{ Abhilash Kumar Bhattaram : Follow on LinkedIn }
series : 23ai - CLOB to JSON Migration Part 2
As a pre-requisite to read further on this blog , I highly recommend to read the first part , series 1 of this blog regarding CLOB to JSON conversion here
CLOB to JSON - Test Data set using weather data
I would like to create some realistic weather data where my JSON data is significantly larger than my table data , so create table is as below. Note that I have a IS JSON check constrainst which verifies if my data is actually JSON ( available from 21c onwards ). This helps avoids JSON pre-check migration issues at a later date
FREE> CREATE TABLE we.weather_data_clob_part_v2 (
2 id NUMBER GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
3 location VARCHAR2(100),
4 created_at DATE DEFAULT SYSDATE NOT NULL,
5 weather_json CLOB
6 )
7 LOB (weather_json) STORE AS SECUREFILE (
8 ENABLE STORAGE IN ROW
9 CHUNK 8192
10 NOCACHE
11 LOGGING
12 )
13 PARTITION BY RANGE (created_at)
14 INTERVAL (INTERVAL '1' MONTH) -- Literal constant
15 (
16 PARTITION p_start_1999 VALUES LESS THAN (DATE '1999-01-01')
17 ) tablespace users;
Table created.Now I would like to generate some random and near realistics weather data.
This PL/SQL block simulates and inserts 5000 complex JSON records into a partitioned Oracle table
This PL/SQL script is designed to stress-test Oracle’s handling of large JSON objects stored in CLOB columns by generating and inserting 10,000 synthetic weather records into a partitioned table. Each record simulates comprehensive weather data by constructing a JSON object with multiple layers: it starts with location information (city, latitude, longitude), includes metadata like creation timestamps, and builds extensive weather data sections. One section comprises a 365-day forecast with randomized values (e.g., temperature, humidity, wind speed, pressure), while another details 168 hourly forecasts over a week. The script leverages DBMS_RANDOM to generate realistic random data, uses DBMS_LOB to manage the JSON text in CLOB format, and commits every 500 records to optimize performance. This approach provides a robust framework for testing ingestion, performance, and querying of complex, voluminous JSON data in an Oracle environment.
-- Generating random weather data
FREE> @/home/oracle/blog/weather/part-stress-load.sql
FREE> DECLARE
2 v_json CLOB;
3 v_chunk CLOB;
4 v_location VARCHAR2(100);
5 v_lat NUMBER;
6 v_lon NUMBER;
7 v_date DATE;
8 v_created_at DATE;
9 v_batch_size CONSTANT PLS_INTEGER := 500; -- adjust if needed
10 BEGIN
11 FOR i IN 1..5000 LOOP
12 DBMS_LOB.CREATETEMPORARY(v_json, TRUE);
13
14 v_location := 'StressCity_' || i;
15 v_lat := DBMS_RANDOM.VALUE(-90, 90);
16 v_lon := DBMS_RANDOM.VALUE(-180, 180);
17 v_date := SYSDATE;
18 v_created_at := TRUNC(ADD_MONTHS(DATE '1999-01-01', FLOOR(DBMS_RANDOM.VALUE(0, 300)))); -- Random month from 1999 onward
19
20 -- JSON header
21 DBMS_LOB.APPEND(v_json, '{"location":{"city":"' || v_location || '",');
22 DBMS_LOB.APPEND(v_json, '"coordinates":{"lat":' || v_lat || ',"lon":' || v_lon || '}},');
23 DBMS_LOB.APPEND(v_json, '"metadata":{"source":"StressGenEngine","version":"1.0",');
24 DBMS_LOB.APPEND(v_json, '"created":"' || TO_CHAR(SYSTIMESTAMP, 'YYYY-MM-DD"T"HH24:MI:SS"Z"') || '"},');
25
26 -- Forecast (365 days)
27 DBMS_LOB.APPEND(v_json, '"forecast":[');
28 FOR d IN 1..365 LOOP
29 v_chunk := '{"date":"' || TO_CHAR(v_date + d, 'YYYY-MM-DD') || '",' ||
30 '"temp_max":' || ROUND(DBMS_RANDOM.VALUE(25, 45), 1) || ',' ||
31 '"temp_min":' || ROUND(DBMS_RANDOM.VALUE(10, 25), 1) || ',' ||
32 '"humidity":' || ROUND(DBMS_RANDOM.VALUE(20, 100), 1) || ',' ||
33 '"wind_speed":' || ROUND(DBMS_RANDOM.VALUE(0, 40), 1) || ',' ||
34 '"pressure":' || ROUND(DBMS_RANDOM.VALUE(980, 1050), 1) || ',' ||
35 '"uv_index":' || ROUND(DBMS_RANDOM.VALUE(0, 11), 1) || ',' ||
36 '"visibility":' || ROUND(DBMS_RANDOM.VALUE(1, 20), 1) || ',' ||
37 '"precip_mm":' || ROUND(DBMS_RANDOM.VALUE(0, 100), 1) || ',' ||
38 '"cloud_pct":' || ROUND(DBMS_RANDOM.VALUE(0, 100), 1) || '}';
39 IF d < 365 THEN
40 v_chunk := v_chunk || ',';
41 END IF;
42 DBMS_LOB.APPEND(v_json, v_chunk);
43 END LOOP;
44 DBMS_LOB.APPEND(v_json, '],');
45
46 -- Hourly (168 hours = 7 days)
47 DBMS_LOB.APPEND(v_json, '"hourly":[');
48 FOR h IN 0..167 LOOP
49 v_chunk := '{"timestamp":"' || TO_CHAR(v_date + h/24, 'YYYY-MM-DD"T"HH24:MI:SS"Z"') || '",' ||
50 '"temperature":' || ROUND(DBMS_RANDOM.VALUE(-10, 45), 1) || ',' ||
51 '"feels_like":' || ROUND(DBMS_RANDOM.VALUE(-15, 50), 1) || ',' ||
52 '"dew_point":' || ROUND(DBMS_RANDOM.VALUE(-5, 25), 1) || ',' ||
53 '"humidity":' || ROUND(DBMS_RANDOM.VALUE(10, 100), 1) || ',' ||
54 '"wind_speed":' || ROUND(DBMS_RANDOM.VALUE(0, 50), 1) || ',' ||
55 '"wind_dir":"' || CHR(TRUNC(DBMS_RANDOM.VALUE(65, 91))) || '",' ||
56 '"pressure":' || ROUND(DBMS_RANDOM.VALUE(980, 1050), 1) || ',' ||
57 '"visibility":' || ROUND(DBMS_RANDOM.VALUE(1, 20), 1) || ',' ||
58 '"cloud_cover":' || ROUND(DBMS_RANDOM.VALUE(0, 100), 1) || ',' ||
59 '"precip_mm":' || ROUND(DBMS_RANDOM.VALUE(0, 100), 1) || '}';
60 IF h < 167 THEN
61 v_chunk := v_chunk || ',';
62 END IF;
63 DBMS_LOB.APPEND(v_json, v_chunk);
64 END LOOP;
65 DBMS_LOB.APPEND(v_json, ']');
66
67 -- Close JSON
68 DBMS_LOB.APPEND(v_json, '}');
69
70 -- Insert into partitioned table
71 INSERT INTO we.weather_data_clob_part (location, created_at, weather_json)
72 VALUES (v_location, v_created_at, v_json);
73
74 -- Commit every batch
75 IF MOD(i, v_batch_size) = 0 THEN
76 COMMIT;
77 DBMS_OUTPUT.PUT_LINE('Inserted: ' || i);
78 END IF;
79
80 DBMS_LOB.FREETEMPORARY(v_json);
81 END LOOP;
82
83 COMMIT;
84 DBMS_OUTPUT.PUT_LINE(' 5000 stress-test records inserted.');
85 END;
86 /
PL/SQL procedure successfully completed.
Elapsed: 00:00:56.53CLOB to JSON - Prechecks
As seen in series 1 , one of the first steps is to perform migration checks as in Oracle Documentation here
Note that in this case we are not seeing any errors because I have a check constraint created in generating realistic JSON data using IS JSON
########### Doing JSON CHECKS
begin
dbms_json.json_type_convertible_check(
owner => 'WE',
tablename => 'WEATHER_DATA_CLOB_PART',
columnname => 'WEATHER_JSON',
statustablename => 'WEATHER_DATA_CLOB_PARTPRECHECK'
);
end;
/
FREE> 2 3 4 5 6 7 8 9
PL/SQL procedure successfully completed.
Elapsed: 00:00:10.34
FREE> execute dbms_json.json_type_convertible_check(owner => 'WE',tablename => 'WEATHER_DATA_CLOB_PART',columnname => 'WEATHER_JSON',statustablename => 'WEATHER_DATA_CLOB_PARTPRECHECK');
PL/SQL procedure successfully completed.
Elapsed: 00:00:09.68
FREE>
select count(*) from WEATHER_DATA_CLOB_PARTPRECHECK;
col STAMP for a30
col SCHEMA_NAME for a10
col TABLE_NAME for a30
col COLUMN_NAME for a20
col ERROR_ROW_ID for a20
col ERROR_CODE for a20
col status for a40
select stamp,schema_name,table_name,column_name,error_row_id,error_code,status from WEATHER_DATA_CLOB_PARTPRECHECK;
FREE> select stamp,schema_name,table_name,column_name,error_row_id,error_code,status from WEATHER_DATA_CLOB_PARTPRECHECK;
STAMP |SCHEMA_NAM|TABLE_NAME |COLUMN_NAME |ERROR_ROW_ID |ERROR_CODE |STATUS
------------------------------|----------|------------------------------|--------------------|--------------------|--------------------|----------------------------------------
03-MAY-25 06.25.13.093533 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |5% completed (Errors found:0)
03-MAY-25 06.25.13.559536 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |10% completed (Errors found:0)
03-MAY-25 06.25.14.031199 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |15% completed (Errors found:0)
03-MAY-25 06.25.14.504599 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |20% completed (Errors found:0)
03-MAY-25 06.25.14.996157 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |25% completed (Errors found:0)
03-MAY-25 06.25.15.456118 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |30% completed (Errors found:0)
03-MAY-25 06.25.15.926015 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |35% completed (Errors found:0)
03-MAY-25 06.25.16.392382 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |40% completed (Errors found:0)
03-MAY-25 06.25.16.862939 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |45% completed (Errors found:0)
03-MAY-25 06.25.17.339892 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |50% completed (Errors found:0)
03-MAY-25 06.25.17.814633 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |55% completed (Errors found:0)
03-MAY-25 06.25.18.288084 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |60% completed (Errors found:0)
03-MAY-25 06.25.18.754714 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |65% completed (Errors found:0)
03-MAY-25 06.25.19.227833 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |70% completed (Errors found:0)
03-MAY-25 06.25.19.709148 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |75% completed (Errors found:0)
03-MAY-25 06.25.20.189663 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |80% completed (Errors found:0)
03-MAY-25 06.25.20.657897 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |85% completed (Errors found:0)
03-MAY-25 06.25.21.145784 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |90% completed (Errors found:0)
03-MAY-25 06.25.21.616373 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |95% completed (Errors found:0)
03-MAY-25 06.25.22.094879 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |100% completed (Errors found:0)
03-MAY-25 06.25.22.095496 PM |WE |WEATHER_DATA_CLOB_PART |WEATHER_JSON | | |Process completed (Errors found: 0)
21 rows selected.
Elapsed: 00:00:00.01
FREE>
FREE> SELECT WEATHER_JSON FROM WE.WEATHER_DATA_CLOB_PART
WHERE ROWID IN (SELECT pt.ERROR_ROW_ID
FROM WEATHER_DATA_CLOB_PARTPRECHECK pt
WHERE pt.schema_name = 'WE'
AND pt.table_name = 'WEATHER_DATA_CLOB_PART'
AND pt.column_name = 'WEATHER_JSON');
2 3 4 5 6
no rows selected
Elapsed: 00:00:00.01
FREE> FREE>
------- There are no inconsistenciesMoving partitioned CLOB to partitioned JSON
The SQL below makes this relatively easier
FREE> select count(*) from WE.WEATHER_DATA_CLOB_PART;
COUNT(*)
--------------------
5000
Elapsed: 00:00:00.02
FREE>
FREE> @part-ctas.sql
FREE> CREATE TABLE we.weather_data_json_part
2 PARTITION BY RANGE (created_at)
3 INTERVAL (INTERVAL '1' MONTH) -- Literal constant
4 (
5 PARTITION p_start_1999 VALUES LESS THAN (DATE '1999-01-01')
6 )
7 AS (SELECT id,location,created_at, JSON(weather_json) weather_json FROM WE.WEATHER_DATA_CLOB_PART);
Table created.
Elapsed: 00:00:33.69
FREE> select count(*) from WE.WEATHER_DATA_JSON_PART;;
COUNT(*)
--------------------
5000 ----------> Records MatchStorage Analysis of CLOB partitioned table and JSON partitioned Table
Now this is where things get intresting , let's look at the storage comparisons first.
-- For CLOB Partitioned Table
FREE> @seg1 WE WEATHER_DATA_CLOB_PART
### Data Segments/Partitions for "WE"."WEATHER_DATA_CLOB_PART"
MB|TABLESPACE_NAME |OWNER |SEGMENT_NAME |SEGMENT_TYPE | PARTITIONS
------------|-------------------------|-------------------------|----------------------------------------|--------------------|--------------------
2,400|USERS |WE |WEATHER_DATA_CLOB_PART |TABLE PARTITION | 300
------------| | | | |
2,400| | | | |
Elapsed: 00:00:00.05
### LOB Partitions for "WE"."WEATHER_DATA_CLOB_PART"
TABLE_OWNER |TABLE_NAME |TABLESPACE_NAME | LOB_PARTITION_COUNT
----------------------------------------|----------------------------------------|-------------------------|--------------------
WE |WEATHER_DATA_CLOB_PART |USERS | 301
Elapsed: 00:00:00.06
### LOB Segment Space for "WE"."WEATHER_DATA_CLOB_PART"
MB|TABLESPACE_NAME |OWNER |SEGMENT_NAME |SEGMENT_TYPE | PARTITIONS
------------|-------------------------|-------------------------|----------------------------------------|--------------------|--------------------
2,891|USERS |WE |SYS_LOB0000084386C00004$$ |LOB PARTITION | 300
------------| | | | |
2,891| | | | |
Elapsed: 00:00:04.90
### LOB Columns for "WE"."WEATHER_DATA_CLOB_PART"
OWNER |TABLE_NAME |SEGMENT_NAME |COLUMN_NAME | COUNT(*)
------------------------------|------------------------------|------------------------------|------------------------------|--------------------
WE |WEATHER_DATA_CLOB_PART |SYS_LOB0000084386C00004$$ |WEATHER_JSON | 363
Elapsed: 00:00:04.73
FREE>-- For JSON Partitioned Table
FREE> @seg1 WE WEATHER_DATA_JSON_PART
### Data Segments/Partitions for "WE"."WEATHER_DATA_JSON_PART"
MB|TABLESPACE_NAME |OWNER |SEGMENT_NAME |SEGMENT_TYPE | PARTITIONS
------------|-------------------------|-------------------------|----------------------------------------|--------------------|--------------------
2,400|USERS |WE |WEATHER_DATA_JSON_PART |TABLE PARTITION | 300
------------| | | | |
2,400| | | | |
Elapsed: 00:00:00.04
### LOB Partitions for "WE"."WEATHER_DATA_JSON_PART"
TABLE_OWNER |TABLE_NAME |TABLESPACE_NAME | LOB_PARTITION_COUNT
----------------------------------------|----------------------------------------|-------------------------|--------------------
WE |WEATHER_DATA_JSON_PART |USERS | 301
Elapsed: 00:00:00.05
### LOB Segment Space for "WE"."WEATHER_DATA_JSON_PART"
MB|TABLESPACE_NAME |OWNER |SEGMENT_NAME |SEGMENT_TYPE | PARTITIONS
------------|-------------------------|-------------------------|----------------------------------------|--------------------|--------------------
2,417|USERS |WE |SYS_LOB0000085294C00004$$ |LOB PARTITION | 300
------------| | | | |
2,417| | | | |
Elapsed: 00:00:10.82
### LOB Columns for "WE"."WEATHER_DATA_JSON_PART"
OWNER |TABLE_NAME |SEGMENT_NAME |COLUMN_NAME | COUNT(*)
------------------------------|------------------------------|------------------------------|------------------------------|--------------------
WE |WEATHER_DATA_JSON_PART |SYS_LOB0000085294C00004$$ |WEATHER_JSON | 303
Elapsed: 00:00:10.39
FREE>Now here are my observations
- There were no JSON convertible errors in this test case , which is the first landmark to achieve , in actual test these needs to be fixed.
- The count 5000 matches for both tables for WE.WEATHER_DATA_CLOB_PART and WE.WEATHER_DATA_JSON_PART
- Note that JSON data type is also a large object but with better storage management.
- The LOB space used by CLOB is 2,891 MB whereas it is 2417 GB - there is a notable ~20% reduction in storage space used by JSON.
Real life test data can vary but it is duly to be noted that we cannot expect exteme compression in Large Objects , it s dependant on the data
An example of 1 row of my large dataset from 5000 rows
As you can see the JSON payload of 1 record is quite exhaustive , the random data generated is about 5000 such records.
FREE> select * from WE.WEATHER_DATA_JSON_PART where rownum < 2;
ID|LOCATION |CREATED_AT |WEATHER_JSON
--------------------|----------------------------------------|-------------|--------------------------------------------------------------------------------------------------------------
16|StressCity_16 |1999-01-01 00|{"location":{"city":"StressCity_16","coordinates":{"lat":-74.62310028689667991220093090832720489,"lon":32.5057
| | |20870023810112985853627368521428}},"metadata":{"source":"StressGenEngine","version":"1.0","created":"2025-05-0
| | |5T16:16:57Z"},"forecast":[{"date":"2025-05-06","temp_max":27.1,"temp_min":13.7,"humidity":89,"wind_speed":13.5
| | |,"pressure":996.4,"uv_index":1.2,"visibility":18.3,"precip_mm":63.5,"cloud_pct":24.9},{"date":"2025-05-07","te
| | |mp_max":31.8,"temp_min":10.6,"humidity":51.8,"wind_speed":29.1,"pressure":1036.3,"uv_index":9.1,"visibility":8
| | |.8,"precip_mm":83.5,"cloud_pct":84.7},{"date":"2025-05-08","temp_max":37.3,"temp_min":22.6,"humidity":88.5,"wi
| | |nd_speed":21.5,"pressure":997.6,"uv_index":5.9,"visibility":5,"precip_mm":12.4,"cloud_pct":33.7},{"date":"2025
| | |-05-09","temp_max":39.4,"temp_min":15.6,"humidity":69.3,"wind_speed":14.2,"pressure":1018.3,"uv_index":9.1,"vi
| | |sibility":3.4,"precip_mm":95.9,"cloud_pct":70.9},{"date":"2025-05-10","temp_max":35.2,"temp_min":14.5,"humidit
| | |y":32.3,"wind_speed":7.2,"pressure":1047.8,"uv_index":3.4,"visibility":14.9,"precip_mm":70,"cloud_pct":64.8},{
| | |"date":"2025-05-11","temp_max":27.2,"temp_min":18.9,"humidity":51.4,"wind_speed":36.8,"pressure":1022,"uv_inde
| | |x":3.6,"visibility":6.9,"precip_mm":62.4,"cloud_pct":84.2},{"date":"2025-05-12","temp_max":35.7,"temp_min":14.
| | |7,"humidity":49.7,"wind_speed":8,"pressure":983.9,"uv_index":6.7,"visibility":14.8,"precip_mm":26.2,"cloud_pct
| | |":18.3},{"date":"2025-05-13","temp_max":26.5,"temp_min":17,"humidity":20.1,"wind_speed":4.3,"pressure":996.7,"
| | |uv_index":1.1,"visibility":19.7,"precip_mm":58.7,"cloud_pct":80.4},{"date":"2025-05-14","temp_max":28.2,"temp_
| | |min":15.2,"humidity":63.4,"wind_speed":20.2,"pressure":1025.5,"uv_index":9.3,"visibility":18.6,"precip_mm":12.
| | |9,"cloud_pct":72.4},{"date":"2025-05-15","temp_max":38.2,"temp_min":10.4,"humidity":99.8,"wind_speed":18.3,"pr
| | |essure":1042.4,"uv_index":9.5,"visibility":4.7,"precip_mm":32.7,"cloud_pct":1.5},{"date":"2025-05-16","temp_ma
| | |x":31.3,"temp_min":13.6,"humidity":65,"wind_speed":13.4,"pressure":997.6,"uv_index":4.8,"visibility":6.9,"prec
| | |ip_mm":83.6,"cloud_pct":93.8},{"date":"2025-05-17","temp_max":40.1,"temp_min":11.4,"humidity":34.3,"wind_speed
| | |":7.9,"pressure":985.5,"uv_index":10.4,"visibility":17.4,"precip_mm":81.5,"cloud_pct":27.3},{"date":"2025-05-1
| | |8","temp_max":38.7,"temp_min":17,"humidity":55.2,"wind_speed":3.8,"pressure":1040.2,"uv_index":0.9,"visibility
| | |":12.5,"precip_mm":18.7,"cloud_pct":15.4},{"date":"2025-05-19","temp_max":25.8,"temp_min":14,"humidity":83.6,"
| | |wind_speed":0.6,"pressure":1009.6,"uv_index":5.2,"visibility":13.7,"precip_mm":31.9,"cloud_pct":83.9},{"date":
| | |"2025-05-20","temp_max":29.8,"temp_min":17.1,"humidity":34.6,"wind_speed":19.2,"pressure":998,"uv_index":8.2,"
| | |visibility":1.4,"precip_mm":12.4,"cloud_pct":20.7},{"date":"2025-05-21","temp_max":38.4,"temp_min":17.9,"humid
| | |ity":87.6,"wind_speed":10.8,"pressure":990.1,"uv_index":3.9,"visibility":6.7,"precip_mm":28.8,"cloud_pct":18.6
| | |},{"date":"2025-05-22","temp_max":27,"temp_min":23.8,"humidity":54,"wind_speed":28.7,"pressure":1006.5,"uv_ind
| | |ex":5.7,"visibility":5.4,"precip_mm":32.9,"cloud_pct":25.9},{"date":"2025-05-23","temp_max":33.3,"temp_min":16
| | |.3,"humidity":53,"wind_speed":0.7,"pressure":1010.7,"uv_index":6,"visibility":3.5,"precip_mm":34.5,"cloud_pct"
| | |:7.2},{"date":"2025-05-24","temp_max":25.3,"temp_min":20.6,"humidity":67,"wind_speed":25.9,"pressure":1033.6,"
| | |uv_index":4.2,"visibility":18.7,"precip_mm":87.6,"cloud_pct":33.1},{"date":"2025-05-25","temp_max":35.2,"temp_
| | |min":15.1,"humidity":64.4,"wind_speed":39.3,"pressure":988.1,"uv_index":3.8,"visibility":18.1,"precip_mm":38.4
| | |,"cloud_pct":69.7},{"date":"2025-05-26","temp_max":32.1,"temp_min":17.1,"humidity":83.9,"wind_speed":17.7,"pre
| | |ssure":1042.6,"uv_index":7.4,"visibility":4,"precip_mm":4.1,"cloud_pct":56.3},{"date":"2025-05-27","temp_max":
| | |40.1,"temp_min":22,"humidity":89.5,"wind_speed":36.7,"pressure":1000.1,"uv_index":9.4,"visibility":18.9,"preci
| | |p_mm":94.7,"cloud_pct":5.4},{"date":"2025-05-28","temp_max":36.4,"temp_min":10.4,"humidity":83.9,"wind_speed":
| | |30.2,"pressure":995.8,"uv_index":7.9,"visibility":2.4,"precip_mm":21.5,"cloud_pct":44.5},{"date":"2025-05-29",
| | |"temp_max":37.1,"temp_min":14.7,"humidity":84.3,"wind_speed":4.4,"pressure":1006.3,"uv_index":10.1,"visibility
| | |":7.6,"precip_mm":57.1,"cloud_pct":23.8},{"date":"2025-05-30","temp_max":40,"temp_min":12.6,"humidity":79.9,"w
| | |ind_speed":6,"pressure":1008.2,"uv_index":6.2,"visibility":5.8,"precip_mm":84.6,"cloud_pct":16.4},{"date":"202
| | |5-05-31","temp_max":28.7,"temp_min":16.8,"humidity":43.1,"wind_speed":24.3,"pressure":1018.7,"uv_index":1.6,"v
| | |isibility":3.7,"precip_mm":65.7,"cloud_pct":61},{"date":"2025-06-01","temp_max":33.3,"temp_min":16.5,"humidity
| | |":75.3,"wind_speed":9.8,"pressure":983.3,"uv_index":2.3,"visibility":10.1,"precip_mm":26.7,"cloud_pct":41.6},{
| | |"date":"2025-06-02","temp_max":34.5,"temp_min":11.6,"humidity":49.7,"wind_speed":4.3,"pressure":1026.5,"uv_ind
| | |ex":5.1,"visibility":12.4,"precip_mm":9.3,"cloud_pct":35.1},{"date":"2025-06-03","temp_max":37.4,"temp_min":22
| | |.4,"humidity":90,"wind_speed":38.5,"pressure":1045.8,"uv_index":7.5,"visibility":1.2,"precip_mm":68.1,"cloud_p
| | |ct":76.7},{"date":"2025-06-04","temp_max":36.8,"temp_min":21.2,"humidity":97.8,"wind_speed":16.5,"pressure":10
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Elapsed: 00:00:00.03
FREE>Tables Exports CLOB vs JSON
Another notable difference is the exports are extremely fast in JSON when compared to CLOB , the size reduction is about 80% which makes manageability much easier.
The datapump exports are taken for the table WE.WEATHER_DATA_CLOB_PART and WE.WEATHER_DATA_JSON_PART
[oracle@machine1 1_working]$ ls -ltrh /opt/oracle/exports/WEATHER_DATA_*_PART.dmp
-rw-r-----. 1 oracle oinstall 934M May 5 17:07 /opt/oracle/exports/WEATHER_DATA_CLOB_PART.dmp
-rw-r-----. 1 oracle oinstall 203M May 5 17:08 /opt/oracle/exports/WEATHER_DATA_JSON_PART.dmp
[oracle@machine1 1_working]$ cat 23ai_we_clob_exp.par
USERID=system/********@FREE
TABLES=WE.WEATHER_DATA_CLOB_PART
DIRECTORY=EXPORTS
DUMPFILE=WEATHER_DATA_CLOB_PART.dmp
LOGFILE=WEATHER_DATA_CLOB_PART.log
[oracle@machine1 1_working]$ cat 23ai_we_json_exp.par
USERID=system/******@FREE
TABLES=WE.WEATHER_DATA_JSON_PART
DIRECTORY=EXPORTS
DUMPFILE=WEATHER_DATA_JSON_PART.dmp
LOGFILE=WEATHER_DATA_JSON_PART.log
[oracle@machine1 1_working]$