Management (SCM) Cloud to support its vision for improved, affordable healthcare.

“When determining the appropriate vendor to support our business, we used a multidisciplinary process to ensure our needs were accurately quantified and comprehensively vetted,” said Tom Lawton, vice president and chief resource officer at Centra Health. “Oracle surfaced as the ERP vendor that checked all of the boxes, and our leadership felt confident that Oracle could support both our current and future needs. We are also anticipating additional value from the relationship with Oracle by having a common platform across our HR, finance, and supply chain operations.”

With Oracle ERP Cloud, Oracle HCM Cloud, and Oracle SCM Cloud, Centra Health will be able to take advantage of the cloud to break down organizational silos, standardize processes, and manage data from its finance, supply chain and HR teams on a single integrated cloud platform. In addition, by providing a common user interface across all functional areas, Oracle Cloud Applications will help Centra Health increase employee engagement, collaboration, and performance. To drive better business results and ensure its team stays focused on providing the best possible patient care, Centra Health will also leverage Oracle’s embedded healthcare and technology expertise, best practices, and third-party partnerships.

“Whether its evolving regulations, the shifting payer-provider dynamic, advances in medicine, or increasing customer expectations, hospitals are struggling to stay ahead of accelerating change,” said Rajan Krishnan, group vice president of product development, Oracle. “Oracle Cloud Applications will help Centra Health outpace all of this change and stay ahead of customer expectations by providing proven, best-of-breed applications across every business function.”

Contact Info
Bill Rundle
Oracle
415.990.3348
bill.rundle@oracle.com
About Oracle

The Oracle Cloud offers a complete suite of integrated applications for Sales, Service, Marketing, Human Resources, Finance, Supply Chain and Manufacturing, plus Highly Automated and Secure Generation 2 Infrastructure featuring the Oracle Autonomous Database. For more information about Oracle (NYSE: ORCL), please visit us at www.oracle.com.

Trademarks

Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

Talk to a Press Contact

Bill Rundle

  • 415.990.3348

Oracle Advanced Analytics Help Banks Battle Financial Crime

Oracle Press Releases - Thu, 2020-02-13 07:00
Press Release
Oracle Advanced Analytics Help Banks Battle Financial Crime New capabilities increase compliance program effectiveness

LONDON, U.K.—Feb 13, 2020

With $5.7 billion in global money laundering fines issued in 2019[i], growing threat sophistication, and rising compliance costs, financial institutions need advanced analytics to deter financial crime. To help banks meet this challenge, Oracle Financial Crime and Compliance Management (FCCM) suite of products now includes an integrated analytics workbench, 300-plus customer risk indicators, and embedded graph analytics visualizations. These capabilities build on Oracle’s more than 20 years of market leadership and innovation to help financial institutions fight money laundering and achieve compliance.

“Financial crimes are increasingly more sophisticated as technology becomes more advanced,” said John Edison, vice president, Financial Crime & Compliance Products, Oracle Financial Services. “Oracle continues to make strategic investments in the area of anti-money laundering and financial crime compliance management to help financial institutions successfully fight these threats. By seamlessly incorporating advanced analytics capabilities into our enterprise-grade platform, financial institutions can quickly overcome adoption impediments and benefit from cutting-edge innovations at scale. This allows their compliance teams to boost their accuracy and efficiency, which is crucial when fighting financial crime and keeping customers safe.”

These new advanced features can help chief compliance officers and data scientists to increase overall program effectiveness, detection accuracy and investigation efficiency through:

  • An integrated analytics workbench that allows data scientists to run graph analytics, data visualizations, machine modeling, scenario authoring, and testing on all of their data in one place;
  • The ability to augment traditional rules-based behavior detection models with machine-based models and 300-plus out-of-the-box customer risk indicators, which enhance the accuracy of models and reduce false positives;
  • Embedded graph analytics visualizations and 30-plus pre-built graph algorithms for advanced case investigations, entity resolution, and discovery of hidden networks. Graph analytics capabilities also support network pattern analysis and deep learning to automate case decisions and provide recommendations to investigators.
 

Global financial institutions continue to select Oracle for its enterprise-grade, anti-financial crime platform, which is regulator-accepted and based on a common data foundation that takes inputs from any transaction system. This single source of truth enables data sciences’ teams to consume data and leverage the advanced analytics to monitor, detect and investigate as needed. With Oracle FCCM, compliance teams can also increase overall program effectiveness, and optimize compliance operations—at scale.

In 2019, Oracle was named a Category Leader in the Chartis RiskTech Quadrant® for:

 

[i] According to statistics at AMLabc.com

Contact Info
Judi Palmer
Oracle Corporation
+1 650.784.7901
judi.palmer@oracle.com
Jack Rankin
CMG
+44 207 067 0823
JRankin@cmgrp.com
About Oracle Financial Services

Oracle Financial Services provides solutions for retail banking, corporate banking, payments, asset management, life insurance, annuities and healthcare payers. With our comprehensive set of integrated digital and data platforms, banks and insurers are empowered to deliver next generation financial services. We enable customer-centric transformation, support collaborative innovation and drive efficiency. Our data and analytical platforms help financial institutions drive customer insight, integrate risk and finance, fight financial crime and comply with regulations. To learn more visit our website at https://www.oracle.com/industries/financial-services/index.html.

About Oracle

The Oracle Cloud offers a complete suite of integrated applications for Sales, Service, Marketing, Human Resources, Finance, Supply Chain and Manufacturing, plus Highly Automated and Secure Generation 2 Infrastructure featuring the Oracle Autonomous Database. For more information about Oracle (NYSE: ORCL), please visit us at www.oracle.com.

Trademarks

Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

Talk to a Press Contact

Judi Palmer

  • +1 650.784.7901

Jack Rankin

  • +44 207 067 0823

Unable to load jar using dbms_java.loadjava in Oracle

Tom Kyte - Thu, 2020-02-13 03:10
I need to upload a jar file in Oracle RDBMS using dbms_java.loadjava method. I have granted all the required permission and able to run below function successfully. <code>create or replace function get_java_property(prop in varchar2) return varch...
Categories: DBA Blogs

LINESIZE and displaying data on a screen : the biggest part of execution time?

Tom Kyte - Thu, 2020-02-13 03:10
Hello Masters, I have one big question about the SQL*Plus parameter LINESIZE and the display of datas. I read in documentation Oracle 19 SQL*Plus : https://docs.oracle.com/en/database/oracle/oracle-database/19/sqpug/sqlplus-users-guide-and-refe...
Categories: DBA Blogs

Transform IF ELSE END IF TO CASE WHEN

Tom Kyte - Thu, 2020-02-13 03:10
I need to use PL-SQL or SQL to do my query. First: I want to transform all the IF..END IF used in the body of function F_CALCUL_TAUX to CASE..WHEN..END, and use the result inside my query. Secundo: It's possible to transform that and use it inside ...
Categories: DBA Blogs

Function for alphabetical sequence like a spreadsheet

Tom Kyte - Thu, 2020-02-13 03:10
I need function which convert numeric to alphabet like when I input 1 then it will return 'A', when i input 2 then it will return 'B' please help me on this.
Categories: DBA Blogs

Copyright Caveat Emptor

Oracle Press Releases - Wed, 2020-02-12 16:15
Blog
Copyright Caveat Emptor

Ken Glueck, Executive Vice President, Oracle—Feb 12, 2020

Today, Oracle submitted our response brief before the United States Supreme Court in Oracle v. Google. Over the next few weeks, we will post short blogs regarding our decade-long litigation against Google’s theft of Java software. We will make our legal arguments before the Supreme Court in March. Here, we will try to set the record straight on the facts and policy, which have been wildly distorted by Google and its PR machine led by Google’s head of Global Affairs and Chief Legal Officer, Kent Walker.

It is noteworthy that United States Solicitor General Noel Francisco will file a brief and argue at the Supreme Court hearing on Oracle’s behalf. It is also noteworthy that President Obama’s Solicitor General Don Verrilli also filed a brief with the Supreme Court in 2015 on Oracle’s behalf, confirming that strong support for copyright is not a question of political ideology nor is it a particularly controversial part of the law.

There will also be numerous Amicus Briefs filed shortly on the side of strong copyright protection for expressive and creative works including computer software. One brief, filed by the Songwriters Guild will state: “There are untold riches in running the Internet of other people’s things.” Only a songwriter could so eloquently capture the essence of this case, and Google’s business practices. We wish we would have thought of that line ourselves, but we didn’t, so we repeat it here (with credit and permission).

Before we get to the facts of the case, we need to start with what Google actually does for a living, which isn’t always well understood. Google is an advertising company. Period. Sure, it uses technology to facilitate its advertising and data collection, but nearly all of its money—and its nearly $1 Trillion market capitalization—comes from plain old-fashioned advertising. Think Don Draper, not da Vinci. The fact is, the fewer constraints placed on other people’s innovations, creations or content, the more money Google makes. While weak protection for content might be great for Google’s bottom line, it is antithetical to everything we know about how to promote innovation, creativity, and growth in the United States.

In this country, protection of intellectual property is the unassailable driver of our economic prosperity. Confiscatory or overly permissive IP regimes that depend on trying to copy or appropriate others’ innovations inevitably lead to technological stagnation, which helps explain why despite massive state subsidies, China remains a follower in software. As others have said, “strip mining intellectual property is not a road to growth.” And we should be cautious about taking copyright input from a company whose entire business model is based on monetizing other people’s property.

Yet, this is exactly the argument we see with Google’s appeal to the Supreme Court. Google is asking the Supreme Court to recast bedrock and long-standing IP principles for its own narrow self-interest. It casts its appeal as a defense of the innovation economy; it portrays itself as the protector of interoperability; it constantly misrepresents what is meant by software interface (which is fact-bound in this case); and redefines “fair use” to include nearly all commercial copying. It pretends that more than 11,000 lines of software code is something other than “software”. It ignores the fact that Google conceded it killed—not enabled—interoperability in Android. And it actually argues with a straight face that verbatim copying for use in one of the world’s most successful commercial products—Android—was meant to be covered by fair use, meaning the exception now truly eats the rule.

Google then conjures an innovation narrative that attempts to redefine the past decade as one of stagnation and economic decline, when in fact this past decade may be the most innovative on record, defined by advancements in software, networking and interoperability. Cloud, 5G, artificial intelligence, machine learning, quantum, mobile, autonomy, blockchain have all exploded at precisely the time Google claims this case is killing innovation. Growth in software is at an all-time high. In fact, notwithstanding the arguments of Google and its allies, there is not a scintilla of evidence suggesting that anyone actually believes Oracle v. Google impacts innovation. Oracle v. Google stands for the simple proposition that stealing—no matter the convenience it may offer to the thief—is not acceptable.

Most troubling, Google has spent tens of millions of dollars propagating its view of the world through law schools, think tanks and paid so-called opinion leaders, none of which seem to acknowledge that the advertising elephant driving all of this is sitting right in the room. There is nothing altruistic about Google’s decades-long assault on intellectual property protection. It’s only about Google’s self-interest … and money. And whether it’s authors, musicians, moviemakers, or publishers, Google has used its market power and vast resources to essentially outrun, outlast, and outspend copyright holders.

Until now.

Let’s review the facts as they occurred and not permit Google to engage in historical revisionism to serve its own interests.

  1. 1. Google was last to the mobile market, behind Apple, Microsoft, Blackberry, Nokia, and a number of other start-ups like Danger, which had created the T-Mobile Sidekick. Google understood that search and advertising were moving from desktop to mobile and it needed to be a major player in the mobile market, or it would lose its advertising golden goose.

  2. 2. Google could have built a platform from scratch to compete with Windows Mobile, Apple iOS, or Blackberry, as those companies did, but given its late start it didn’t have the time to spend writing a new platform and convincing independent developers to embrace it. So Google turned to Sun Microsystems and Java. Java at the time was revolutionary. It was wildly popular, “open,” and practically the definition of interoperable. It was democratizing computing by enabling developers to “write once, run anywhere.” It was developed by Sun. And, yes, it was protected by copyright and licensed to other companies.

  3. 3. Java was made available by Sun under a number of licensing options, including an open source license, a specification license and a commercial license. Developers could of course develop applications using Java with no license at all. Platform and device makers who wanted to run those Java applications licensed Java for their products. A virtual who’s who of technology companies licensed Java under one of those three regimes. Of particular note, Danger, run by Andy Rubin (who was later hired by Google to run Android) had licensed Java for his company’s T-Mobile Sidekick—a popular early smartphone.

  4. 4. Google could have taken a license for Java as well, but it had a problem. It wanted to use Java because of its popularity among app developers, but it did not want interoperability and it didn’t want open source. It wanted to turn “write once, run anywhere” into “write once, run only on Android.” Google certainly didn’t want applications written for Android to be easily portable to Windows, Apple, or any potential new entrant to the market.

  5. 5. Google evaluated the alternatives to Java and decided they “all suck.” So, it decided to steal Java anyway even if it meant “making enemies along the way.” It went ahead and copied verbatim more than 11,000 lines of Java software code and expropriated it for its own clearly commercial use.

  6. 6. Google understood exactly what it was doing. In addition to the new head of Google’s Android division, Andy Rubin, who had licensed Java when running Danger, Google’s CEO was Eric Schmidt. Mr. Schmidt was of course the Vice President of Software at Sun during Java’s development.

  7. 7. What’s even worse than the harm caused to Sun by stealing Java, Google immediately executed its plan to kill interoperability, also harming millions of developers who counted on Java interoperability to lower the costs of app development.

Google then went straight to the “catch me if you can” chapter of its playbook, assuming that Sun would never stand up to Google’s theft. They were mistaken.

Given Google’s problem with the undisputed facts, during the intervening decade, Google has exercised every bit of its vast resources to attempt to change the law retroactively, to little avail. Since it is not in dispute that Google copied verbatim more than 11,000 lines of code, it attempts to carve this code out of copyright by claiming this particular code is not copyrightable in the first place.

In page after page of policy arguments in its brief, Google asks the Supreme Court to draw some ill-defined line between a made-up category of source code it calls “interfaces” and other source code. Google’s problem is that the Copyright Act makes no such distinction, and policy makers around the world have largely rejected Google’s views and have opted for the status quo, which protects all software code under the Copyright Act. Most policy makers fully understand that Google’s interests are far afield from the interests of actual innovators and creators.

As if Google’s views on copyrightability are not brazen enough, Google then attempts to claim that its theft and clear commercial use of an existing technology already in the market is somehow covered by the fair use doctrine. Such a proposition based on these facts would virtually eliminate copyright altogether because nearly all copying would be “fair”.

Then Google takes us down a road of made up doctrines pulled out of thin air, leaving us with a wall covered in spaghetti. One of the most extraordinary is the concept that the more popular a work is, the less copyright protection it deserves because its free availability would be in the public interest. Seriously? That is closely followed by the bizarre concept that it is standard practice in the industry to freely copy other innovators’ source code. This appears to be the doctrine of “everyone does it.” In fact, what everyone does is safeguard their innovations with intellectual property protections, which most often result in incentives to work cooperatively with mutually beneficial licensing regimes. We then get a dose of the “convenience” principle which, if we even understand their point, goes something like, “we only did it for the convenience of others,” painting itself as something of a Sir Robin of Software. Last, we are told that innovators who are granted constitutional and statutory “monopolies” to their innovations are … wait for it … monopolists. Those living in glass houses shouldn’t throw kettles.

But the most insulting argument Google makes is to paint itself as the standard bearer for openness and interoperability, which is historical revisionism at its finest. Sun, and the thousands of engineers who participated in the development of Java, stood for openness and interoperability. Oracle has maintained that principled stance. Java is among the most important software innovations in history. Google stole it, broke interoperability, baked it into its proprietary, tightly controlled Android operating environment, and has made tens of billions of dollars on the back of Sun’s revolutionary innovation.

Leading us back to where we started: “There are untold riches in running the Internet of other people’s things.”

Next week we will address Google’s Amicus Briefs.

Oracle Announces Oracle Cloud Data Science Platform

Oracle Press Releases - Wed, 2020-02-12 07:05
Press Release
Oracle Announces Oracle Cloud Data Science Platform

REDWOOD SHORES, Calif.—Feb 12, 2020

New service makes it quick and easy for data science teams to collaboratively build and deploy powerful machine learning models New Python support for machine learning algorithms within the Oracle Autonomous Database reduces the need to move data Seven new services, including new data catalog, to discover, find, organize, enrich and create data assets; new big data service delivers a full Cloudera Hadoop implementation; new service provides SQL access to HDFS; new fully managed service to run Apache Spark applications


Oracle today announced the availability of the Oracle Cloud Data Science Platform. At the core is Oracle Cloud Infrastructure Data Science, helping enterprises to collaboratively build, train, manage and deploy machine learning models to increase the success of data science projects. Unlike other data science products that focus on individual data scientists, Oracle Cloud Infrastructure Data Science helps improve the effectiveness of data science teams with capabilities like shared projects, model catalogs, team security policies, reproducibility and auditability. Oracle Cloud Infrastructure Data Science automatically selects the most optimal training datasets through AutoML algorithm selection and tuning, model evaluation and model explanation.

Today, organizations realize only a fraction of the enormous transformational potential of data because data science teams don’t have easy access to the right data and tools to build and deploy effective machine learning models. The net result is that models take too long to develop, don’t always meet enterprise requirements for accuracy and robustness and too frequently never make it into production.

“Effective machine learning models are the foundation of successful data science projects, but the volume and variety of data facing enterprises can stall these initiatives before they ever get off the ground,” said Greg Pavlik, senior vice president product development, Oracle Data and AI Services. “With Oracle Cloud Infrastructure Data Science, we’re improving the productivity of individual data scientists by automating their entire workflow and adding strong team support for collaboration to help ensure that data science projects deliver real value to businesses.”

Designed for Data Science Teams and Scientists

Oracle Cloud Infrastructure Data Science includes automated data science workflow, saving time and reducing errors with the following capabilities:

  • AutoML automated algorithm selection and tuning automates the process of running tests against multiple algorithms and hyperparameter configurations. It checks results for accuracy and confirms that the optimal model and configuration is selected for use. This saves significant time for data scientists and, more importantly, is designed to allow every data scientist to achieve the same results as the most experienced practitioners.
  • Automated predictive feature selection simplifies feature engineering by automatically identifying key predictive features from larger datasets.
  • Model evaluation generates a comprehensive suite of evaluation metrics and suitable visualizations to measure model performance against new data and can rank models over time to enable optimal behavior in production. Model evaluation goes beyond raw performance to take into account expected baseline behavior and uses a cost model so that the different impacts of false positives and false negatives can be fully incorporated.
  • Model explanation: Oracle Cloud Infrastructure Data Science provides automated explanation of the relative weighting and importance of the factors that go into generating a prediction. Oracle Cloud Infrastructure Data Science offers the first commercial implementation of model-agnostic explanation. With a fraud detection model, for example, a data scientist can explain which factors are the biggest drivers of fraud so the business can modify processes or implement safeguards.
 

Getting effective machine learning models successfully into production needs more than just dedicated individuals. It requires teams of data scientists working together collaboratively. Oracle Cloud Infrastructure Data Science delivers powerful team capabilities including:

  • Shared projects help users organize, enable version control and reliably share a team’s work including data and notebook sessions.
  • Model catalogs enable team members to reliably share already-built models and the artifacts necessary to modify and deploy them.
  • Team-based security policies allow users to control access to models, code and data, which are fully integrated with Oracle Cloud Infrastructure Identity and Access Management.
  • Reproducibility and auditability functionalities enable the enterprise to keep track of all relevant assets, so that all models can be reproduced and audited, even if team members leave.
 

With Oracle Cloud Infrastructure Data Science, organizations can accelerate successful model deployment and produce enterprise-grade results and performance for predictive analytics to drive positive business outcomes.

Comprehensive Data and Machine Learning Services

The Oracle Cloud Data Science Platform includes seven new services that deliver a comprehensive end-to-end experience designed to accelerate and improve data science results:

  • Oracle Cloud Infrastructure Data Science: Enables users to build, train and manage new machine learning models on Oracle Clou using Python and other open-source tools and libraries including TensorFlow, Keras and Jupyter.
  • Powerful New Machine Learning Capabilities in Oracle Autonomous Database: Machine learning algorithms are tightly integrated in Oracle Autonomous Database with new support for Python and automated machine learning. Upcoming integration with Oracle Cloud Infrastructure Data Science will enable data scientists to develop models using both open source and scalable in-database algorithms. Uniquely, bringing algorithms to the data in Oracle Database speeds time to results by reducing data preparation and movement.
  • Oracle Cloud Infrastructure Data Catalog: Allows users to discover, find, organize, enrich and trace data assets on Oracle Cloud. Oracle Cloud Infrastructure Data Catalog has a built-in business glossary making it easy to curate and discover the right, trusted data.
  • Oracle Big Data Service: Offers a full Cloudera Hadoop implementation, with dramatically simpler management than other Hadoop offerings, including just one click to make a cluster highly available and to implement security. Oracle Big Data Service also includes machine learning for Spark allowing organizations to run Spark machine learning in memory with one product and with minimal data movement.
  • Oracle Cloud SQL: Enables SQL queries on data in HDFS, Hive, Kafka, NoSQL and Object Storage. Only CloudSQL enables any user, application or analytics tool that can talk to Oracle databases to transparently work with data in other data stores, with the benefit of push-down, scale-out processing to minimize data movement.
  • Oracle Cloud Infrastructure Data Flow: A fully-managed Big Data service that allows users to run Apache Spark applications with no infrastructure to deploy or manage. It enables enterprises to deliver Big Data and AI applications faster. Unlike competing Hadoop and Spark services, Oracle Cloud Infrastructure Data Flow includes a single window to track all Spark jobs making it simple to identify expensive tasks or troubleshoot problems.
  • Oracle Cloud Infrastructure Virtual Machines for Data Science: Preconfigured GPU-based environments with common IDEs, notebooks and frameworks that can be up and running in under 15 minutes, for $30 a day.
 

What Customers Are Saying

AgroScout is dedicated to detecting early stage crop diseases to improve crop yields, reduce pesticide use and increase profits. “Our vision is to make modern agronomy economically accessible to the 1 billion farmers working on 500 million farms worldwide, constituting 30 percent of the global workforce. We plan to achieve this by offering cloud based, AI-driven sustainable agronomy, relying purely on input from low cost drones, mobile phones and manual inputs by growers,” said Simcha Shore, Founder and CEO AgroScout. “Success of this vision relies on the ability to manage a continuous and increasing flow of input data and our own AI-based solution to transform that data into precision and decision agriculture, at scale. Speed, scale and agility of Oracle Cloud has helped us realize our dream. Now, new horizons have opened up with the recent addition of Oracle Cloud Infrastructure Data Science that improves our data scientists’ ability to collaboratively build, train and deploy machine learning models. This addition has reduced costs, increased efficiency and has helped us increase our global footprint faster.”

IDenTV provides advanced video analytics based on AI capabilities powered by computer vision, automated speech recognition and textual semantic classifiers. “With Oracle Cloud Infrastructure Data Science, we are able to scale our data science efforts to deliver business value faster than ever before. Our data science teams can now seamlessly access data without worrying about the complexities of data locations or access mechanisms. While using open-source capabilities like TensorFlow, Keras, and Jupyter notebooks embedded within the environment, we can streamline our model training and deployment tasks resulting in tremendous cost savings and faster results,” said Amro Shihadah, Founder and COO, IDenTV. “We feel that Oracle Cloud Infrastructure Data Science in conjunction with benefits of Autonomous Database will give us the edge we need to be competitive and unique in the market.”

Contact Info
Nicole Maloney
Oracle
+1.650.506.0806
nicole.maloney@oracle.com
Victoria Brown
Oracle
+1.650.850.2009
victoria.brown@oracle.com
About Oracle

The Oracle Cloud offers a complete suite of integrated applications for Sales, Service, Marketing, Human Resources, Finance, Supply Chain and Manufacturing, plus Highly Automated and Secure Generation 2 Infrastructure featuring the Oracle Autonomous Database. For more information about Oracle (NYSE: ORCL), please visit us at www.oracle.com.

Future Product Disclaimer

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation.

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Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

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Nicole Maloney

  • +1.650.506.0806

Victoria Brown

  • +1.650.850.2009

Oracle and Microsoft Bring Enterprise Cloud Interoperability to European Customers

Oracle Press Releases - Wed, 2020-02-12 07:03
Blog
Oracle and Microsoft Bring Enterprise Cloud Interoperability to European Customers

Vinay Kumar, vice president, product management, Oracle Cloud Infrastructure—Feb 12, 2020

This week, Oracle and Microsoft are extending their cloud partnership with a new cloud interconnect location in Amsterdam. This is good news for the many local businesses that rely on software from both companies. The new Amsterdam interconnect will enable these businesses to share data across applications running in Microsoft Azure and Oracle Cloud.

The facility in Amsterdam—a strategic data center hub for Europe—joins interconnected regions already up and running in Toronto; Ashburn, Virginia; and London, and is part of a broader Oracle-Microsoft cloud interoperability partnership announced last year.

The goal of the overall collaboration is to make it faster and easier for enterprises to move their on premise workloads to the cloud that best suits the specific needs of an application. For example, an enterprise customer may want to run a Windows-based applications on Microsoft Azure connected to Oracle’s Autonomous Database or Exadata on Oracle Cloud Infrastructure. Or they may want related Microsoft-centric and Oracle-centric apps to communicate in the cloud in a low latency way.

MESTEC is using Oracle Cloud—Microsoft Azure to deliver solutions to its customers that enable them to dramatically improve their manufacturing performance.

“MESTEC’s leading smart factory solution is powered by high performance cloud infrastructure and database systems. We put Azure and Oracle Cloud to the test by implementing our application tier in Azure connected to Oracle Autonomous Database, running on Oracle Cloud Infrastructure, and the results have been extremely positive,” said Mark Carleton, COO, MESTEC. “We are projecting a 50 percent reduction in infrastructure and management cost and up to 500 percent increase in performance. By connecting Oracle and Azure, we’re able to rapidly introduce innovative technologies into our solution, ultimately resulting in a better, smarter solution for our customers enabling them to make dramatic improvements in manufacturing performance.”

This partnership and the resulting interconnected cloud is important for businesses that want to put more of their data and workloads into the cloud but prefer to use cloud providers attuned to their mission-critical data and applications. In addition to offering a high degree of choice and flexibility, Oracle and Microsoft offer integrated identity and access management so customers don’t have to manage multiple passwords when accessing their cloud resources and applications. The collaborative support model and global partner ecosystem add to the enterprise-class experience.

In a recent study, global systems integrator Accenture found that customers using the interconnect can expect performance to meet the demands of latency-sensitive applications. Accenture’s tests found dramatically reduced latency, the average round-trip latency was 1.5 milliseconds.

Amsterdam is not the end of the story. Oracle and Microsoft plan to interconnect additional cloud regions on the U.S. West Coast, Asia, and regions dedicated to U.S. public sector customers.

New Database Innovations Deliver a Single Database that Supports all Data

Oracle Press Releases - Wed, 2020-02-12 07:00
Blog
New Database Innovations Deliver a Single Database that Supports all Data

Jenny Tsai-Smith, vice president, product management—Feb 12, 2020

Today during his keynote at Oracle OpenWorld London, Oracle Executive Vice President Juan Loaiza announced the latest innovations which further strengthen Oracle’s strategy of providing a single converged database engine able to meet all the needs of a business. The new database features enable customers to take advantage of new technology trends—such as employing blockchain for fraud prevention, leveraging the flexibility of JSON documents, or training and evaluating machine learning algorithms inside the database.

The future is data driven, and effective use of data will increasingly determine a company’s competitiveness. Unlocking the full value of an enterprise’s data requires a new generation of data driven apps. Oracle makes it easy to create modern data driven apps utilizing a single database engine which supports the most suitable data model, process type, and development paradigm for a wide variety of business requirements. We enable our customers to easily run many kinds of workloads against the same data. In contrast, other cloud providers require dozens of different specialized databases to handle different data types. Having to deploy multiple single-purpose databases leads additional challenges. Having to implement multiple different database engines will increase complexity, risk, and cost because each database introduces its own security model, its own set of procedures for implementing high availability, its own scalability capabilities, and requires separate skillsets to operate.

Much in the way a single smartphone is now a camera, a calendar, a platform for entertainment, and a messaging system, the same idea applies to Oracle’s converged database engine. With Oracle Database, enterprises are no longer forced into purchasing multiple individual single-purpose databases, when all they need is one converged database engine that handles everything.

Today, Oracle is announcing several new features which extend the converged capabilities in Oracle Database. These include: 

  • Oracle Machine Learning for Python (OML4Py): Oracle Machine Learning (OML) inside Oracle Database accelerates predictive insights by embedding advanced ML algorithms which can be applied directly to the data. Because the ML algorithms are already collocated with the data, there is no need to move the data out of the database. Data scientists can also use Python to extend the in-database ML algorithms.
  • OML4Py AutoML: With OML4Py AutoML, even non-experts can take advantage of machine learning. AutoML will recommend best-fit algorithms, automate feature selection, and tune hyperparameters to significantly improve model accuracy.
  • Native Persistent Memory Store: Database data and redo can now be stored in local Persistent Memory (PMEM). SQL can run directly on data stored in the mapped PMEM file system, eliminating IO code path, and reducing the need for large buffer caches. Allows enterprises to accelerate data access across workloads that demand lower latency, including high frequency trading and mobile communication.
  • Automatic In-Memory Management: Oracle Database In-Memory optimizes both analytics and mixed workload online transaction processing, delivering optimized performance for transactions while simultaneously supporting real-time analytics, and reporting. Automatic In-Memory Management greatly simplifies the use of In-Memory by automatically evaluating data usage patterns, and determining, without any human intervention, which tables would most benefit from being placed in the In-Memory Column Store.
  • Native Blockchain Tables: Oracle makes it easy to use Blockchain technology to help identify and prevent fraud. Oracle native blockchain tables look like standard tables. They allow SQL inserts, and inserted rows are cryptographically chained. Optionally, row data can be signed to ensure identity fraud protection. Oracle blockchain tables are simple to integrate into apps. They are able to participate in transactions and queries with other tables. Additionally, they support very high insert rates compared to a decentralized blockchain because commits do not require consensus.
  • JSON Binary Data Type: JSON documents stored in binary format in the Oracle Database enables 4X faster updates, and scanning up to 10X faster.

Oracle’s continuing to lead the industry in delivering the world’s most comprehensive data management solutions, including the industry’s first and only self-driving database, Oracle Autonomous Database. The company was recently named the leader in “The Forrester WaveTM: Translytical Data Platforms, Q4 2019 report which cites that, “unlike other vendors, Oracle uses a dual-format database (row and columns for the same table) to deliver optimal translytical performance,” and that “customers like Oracle’s capability to support many workloads including OLTP, IoT, microservices, multi-model, data science, AI/ML, spatial, graph, and analytics.” 

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Future Product Disclaimer

The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation.

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