The Internet of Things (IoT) has deep use cases - energy grids, communications, policing, security, and manufacturing. I’ll show how to use Oracle 19c’s Fast Ingest and Fast Lookup features to load IoT data from “edge” sources to take immediate advantage of that information in nearly real time.

1. Jim Czuprynski
@JimTheWhyGuy
Zero Defect Computing, Inc.
Fast and Furious: Handling Edge
Computing Data With Oracle 19c
Fast Ingest and Fast Lookup

2. My Credentials
• 40 years of database-centric IT experience
• Oracle DBA since 2001
• Oracle 9i, 10g, 11g, 12c OCP and ADWC
• Oracle ACE Director since 2014
• ODTUG Database Committee Lead and Board Member
• Editor of ODTUG TechCeleration
• Oracle-centric blog (Generally, It Depends)
• Regular speaker at Oracle OpenWorld, CodeONE,
KSCOPE, and international and regional OUGs
E-mail me at jczuprynski@zerodefectcomputing.com
Follow me on Twitter (@JimTheWhyGuy)
Connect with me on LinkedIn (Jim Czuprynski)

3. Edge Computing Opportunities, Everywhere You Look!
Industrial Internet of Things (IIoT),
especially manufacturing
Artificial Intelligence IoT (AIOT)
Elon Musk’s $100M carbon capture prize
Smart Cities: Scooters, Bicycles,
and Traffic Flow
Autonomous delivery vehicles
Improving farming: More
efficient use of resources &
eliminating over-watering
Reducing unnecessary food waste
Improved worker safety for occupations
most affected by climate change

4. The Likely Best Solution Is Obvious.
Electrify Everything!
Renewable energy costs (wind & solar) are now
cheaper than natural gas and coal
Smart Meters for electrical
consumption are ubiquitous
“Green” hydrogen: A raging
(but good!) debate
New safer, compartmentalized
nuclear technology
Shifts in electrical demand cycles as
working from home becomes the
“new normal”
For a brief introduction on this concept, check out this short video

5. Application Server
Oracle 19c Database
Fast Ingest Capabilities of Oracle 19c
Internet of Things
(IoT)
SGA
Large Pool
Rows Batch 1
Rows Batch 2
Rows Batch 3
Rows Batch n
Drainers
Smart Meters
Social Media
Feeds
Images (CCTV)
High-
Frequency
INSERTs
Permanent
Storage
INSERTs
These are deferred from
immediate COMMIT!
Verification of Synchronicity
Background
Processes

6. SwingBench
Load Generator
Oracle 19c Database
Simulating IoT Load Testing For Fast Ingest Evaluation
IoT
SGA_TARGET (8GB)
LARGE_POOL_SIZE
Simulated Smart
Meter Payloads
Complex simulated
transactions built via PL/SQL
procedures …
DATA Tablespace: Table
T_METER_READINGS
MEMOPTIMIZE_
POOL_SIZE (2G)
Single-
Row
INSERTs
Verification via
DBMS_MEMOPTIMIZE
Rows Batch 1
Rows Batch 2
Rows Batch 3
Rows Batch n
… and sent by multiple user
sessions to Oracle 19c
database via SwingBench
workload generator
W00n
W002
W001
W000

7. SIMIOT: A Simple IoT Schema
Entities and Relationships:
SMART_METERS
Contains information about
individual smart meters for
which data is being collected
METER_READINGS
Individual readings for each smart
meter over (often extremely!) short
time intervals
BUSINESS_DESCRIPTIONS
Describes unique business
classifications based on
licensing issued
Test Environment #1:
Oracle 19c (19.3) Developer
Virtual Appliance Image
• VirtualBox 6.1.0
• Linux 7.7
• 8GB memory
• 1 vCPU
Test Environment #2:
Oracle 21c (21.1)
OCI DBCS Instance
• EE - Extreme Performance
• Linux 7.7
• 30GB memory
• 2 OCPUs

8. Preparing For a Fast Ingest Workload: Some Unexpected Surprises (1)
SQL> ALTER SYSTEM SET memoptimize_pool_size = 2G SCOPE=SPFILE;
System altered.
SQL> shutdown immediate;
Database closed.
Database dismounted.
ORACLE instance shut down.
SQL> startup;
ORACLE instance started.
Total System Global Area 1.4496E+10 bytes
Fixed Size 9702624 bytes
Variable Size 3892314112 bytes
Database Buffers 1.0570E+10 bytes
Redo Buffers 23851008 bytes
Database mounted.
Database opened.
. . .
Activating the Memory
Optimized Row Store
does require an
instance bounce
. . .
SQL> show parameter pool_size
NAME TYPE VALUE
------------------------------------ ----------- ------------
java_pool_size big integer 0
large_pool_size big integer 0
memoptimize_pool_size big integer 2G
olap_page_pool_size big integer 0
shared_pool_size big integer 0
streams_pool_size big integer 0
SQL> ALTER SESSION SET CONTAINER = pdb19;
Session altered.
. . .
… Awesome!!
Almost there …
SQL> ALTER TABLE simiot.t_meter_readings MEMOPTIMIZE FOR WRITE;
ERROR at line 1: ORA-62172: MEMOPTIMIZE FOR WRITE feature
cannot be enabled on table in encrypted tablespace.
Wait … what?
Seriously?!?
See MOS Note #2396777.1, 18c New Memoptimized Rowstore, for list of supported infrastructure

9. Preparing For a Fast Ingest Workload: Some Unexpected Surprises (2)
SQL> ALTER SYSTEM
SET "_exadata_feature_on” = true
SCOPE=SPFILE;
SQL> SHUTDOWN IMMEDIATE;
SQL> STARTUP;
Here’s a neat trick to fake out your Oracle
19c test environment into thinking it’s
actually an Exadata platform!
(Provenance: @JulianDontcheff)
SQL> ALTER TABLE simiot.t_meter_readings MEMOPTIMIZE FOR WRITE;
ERROR at line 1: ORA-62144: MEMOPTIMIZE FOR WRITE feature not
allowed on table with foreign key constraint.
Huzzah!!
Almost there (again) …
Why you little …
now what?

10. Fast Ingest: Intrinsic Limitations
10
Can I Use Fast Ingest On a Table That … Allowed?
Uses RANGE or HASH partitioning? Yes
Uses REFERENCE, INTERVAL, SYSTEM, or AUTOLIST partitioning? No
Employs a Foreign Key constraint against another table? No
Employs triggers? No
Is stored within an encrypted tablespace? No
Uses any storage compression? No
Leverages IN MEMORY compression? No
Is EXTERNAL, Index-Organized, TEMPORARY, or NESTED? No
Uses a function-based, DOMAIN, BITMAP, or BITMAP JOIN index? No
Has a column with default values, or that’s marked INVISIBLE, VIRTUAL, OR UNUSED? No
Upon further review,
most of these restrictions
make sense
After all, our intent is to
drink from a firehose!
See MOS Note #2605883.1, Limitations for Using Memoptimized Fast Ingest, for full list of limitations

11. Fast Ingest: How Are Transactions Handled?
Feature Advantages Drawbacks
Ingested data is captured in batches within
the Large Pool, but not immediately written
to the database
Ingesting data is quite fast, and huge
volumes of data from numerous sessions
can be captured with extreme efficiency
because the database isn’t processing
individual rows
Should the database instance crash before all
ingested data is written to the database, it is
possible to lose data
Since Fast Ingest is not a transaction in the
traditional Oracle sense, COMMITs don’t
occur during within its context
“Normal” Oracle transaction
mechanisms are bypassed to enable
rapid data capture
• No COMMITs mean no ROLLBACKs, either!
• Parent-child transactions must be
coordinated to ensure against data loss
• Data cannot be queried until it’s actually
been flushed to disk from Fast Ingest
buffers
Index operations and constraint checking
only happens when data is finally written
from the Large Pool Fast Ingest area to disk
Not necessarily a bad thing! Should a primary key violation occur while the
“drainer” background processes are writing
data from the FI buffers to disk, the violating
rows won’t be INSERTed … but no exception
will be raised
Unless the insert fails for valid reasons, the
application itself must ensure that all valid
data have actually been inserted
DBMS_MEMOPTIMIZE procedures can
be called to verify all data has been
written successfully the database
The application itself must now ensure all data
has indeed been written to the database

12. Tracking Status Via V$MEMOPTIMIZE_WRITE_AREA
SQL> SELECT
(total_size / (1024*1024)) total_size_mb
,(used_space / (1024*1024)) used_space_mb
,(free_space / (1024*1024)) free_space_mb
,num_writes
,num_writers
,con_id
FROM v$memoptimize_write_area;
V$MEMOPTIMIZE_WRITE_AREA
contains statistics about the activity of
the background “drainer” processes as
well as how much memory is in use …
TOTAL_SIZE_MB USED_SPACE_MB FREE_SPACE_MB NUM_WRITES NUM_WRITERS CON_ID
------------- ------------- ------------- ---------- ----------- ------
2055 .156784058 2054.84322 0 106 3
. . .
TOTAL_SIZE_MB USED_SPACE_MB FREE_SPACE_MB NUM_WRITES NUM_WRITERS CON_ID
------------- ------------- ------------- ---------- ----------- ------
2055 1.15670776 2053.84329 8830 107 3
. . .
TOTAL_SIZE_MB USED_SPACE_MB FREE_SPACE_MB NUM_WRITES NUM_WRITERS CON_ID
------------- ------------- ------------- ---------- ----------- ------
2055 3.15655518 2051.84344 24681 109 3
… which makes it easy to
see the number of writes
to the database continue
as the generated
workload progresses

13. Verifying the State of Rows Written Via Fast Ingest
DECLARE
applied_hwm NUMBER(15,0);
written_hwm NUMBER(15,0);
BEGIN
applied_hwm := DBMS_MEMOPTIMIZE.GET_APPLY_HWM_SEQID;
written_hwm := DBMS_MEMOPTIMIZE.GET_WRITE_HWM_SEQID;
DBMS_OUTPUT.PUT_LINE(
'Low-Water-Mark for All Rows Applied (Written Globally): '
|| applied_hwm);
DBMS_OUTPUT.PUT_LINE(
'High Water Mark for Rows Written For This Session: '
|| written_hwm);
EXCEPTION
WHEN OTHERS THEN
DBMS_OUTPUT.PUT_LINE(
'Fatal unexpected error: ' ||
SQLCODE || ' - ' || SQLERRM);
END;
Low-Water-Mark for All Rows Applied (Written Globally): 498413730056
High Water Mark for Rows Written For This Session: 0
Here’s the state of the
LWM and HWM before
a workload begins …
Low-Water-Mark for All Rows Applied (Written Globally): 498536313235
High Water Mark for Rows Written For This Session: 498536328525
… and then after a
workload has
been generated
within this same
session …
Low-Water-Mark for All Rows Applied (Written Globally): 499537736037
High Water Mark for Rows Written For This Session: 498536328525
… and finally, after
more time has
passed, note the
global LWM has
surpassed the
session’s HWM

14. Controlling Fast Ingest Features Via PL/SQL
-----
-- Flush all unwritten data to the database
-- for the +current+ session
-----
BEGIN
DBMS_MEMOPTIMIZE.WRITE_END;
EXCEPTION
WHEN OTHERS THEN
DBMS_OUTPUT.PUT_LINE(
'Fatal unexpected error: ‘ ||
SQLCODE || ' - ' || SQLERRM);
END;
/
We can force any as-yet unwritten
data to be written to the database at
either a session level …
-----
-- Flush all unwritten data to the database
-- for +all+ sessions
-----
BEGIN
DBMS_MEMOPTIMIZE_ADMIN.WRITES_FLUSH;
EXCEPTION
WHEN OTHERS THEN
DBMS_OUTPUT.PUT_LINE(
'Fatal unexpected error: ' ||
SQLCODE || ' - ' || SQLERRM);
END;
/
… or for the entire database with
calls to the appropriate procedure

15. Fast Ingest vs. Single-Row Insert: Performance Comparison
Note the difference in
statements executed – a
36% improvement with just
one hint added!
Note there are fewer physical writes overall,
even with many more rows inserted in the
same amount of time

16. SwingBench
Load Generator
Oracle 19c Database
Fast Lookup Concepts
IoT SGA_TARGET (8GB)
LARGE_POOL_SIZE
Simulated Smart
Meter Payloads
T_METER_READINGS
MEMOPTIMIZE_
POOL_SIZE (2G)
Single-
Row
INSERTs
Rows Batch 1
Rows Batch 2
Rows Batch 3
Rows Batch n T_SMARTMETERS
T_METER_READINGS
Analytics
SQL> ALTER TABLE T_METER_READINGS
MEMOPTIMIZE FOR WRITE;
ALTER TABLE T_METER_READINGS MEMOPTIMIZE FOR READ;
ALTER TABLE T_SMARTMETERS MEMOPTIMIZE FOR READ;

17. Fast Lookup: Reporting on Frequently Accessed Data
Ideally, Fast Lookup is best deployed for data that will be
accessed frequently based on exact primary key values
• When a table is in MEMOPTIMIZE READ mode, any Fast Lookup request
against its data builds a hash table of PK values
• Corresponding rows are added to buffers in MEMOPTIMIZEd pool and then
pinned
• The hash table is thus shortcut to reading a row directly from its
corresponding buffer much more quickly than from traditional buffer cache
NOTE: This doesn’t mean Fast Lookup is only useful for tables leveraging Fast Ingest!
Scenario Example
Often-accessed rows Rows containing values most likely to be accessed most frequently –
for example, movies most recently added to a streaming service
Most recently loaded rows Users tend to access data about most recent store sales than
yesterday’s / last week’s sales
Repetitive reads on the same
larger “chunks” of data
Rows containing a nearly-fully-populated VARCHAR2(2000) column

18. Preparing For a Fast Lookup Workload
SQL> ALTER TABLE simiot.t_smartmeters MEMOPTIMIZE FOR READ;
ERROR at line 1: ORA-62142: MEMOPTIMIZE FOR READ feature
requires NOT DEFERRABLE PRIMARY KEY constraint on the table
Doh! That’s right, a
table must have a
primary key for Fast
Lookup to work.
SQL> ALTER TABLE simiot.t_meter_readings MEMOPTIMIZE FOR READ;
ERROR at line 1: ORA-62156: MEMOPTIMIZE FOR READ feature not
allowed on segment with deferred storage
Jeez, now
what?
As of now, there is no MOS Note that contains a full list of limitations!
Look through Oracle Error Messages in the range of ORA-62100 thru ORA-62200 for a complete list.

19. Controlling Fast Lookup Features Via PL/SQL
-----
-- Populate an object into the MORS pool
-- for Fast Lookup
-----
BEGIN
DBMS_MEMOPTIMIZE.POPULATE(
schema_name => 'SIMIOT'
,table_name => 'T_METER_READINGS'
,partition_name => NULL
);
EXCEPTION
WHEN OTHERS THEN
DBMS_OUTPUT.PUT_LINE(
'Fatal unexpected error: ‘ ||
SQLCODE || ' - ' || SQLERRM);
END;
/
-----
-- Remove an object enabled for Fast Lookup
-- from the MORS pool
-----
BEGIN
DBMS_MEMOPTIMIZE.DROP_OBJECT(
schema_name => 'SIMIOT'
,table_name => 'T_METER_READINGS'
,partition_name => NULL
);
EXCEPTION
WHEN OTHERS THEN
DBMS_OUTPUT.PUT_LINE(
'Fatal unexpected error: ‘ ||
SQLCODE || ' - ' || SQLERRM);
END;
/

20. Fast Lookup: Indications of Success (1)
SELECT name, value
FROM v$sysstat
WHERE name LIKE '%memopt r%'
ORDER BY name;
NAME VALUE
------------------------------------ ----------
memopt r NO IM tasks accepted 4
memopt r cleanup 1
memopt r entries deleted 36
memopt r lookups 1
memopt r misses 1
memopt r populate tasks accepted 4
System-level statistics for
Fast Lookup all contain
the string ”memopt r” …
SELECT
P.program "Executor"
,N.name "Statistic"
,S.value "Value"
FROM
v$sesstat S
,v$statname N
,v$session P
WHERE S.statistic# = N.statistic#
AND S.sid = P.sid
AND N.name LIKE '%memopt r%'
AND S.value > 0
ORDER BY P.program, N.name;
Executor Statistic Value
------------------------------------- ---------------------------------------- ----------
SQL Developer memopt r NO IM tasks accepted 4
SQL Developer memopt r populate tasks accepted 2
oracle@localhost.localdomain (W000) memopt r blocks populated 457
oracle@localhost.localdomain (W000) memopt r populate 1
oracle@localhost.localdomain (W000) memopt r puts 74314
oracle@localhost.localdomain (W000) memopt r rows populated 74314
oracle@localhost.localdomain (W000) memopt r successful puts 74314
oracle@localhost.localdomain (W001) memopt r cleanup 1
oracle@localhost.localdomain (W001) memopt r entries deleted 32817
oracle@localhost.localdomain (W002) memopt r blocks populated 34734
oracle@localhost.localdomain (W002) memopt r puts 5660881
oracle@localhost.localdomain (W002) memopt r puts:buckets full 681453
oracle@localhost.localdomain (W002) memopt r rows populated 5660881
oracle@localhost.localdomain (W002) memopt r successful puts 5660881
oracle@localhost.localdomain (W002) memopt r successful puts:with evictions 681453
oracle@localhost.localdomain (W002) memopt r tag collisions 104
oracle@localhost.localdomain (W007) memopt r cleanup 1
oracle@localhost.localdomain (W007) memopt r entries deleted 2800857
… and here’s a way to look
at Fast Lookup statistics at
a session level

21. Fast Lookup: Indications of Success (2)
… but an
equality
predicate
definitely
does!
A range scan can’t
take advantage of
Fast Lookup …

22. Analyzing Near-Real-Time Data with Oracle Machine Learning (1)
CREATE OR REPLACE VIEW simiot.solar_superusers AS
SELECT
sm_business_type
,sm_zipcode
,sm_id
,ROUND(AVG(smr_kwh_used),2) avg_kwh_used
,ROUND(AVG(smr_solar_kwh),2) avg_solar_kwh
,ROUND(AVG(smr_solar_kwh) / AVG(smr_kwh_used) ,2) pct_solar
,CASE
WHEN ROUND(AVG(smr_solar_kwh) / AVG(smr_kwh_used) ,2) >= 0.15
THEN 1 ELSE 0
END as solar_superuser
FROM
t_smartmeters
,t_meter_readings
WHERE smr_id = sm_id
GROUP BY sm_business_type, sm_zipcode, sm_id
ORDER BY sm_business_type, sm_zipcode, sm_id
;
We’ll use this
metric to identify
which
SmartMeter
customers are
solar energy
super-users
(defined as 15%
or more of their
energy generated
via solar power
on-site)

23. Analyzing Near-Real-Time Data with OML4SQL (2)
DECLARE
-- Processing variables:
SQLERRNUM INTEGER := 0;
SQLERRMSG VARCHAR2(255);
vcReturnValue VARCHAR2(256) := 'Failure!';
v_setlist DBMS_DATA_MINING.SETTING_LIST;
BEGIN
v_setlist('ALGO_NAME') := 'ALGO_KMEANS';
v_setlist('PREP_AUTO') := 'ON';
v_setlist('KMNS_DISTANCE') := 'KMNS_EUCLIDEAN';
v_setlist('KMNS_DETAILS') := 'KMNS_DETAILS_ALL';
v_setlist('KMNS_ITERATIONS') := '3';
v_setlist('KMNS_NUM_BINS') := '10’;
. . .
We’ll use a k-Means
algorithm to delve into
which Smart Meters are
clustered together, based
on various attributes of the
companies using them …

24. DECLARE
-- Processing variables:
SQLERRNUM INTEGER := 0;
SQLERRMSG VARCHAR2(255);
vcReturnValue VARCHAR2(256) := 'Failure!';
v_setlist DBMS_DATA_MINING.SETTING_LIST;
BEGIN
v_setlist('ALGO_NAME') := 'ALGO_KMEANS';
v_setlist('PREP_AUTO') := 'ON';
v_setlist('KMNS_DISTANCE') := 'KMNS_EUCLIDEAN';
v_setlist('KMNS_DETAILS') := 'KMNS_DETAILS_ALL';
v_setlist('KMNS_ITERATIONS') := '3';
v_setlist('KMNS_NUM_BINS') := '10’;
. . .
Analyzing Near-Real-Time Data with OML4SQL (2)
. . .
DBMS_DATA_MINING.CREATE_MODEL2(
model_name => 'OML4_SIMIOT_CLUSTERING',
mining_function => 'CLUSTERING',
data_query => 'SELECT * FROM simiot.solar_superusers',
set_list => v_setlist,
case_id_column_name => 'SM_ID');
EXCEPTION
WHEN OTHERS THEN
SQLERRNUM := SQLCODE;
SQLERRMSG := SQLERRM;
vcReturnValue :=
'Error creating new OML4SQL model: ' ||
SQLERRNUM || ' - ' || SQLERRMSG;
DBMS_APPLICATION_INFO.SET_MODULE(NULL,NULL);
DBMS_OUTPUT.PUT_LINE(vcReturnValue);
END;
/
… and leverage the view we just
created as the source for each
Smart Meter’s related attributes

25. Analyzing Near-Real-Time Data with OML4SQL (3)
Here’s an example of
using a Radar Area
graph to show the top
15 business types that
are the most egregious
outliers in terms of solar
power used (aka
distance from centroid)

26. So … Where Are the Solar-Powered Superstars?
… Oracle’s powerful Spatial and Graph
toolsets and free APEX plug-ins make
short work of identifying which
businesses are taking maximum
advantage of solar power
With a relatively simple SQL query
against our SmartMeter attributes and
their most recent meter readings …

27. Plans for Future Experimentation
Simulating intense direct streaming from IoT
devices using Oracle Streaming Services (OSS)
Testing with extreme workloads against an
Autonomous Transaction Processing (ATP)
instance
Incorporating a new workload generator for more
complex IoT streams (details coming soon!)

28. Useful Resources and Documentation
City of Chicago Reference Information:
https://data.cityofchicago.org/Community-Economic-Development/Business-Licenses-Current-Active/uupf-x98q/data
Architecture of the Memoptimized Rowstore:
https://docs.oracle.com/en/database/oracle/oracle-database/19/tgdba/tuning-system-global-
area.html#GUID-9752E93D-55A7-4584-B09B-9623B33B5CCF
Todd Sharp’s Blog Posts on Oracle Streaming Service (OSS):
https://blogs.oracle.com/developers/back-to-the-database-part-1-preparing-to-persist-data-from-a-stream
https://blogs.oracle.com/developers/back-to-the-database-part-2-persisting-data-from-a-stream
https://blogs.oracle.com/developers/back-to-the-database-part-3-publishing-database-changes-to-a-stream

29. Articles on the Future of IoT … and Our Planet
IoT and the Potential to Save the World
https://www.iotforall.com/iot-potential-to-save-the-world
What is IIoT? Industrial Internet of Things Explained
https://www.hologram.io/blog/what-is-iiot
IoT For All at CES: AI, IoT, and Robots
https://www.iotforall.com/ai-iot-aiot-ces
How Smart Devices with Edge Analytics are Helping Business
https://www.iotforall.com/how-smart-devices-with-edge-analytics-are-helping-business
How 100% Clean Energy Could Power Our Cities And Towns
https://grist.org/energy/how-100-clean-energy-could-power-our-cities-and-towns
American suburbs are about to look more like European cities
https://www.fastcompany.com/90576432/american-suburbs-are-about-to-look-more-like-european-cities
Miami pilots e-cargo bikes to reduce congestion, pollution
https://www.smartcitiesdive.com/news/miami-e-cargo-bike-pilot-dhl-city-congestion-pollution/578115/
You say old coal plant, I say green hydrogen
https://www.utilitydive.com/news/you-say-old-coal-plant-i-say-green-hydrogen/588499/