Is it possible to import table queries into sqlyog? - view

I have a few .sql files containing queries for view tables. It would be helpful if I can just import them all at once into a database in sqlyog.
One of the example :
DELIMITER $$
ALTER ALGORITHM=UNDEFINED DEFINER=`root`#`localhost`
SQL SECURITY DEFINER
VIEW `viewrankingpointsbachelor` AS
SELECT
SUM(`vpd`.`marks`) AS `points`,
`vpd`.`student_no` AS `student_no`,
`vpd`.`type` AS `education_level`,
`s`.`name` AS `name`,
`s`.`gender` AS `gender`,
`s`.`campus_id` AS `campus_id`
FROM ((`viewpointsdetails` `vpd`
JOIN `student` `s`
ON(((`s`.`no_pelajar` = `vka`.`no_pelajar`)
AND (SUBSTR(`s`.`program_code`,3,1) = 1))))
JOIN `semester` `sem`
ON(((`sem`.`id` = (SELECT MAX(`sem`.`id`) FROM `semester`
WHERE (`sem`.`type` = 'Bachelor')))
AND (CAST(`vpd`.`date` AS DATE)
BETWEEN CAST(`sem`.`start` AS DATE) AND CAST(`sem`.`end` AS DATE)))))
GROUP BY `vpd`.`student_no`,`vpd`.`type`,`s`.`name`,`s`.`gender`,`s`.`campus_id`
ORDER BY `points` DESC,`s`.`campus_id`$$
DELIMITER ;
It is possible to import other than sql dump file? Thanks.

Related

How do I search for all the columns/field names starting with "XYZ" in Azure Databricks

I would like to do a big search on all field/columns names that contain "XYZ".
I tried below sql but it's giving me an error.
SELECT
table_name
,column_name
FROM information_schema.columns
WHERE column_name like '%account%'
order by table_name, column_name
ERROR states "Table or view not found: information_schema.columns; line 4, pos 5"

information_schema.columns is not supported in Databricks SQL. There are no in-built views available to get the complete details of tables along with columns. There is SHOW TABLES (database needs to be given) and SHOW COLUMNS (table name needs to be given).
You might have to use Pyspark capabilities to get the required result. First use the following code to get the details of all tables and respective columns:
db_tables = spark.sql(f"SHOW TABLES in default")
from pyspark.sql.functions import *
final_df = None
for row in db_tables.collect():
if(final_df is None):
final_df = spark.sql(f"DESCRIBE TABLE {row.database}.{row.tableName}")\
.withColumn('database',lit(f'{row.database}'))\
.withColumn('tablename',lit(f'{row.tableName}'))\
.select('database','tablename','col_name')
else:
final_df = final_df.union(spark.sql(f"DESCRIBE TABLE {row.database}.{row.tableName}")\
.withColumn('database',lit(f'{row.database}'))\
.withColumn('tablename',lit(f'{row.tableName}'))\
.select('database','tablename','col_name'))
#display(final_df)
final_df.createOrReplaceTempView('req')
Create a view and then apply the following query:
%sql
SELECT tablename,col_name FROM req WHERE col_name like '%id%' order by tablename, col_name

can i set up an SSRS report where users input parameters to a table

I have an oracle query that uses a created table as part of the code. Every time I need to run a report I delete current data and import the new data I receive. This is one column of id's. I need to create a report on SSRS in which the user can input this data into said table as a parameter. I have designed a simple report that they can enter some of the id's into a parameter, but there may be times when they need to enter in a few thousand id's, and the report already runs long. Here is what the SSRS code currently says:
select distinct n.id, n.notes
from notes n
join (
select max(seq_num) as seqnum, id from notes group by id) maxresults
on n.id = maxresults.ID
where n.seq_num = maxresults.seqnum
and n.id in (#MyParam)
Is there a way to have MyParam insert data into a table I would join called My_ID, joining as Join My_Id id on n.id = id.id
I do not have permissions to create functions or procedures in the database.
Thank you

You may try the trick with MATERIALIZE hint which normally forces Oracle to create a temporary table :
WITH cte1 AS
( SELECT /*+ MATERIALIZE */ 1 as id FROM DUAL
UNION ALL
SELECT 2 DUAL
)
SELECT a.*
FROM table1 a
INNER JOIN cte1 b ON b.id = a.id

Slowly changing dimensions- SCD1 and SCD2 implementation in Hive [closed]

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I am looking for SCD1 and SCD2 implementation in Hive (1.2.1). I am aware of the workaround to load SCD1 and SCD2 tables prior to Hive (0.14). Here is the link for loading SCD1 and SCD2 with the workaround approach http://hortonworks.com/blog/four-step-strategy-incremental-updates-hive/
Now that Hive supports ACID operations just want to know if there is a better or direct way of loading it.

As HDFS is immutable storage it could be argued that versioning data and keeping history (SCD2) should be the default behaviour for loading dimensions. You can create a View in your Hadoop SQL query engine (Hive, Impala, Drill etc.) that retrieves the current state/latest value using windowing functions. You can find out more about dimensional models on Hadoop in my blog post, e.g. how to handle a large dimension and fact table.

Well, I work it around using two temp tables:
drop table if exists administrator_tmp1;
drop table if exists administrator_tmp2;
set hive.exec.dynamic.partition=true;
set hive.exec.dynamic.partition.mode=nonstrict;
--review_administrator
CREATE TABLE if not exists review_administrator(
admin_id bigint ,
admin_name string,
create_time string,
email string ,
password string,
status_description string,
token string ,
expire_time string ,
granter_user_id bigint ,
admin_time string ,
effect_start_date string ,
effect_end_date string
)
partitioned by (current_row_indicator string comment 'current, expired')
stored as parquet;
--tmp1 is used for saving origin data
CREATE TABLE if not exists administrator_tmp1(
admin_id bigint ,
admin_name string,
create_time string,
email string ,
password string ,
status_description string ,
token string ,
expire_time string ,
granter_user_id bigint ,
admin_time string ,
effect_start_date string ,
effect_end_date string
)
partitioned by (current_row_indicator string comment 'current, expired:')
stored as parquet;
--tmp2 saving the scd data
CREATE TABLE if not exists administrator_tmp2(
admin_id bigint ,
admin_name string,
create_time string,
email string ,
password string ,
status_description string ,
token string ,
expire_time string ,
granter_user_id bigint ,
admin_time string ,
effect_start_date string ,
effect_end_date string
)
partitioned by (current_row_indicator string comment 'current, expired')
stored as parquet;
--insert origin data into tmp1
INSERT OVERWRITE TABLE administrator_tmp1 PARTITION(current_row_indicator)
SELECT
user_id as admin_id,
name as admin_name,
time as create_time,
email as email,
password as password,
status as status_description,
token as token,
expire_time as expire_time,
admin_id as granter_user_id,
admin_time as admin_time,
'{{ ds }}' as effect_start_date,
'9999-12-31' as effect_end_date,
'current' as current_row_indicator
FROM
ks_db_origin.gifshow_administrator_origin
;
--insert scd data into tmp2
--for the data unchanged
INSERT INTO TABLE administrator_tmp2 PARTITION(current_row_indicator)
SELECT
t2.admin_id,
t2.admin_name,
t2.create_time,
t2.email,
t2.password,
t2.status_description,
t2.token,
t2.expire_time,
t2.granter_user_id,
t2.admin_time,
t2.effect_start_date,
t2.effect_end_date as effect_end_date,
t2.current_row_indicator
FROM
administrator_tmp1 t1
INNER JOIN
(
SELECT * FROM review_administrator
WHERE current_row_indicator = 'current'
) t2
ON
t1.admin_id = t2.admin_id
AND t1.admin_name = t2.admin_name
AND t1.create_time = t2.create_time
AND t1.email = t2.email
AND t1.password = t2.password
AND t1.status_description = t2.status_description
AND t1.token = t2.token
AND t1.expire_time = t2.expire_time
AND t1.granter_user_id = t2.granter_user_id
AND t1.admin_time = t2.admin_time
;
--for the data changed , update the effect_end_date
INSERT INTO TABLE administrator_tmp2 PARTITION(current_row_indicator)
SELECT
t2.admin_id,
t2.admin_name,
t2.create_time,
t2.email,
t2.password,
t2.status_description,
t2.token,
t2.expire_time,
t2.granter_user_id,
t2.admin_time,
t2.effect_start_date as effect_start_date,
'{{ yesterday_ds }}' as effect_end_date,
'expired' as current_row_indicator
FROM
administrator_tmp1 t1
INNER JOIN
(
SELECT * FROM review_administrator
WHERE current_row_indicator = 'current'
) t2
ON
t1.admin_id = t2.admin_id
WHERE NOT
(
t1.admin_name = t2.admin_name
AND t1.create_time = t2.create_time
AND t1.email = t2.email
AND t1.password = t2.password
AND t1.status_description = t2.status_description
AND t1.token = t2.token
AND t1.expire_time = t2.expire_time
AND t1.granter_user_id = t2.granter_user_id
AND t1.admin_time = t2.admin_time
)
;
--for the changed data and the new data
INSERT INTO TABLE administrator_tmp2 PARTITION(current_row_indicator)
SELECT
t1.admin_id,
t1.admin_name,
t1.create_time,
t1.email,
t1.password,
t1.status_description,
t1.token,
t1.expire_time,
t1.granter_user_id,
t1.admin_time,
t1.effect_start_date,
t1.effect_end_date,
t1.current_row_indicator
FROM
administrator_tmp1 t1
LEFT OUTER JOIN
(
SELECT * FROM review_administrator
WHERE current_row_indicator = 'current'
) t2
ON
t1.admin_id = t2.admin_id
AND t1.admin_name = t2.admin_name
AND t1.create_time = t2.create_time
AND t1.email = t2.email
AND t1.password = t2.password
AND t1.status_description = t2.status_description
AND t1.token = t2.token
AND t1.expire_time = t2.expire_time
AND t1.granter_user_id = t2.granter_user_id
AND t1.admin_time = t2.admin_time
WHERE t2.admin_id IS NULL
;
--for the data already marked by 'expired'
INSERT INTO TABLE administrator_tmp2 PARTITION(current_row_indicator)
SELECT
t1.admin_id,
t1.admin_name,
t1.create_time,
t1.email,
t1.password,
t1.status_description,
t1.token,
t1.expire_time,
t1.granter_user_id,
t1.admin_time,
t1.effect_start_date,
t1.effect_end_date,
t1.current_row_indicator
FROM
review_administrator t1
WHERE t1.current_row_indicator = 'expired'
;
--populate the dim table
INSERT OVERWRITE TABLE review_administrator PARTITION(current_row_indicator)
SELECT
t1.admin_id,
t1.admin_name,
t1.create_time,
t1.email,
t1.password,
t1.status_description,
t1.token,
t1.expire_time,
t1.granter_user_id,
t1.admin_time,
t1.effect_start_date,
t1.effect_end_date,
t1.current_row_indicator
FROM
administrator_tmp2 t1
;
--drop the two temp table
drop table administrator_tmp1;
drop table administrator_tmp2;
-- --example data
-- --2017-01-01
-- insert into table review_administrator PARTITION(current_row_indicator)
-- SELECT '1','a','2016-12-31','a#ks.com','password','open','token1','2017-12-31',
-- 0,'2017-12-31','2017-01-01','9999-12-31','current'
-- FROM default.sample_07 limit 1;
-- --2017-01-02
-- insert into table administrator_tmp1 PARTITION(current_row_indicator)
-- SELECT '1','a','2016-12-31','a01#ks.com','password','open','token1','2017-12-31',
-- 0,'2017-12-31','2017-01-02','9999-12-31','current'
-- FROM default.sample_07 limit 1;
-- insert into table administrator_tmp1 PARTITION(current_row_indicator)
-- SELECT '2','b','2016-12-31','a#ks.com','password','open','token1','2017-12-31',
-- 0,'2017-12-31','2017-01-02','9999-12-31','current'
-- FROM default.sample_07 limit 1;
-- --2017-01-03
-- --id 1 is changed
-- insert into table administrator_tmp1 PARTITION(current_row_indicator)
-- SELECT '1','a','2016-12-31','a03#ks.com','password','open','token1','2017-12-31',
-- 0,'2017-12-31','2017-01-03','9999-12-31','current'
-- FROM default.sample_07 limit 1;
-- --id 2 is not changed at all
-- insert into table administrator_tmp1 PARTITION(current_row_indicator)
-- SELECT '2','b','2016-12-31','a#ks.com','password','open','token1','2017-12-31',
-- 0,'2017-12-31','2017-01-03','9999-12-31','current'
-- FROM default.sample_07 limit 1;
-- --id 3 is a new record
-- insert into table administrator_tmp1 PARTITION(current_row_indicator)
-- SELECT '3','c','2016-12-31','c#ks.com','password','open','token1','2017-12-31',
-- 0,'2017-12-31','2017-01-03','9999-12-31','current'
-- FROM default.sample_07 limit 1;
-- --now dim table will show you the right SCD.

Here's the detailed implementation of slowly changing dimension type 2 in Hive using exclusive join approach.
Assuming that the source is sending a complete data file i.e. old, updated and new records.
Steps-
Load the recent file data to STG table
Select all the expired records from HIST table
select * from HIST_TAB where exp_dt != '2099-12-31'
Select all the records which are not changed from STG and HIST using inner join and filter on HIST.column = STG.column as below
select hist.* from HIST_TAB hist
inner join STG_TAB stg
on hist.key = stg.key
where hist.column = stg.column
Select all the new and updated records which are changed from STG_TAB using exclusive left join with HIST_TAB and set expiry and effective date as below
select stg.*, eff_dt (yyyy-MM-dd), exp_dt (2099-12-31)
from STG_TAB stg
left join
(select * from HIST_TAB where exp_dt = '2099-12-31') hist
on hist.key = stg.key
where hist.key is null
or hist.column != stg.column
Select all updated old records from the HIST table using exclusive left join with STG table and set their expiry date as shown below:
select hist.*, exp_dt(yyyy-MM-dd) from
(select * from HIST_TAB where exp_dt = '2099-12-31') hist
left join STG_TAB stg
on hist.key= stg.key
where hist.key is null
or hist.column!= stg.column
unionall queries from 2-5 and insert overwrite result to HIST table
More detailed implementation of SCD type 2 can be found here-
https://github.com/sahilbhange/slowly-changing-dimension

drop table if exists harsha.emp;
drop table if exists harsha.emp_tmp1;
drop table if exists harsha.emp_tmp2;
drop table if exists harsha.init_load;
show databases;
use harsha;
show tables;
create table harsha.emp (eid int,ename string,sal int,loc string,dept int,start_date timestamp,end_date timestamp,current_status string)
comment "emp scd implementation"
row format delimited
fields terminated by ','
lines terminated by '\n'
;
create table harsha.emp_tmp1 (eid int,ename string,sal int,loc string,dept int,start_date timestamp,end_date timestamp,current_status string)
comment "emp scd implementation"
row format delimited
fields terminated by ','
lines terminated by '\n'
;
create table harsha.emp_tmp2 (eid int,ename string,sal int,loc string,dept int,start_date timestamp,end_date timestamp,current_status string)
comment "emp scd implementation"
row format delimited
fields terminated by ','
lines terminated by '\n'
;
create table harsha.init_load (eid int,ename string,sal int,loc string,dept int)
row format delimited
fields terminated by ','
lines terminated by '\n'
;
show tables;
insert into table harsha.emp select 101 as eid,'aaaa' as ename,3400 as sal,'chicago' as loc,10 as did,from_unixtime(unix_timestamp()) as start_date,from_unixtime(unix_timestamp('9999-12-31 23:59:59','yyyy-mm-dd hh:mm:ss')) as end_date,'current' as current_status from (select '123')x;
insert into table harsha.emp select 102 as eid,'abaa' as ename,6400 as sal,'ny' as loc,10 as did,from_unixtime(unix_timestamp()) as start_date,from_unixtime(unix_timestamp('9999-12-31 23:59:59','yyyy-mm-dd hh:mm:ss')) as end_date,'current' as current_status from (select '123')x;
insert into table harsha.emp select 103 as eid,'abca' as ename,2300 as sal,'sfo' as loc,20 as did,from_unixtime(unix_timestamp()) as start_date,from_unixtime(unix_timestamp('9999-12-31 23:59:59','yyyy-mm-dd hh:mm:ss')) as end_date,'current' as current_status from (select '123')x;
insert into table harsha.emp select 104 as eid,'afga' as ename,3000 as sal,'seattle' as loc,10 as did,from_unixtime(unix_timestamp()) as start_date,from_unixtime(unix_timestamp('9999-12-31 23:59:59','yyyy-mm-dd hh:mm:ss')) as end_date,'current' as current_status from (select '123')x;
insert into table harsha.emp select 105 as eid,'ikaa' as ename,1400 as sal,'LA' as loc,30 as did,from_unixtime(unix_timestamp()) as start_date,from_unixtime(unix_timestamp('9999-12-31 23:59:59','yyyy-mm-dd hh:mm:ss')) as end_date,'current' as current_status from (select '123')x;
insert into table harsha.emp select 106 as eid,'cccc' as ename,3499 as sal,'spokane' as loc,20 as did,from_unixtime(unix_timestamp()) as start_date,from_unixtime(unix_timestamp('9999-12-31 23:59:59','yyyy-mm-dd hh:mm:ss')) as end_date,'current' as current_status from (select '123')x;
insert into table harsha.emp select 107 as eid,'toiz' as ename,4000 as sal,'WA.DC' as loc,40 as did,from_unixtime(unix_timestamp()) as start_date,from_unixtime(unix_timestamp('9999-12-31 23:59:59','yyyy-mm-dd hh:mm:ss')) as end_date,'current' as current_status from (select '123')x;
load data local inpath 'Documents/hadoop_scripts/t3.txt' into table harsha.emp;
load data local inpath 'Documents/hadoop_scripts/t4.txt' into table harsha.init_load;
insert into table harsha.emp_tmp1 select eid,ename,sal,loc,dept,from_unixtime(unix_timestamp()) as start_date,from_unixtime(unix_timestamp('9999-12-31 23:59:59','yyyy-mm-dd hh:mm:ss')) as end_date,'current' as current_status
from harsha.init_load;
insert into table harsha.emp_tmp2
select a.eid,a.ename,a.sal,a.loc,a.dept,from_unixtime(unix_timestamp()) as start_date,from_unixtime(unix_timestamp('9999-12-31 23:59:59','yyyy-mm-dd hh:mm:ss')) as end_date,'updated' as current_status from emp_tmp1 a
left outer join emp b on
a.eid=b.eid and
a.ename=b.ename and
a.sal=b.sal and
a.loc = b.loc and
a.dept = b.dept
where b.eid is null
union all
select a.eid,a.ename,a.sal,a.loc,a.dept,from_unixtime(unix_timestamp()) as start_date,from_unixtime(unix_timestamp('9999-12-31 23:59:59','yyyy-mm-dd hh:mm:ss')) as end_date,'current' as current_status from emp_tmp1 a
left outer join emp b on
a.eid = b.eid and
a.ename=b.ename and
a.sal=b.sal and
a.loc=b.loc and
a.dept=b.dept
where b.eid is not null
union all
select b.eid,b.ename,b.sal,b.loc,b.dept,b.start_date as start_date,from_unixtime(unix_timestamp()) as end_date,'expired' as current_status from emp b
inner join emp_tmp1 a on
a.eid=b.eid
where
a.ename <> b.ename or
a.sal <> b.sal or
a.loc <> b.loc or
a.dept <> b.dept
;
insert into table harsha.emp select eid,ename,sal,loc,dept,start_date,end_date,current_status from emp_tmp2;
records including expired:
select * from harsha.emp order by eid;
latest recods:
select a.* from emp a inner join (select eid ,max(start_date) as start_date from emp where current_status <> 'expired' group by eid) b on a.eid=b.eid and a.start_date=b.start_date;

I did use another approach when it come to managing data with SCDs:
Never update data that does exist inside your historical file or table.
Make sure that new rows will be compared to the most recent generation, for instance the load logic will add control columns : loaded_on, checksum and if needed a sequence column that would be used if multiple loads does occur the same day then comparing new data to the most recent generation will use both control columns and a key column that does exist inside your data like a customer or product key.
Now, the magic does take place by computing the checksum of all the column involved but the control columns, creating a unique finger print for each row. The finger print (checksum) column then will be used to determine if any columns have changed compared to the most recent generation (most recent generation is based on the latest state of the data based on the key, loaded_on and sequence).
Now, you know if a row coming from your daily update is new because there is no previous generation or if a row coming from your daily update will require to create a new row (new generation) inside your historical file or table and last if a row coming from your daily update does not have any changes therefore no need to create a row because there is no difference compared to previous generation.
The type of logic needed can be build using Apache Spark, in a single statement you can ask Spark to concatenate any number of columns of any datatypes then compute a hash value that is used to finger print it.
All together now you can develop a utility based on spark that will accept any data source and output a well organized, clean and slow dimensions aware historical file, table,... last, never update append only!

How to use SQL query to define table in dbtable?

In JDBC To Other Databases I found the following explanation of dbtable parameter:
The JDBC table that should be read. Note that anything that is valid in a FROM clause of a SQL query can be used. For example, instead of a full table you could also use a subquery in parentheses.
When I use the code:
CREATE TEMPORARY TABLE jdbcTable
USING org.apache.spark.sql.jdbc
OPTIONS (
url "jdbc:postgresql:dbserver",
dbtable "mytable"
)
everything works great, but the following:
dbtable "SELECT * FROM mytable"
leads to the error:
What is wrong?

Since dbtable is used as a source for the SELECT statement it has be in a form which would be valid for normal SQL query. If you want to use subquery you should pass a query in parentheses and provide an alias:
CREATE TEMPORARY TABLE jdbcTable
USING org.apache.spark.sql.jdbc
OPTIONS (
url "jdbc:postgresql:dbserver",
dbtable "(SELECT * FROM mytable) tmp"
);
It will be passed to the database as:
SELECT * FROM (SELECT * FROM mytable) tmp WHERE 1=0

Code In Scala
val checkQuery = "(SELECT * FROM " + inputTableName + " ORDER BY " + columnName + " DESC LIMIT 1) AS timetable"
val timeStampDf = spark.read.format("jdbc").option("url", url).option("dbtable", checkQuery).load()
Adding an alias is also necessary after the query in parenthesis.

Finding sequences and triggers associated with an Oracle table

I have used this query to fetch the list of sequences belonging to an Oracle database user:
SELECT * FROM all_sequences x,all_tables B
WHERE x.sequence_owner=B.owner AND B.TABLE_NAME='my_table';
But that database user is having many more sequence also, so the query returns me all the sequence of the database user. Can anybody help me to find the particular sequence of my_table using query so that I can get the auto increment id in my application.

i want the query which fetch list of table of my database user with the sequence and triggers used in the table
You can get the triggers associated with your tables from the user_triggers view. You can then look for any dependencies recorded for those triggers in user_dependencies, which may include objects other than sequences (packages etc.), so joining those dependencies to the user_sequences view will only show you the ones you are interested in.
Something like this, assuming you are looking at your own schema, and you're only interesting in triggers that references sequences (which aren't necessarily doing 'auto increment', but are likely to be):
select tabs.table_name,
trigs.trigger_name,
seqs.sequence_name
from user_tables tabs
join user_triggers trigs
on trigs.table_name = tabs.table_name
join user_dependencies deps
on deps.name = trigs.trigger_name
join user_sequences seqs
on seqs.sequence_name = deps.referenced_name;
SQL Fiddle demo.
If you're actually looking at a different schema then you'll need to use all_tables etc. and filter and join on the owner column for the user you're looking for. And if you want to include tables which don't have triggers, or triggers which don't refer to sequences, you can use outer joins.
Version looking for a different schema, though this assumes you have the privs necessary to access the data dictionary information - that the tables etc. are visible to you, which they may not be:
select tabs.table_name,
trigs.trigger_name,
seqs.sequence_name
from all_tables tabs
join all_triggers trigs
on trigs.table_owner = tabs.owner
and trigs.table_name = tabs.table_name
join all_dependencies deps
on deps.owner = trigs.owner
and deps.name = trigs.trigger_name
join all_sequences seqs
on seqs.sequence_owner = deps.referenced_owner
and seqs.sequence_name = deps.referenced_name
where tabs.owner = '<owner>';
If that can't see them then you might need to look at the DBA views, again if you have sufficient privs:
select tabs.table_name,
trigs.trigger_name,
seqs.sequence_name
from dba_tables tabs
join dba_triggers trigs
on trigs.table_owner = tabs.owner
and trigs.table_name = tabs.table_name
join dba_dependencies deps
on deps.owner = trigs.owner
and deps.name = trigs.trigger_name
join dba_sequences seqs
on seqs.sequence_owner = deps.referenced_owner
and seqs.sequence_name = deps.referenced_name
where tabs.owner = '<owner>';

One way would be to run these queries to check if there are any sequence's Pseudocolumns (NEXTVAL and CURRVAL ) used in your functions , procedures, packages, Triggers or PL/SQL JAVA SOURCE.
select * from user_source where
UPPER(TEXT) LIKE '%NEXTVAL%';
select * from all_source where
UPPER(TEXT) LIKE '%NEXTVAL%';
Then go to the specific Procedure, Function or Trigger to check which column/table gets populated by a sequence.
The query could also be used with '%CURRVAL%'
This might not help if you are running inserts from JDBC or other external applications using a sequence.
Oracle 12c introduced the IDENTITY columns, using which you could create a table with an identity column, which is generated by default.
CREATE TABLE t1 (c1 NUMBER GENERATED BY DEFAULT ON NULL AS IDENTITY,
c2 VARCHAR2(10));
This will internally create a sequence that auto-generates the value for the table's column.So, If you would like to know which sequence generates the value for which table, you may query the all_tab_columns
SELECT data_default AS sequence_val
,table_name
,column_name
FROM all_tab_columns
WHERE OWNER = 'HR'
AND identity_column = 'YES';
SEQUENCE_VAL |TABLE_NAME |COLUMN_NAME
-----------------------------------------|-------------------------------------
"HR"."ISEQ$$_78160".nextval |T1 |C1

I found a solution to this problem to guess the sequence of a particular sequence
select * from SYS.ALL_SEQUENCES where SEQUENCE_OWNER='OWNER_NAME' and LAST_NUMBER between (select max(FIELD_NAME) from TABLE_NAME) and (select max(FIELD_NAME)+40 from TABLE_NAME);
This query will guess by search the LAST_NUMBER of the sequence value between MAX value of the field using sequence and Max value + 40 (in my case cache value is 20, so I put 40)

select SEQUENCE_NAME from sys.ALL_TAB_IDENTITY_COLS where owner = 'SCHEMA_NAME' and table_name = 'TABLE_NAME';

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