Oracle Analytics (via Collector method) - v3.0.4

Open in new tab

About Collectors

Collector Method

Pre-requisites

Collector Server Minimum Requirements

Oracle Analytics Requirements

Access to Oracle Analytics

Step 1: Establish Oracle Analytics Access

Create an Oracle user with read access to following tables

dba_hist_active_sess_history
dba_hist_snapshot
dba_users
dba_hist_sqltext
dba_col_comments
dba_tab_columns
dba_audit_trail (If you do not have Auditing configured, speak to KADA about it.)

The following Materialized Views will need to be created to support the Extraction process, consult KADA before proceeding.

-- table column metadata

CREATE MATERIALIZED
VIEW <SCHEMA>.MV_KADA_TABLES
TABLESPACE DATA
PARALLEL 4
     BUILD IMMEDIATE
AS
SELECT
    atc.owner "Owner",
    atc.table_name  "Table_Name",
    atc.column_id,
    atc.column_name "Column_Name",
    initcap(data_type) ||
        decode(data_type, 
            'CHAR',      '('|| char_length ||')',
            'VARCHAR',   '('|| char_length ||')',
            'VARCHAR2',  '('|| char_length ||')',
            'NCHAR',     '('|| char_length ||')',
            'NVARCHAR',  '('|| char_length ||')', 
            'NVARCHAR2', '('|| char_length ||')',
            'NUMBER',    '('|| nvl(data_precision,data_length)|| decode(data_scale,null,null,','||data_scale)||')', null) "Data_Type",
    nullable "Nullable",
    atc.owner       sdev_link_owner,
    atc.table_name  sdev_link_name,
    'TABLE'     sdev_link_type,
    acc.comments
FROM
    sys.dba_tab_columns ATC,
    sys.dba_COL_COMMENTS ACC
WHERE atc.owner = acc.owner
    and atc.table_name = acc.table_name
    and atc.column_name = acc.column_name
order by atc.owner, atc.table_name, atc.column_id
;
GRANT SELECT ON <SCHEMA>.MV_KADA_TABLES to <KADA USER>;

-- query history

CREATE MATERIALIZED VIEW <SCHEMA>.MV_KADA_DB_LOG
TABLESPACE DATA
     BUILD IMMEDIATE
REFRESH FORCE ON DEMAND
WITH ROWID USING TRUSTED CONSTRAINTS     
AS
SELECT
    r.begin_interval_time,
    r.dbid,
    r.snap_id,
    b.username AS user_name,
    b.username || '/' || r.session_id || '/' || r.session_serial# || '/' || r.instance_number AS session_id,
    r.instance_number,
    r.sql_id,
    r.sample_id,
    r.service_hash,
    r.client_id,
    r.machine,
    r.port,
    s.command_type,
    s.sql_text,
    r.start_time,
    r.cpu_time_ms,
    r.time_ms,
    r.db_time_ms,
    r.machine || ':' || r.port AS client_addr
FROM (
    SELECT
        s.begin_interval_time,
        a.DBID,
        a.snap_id,
        a.user_id,
        a.session_id,
        a.session_serial#,
        a.sql_id,
        a.sample_id,
        a.service_hash,
        a.client_id,
        a.machine,
        a.port,
        a.instance_number,
        MIN(a.sample_time) AS start_time,
        SUM(a.tm_delta_cpu_time) AS cpu_time_ms,
        SUM(a.tm_delta_time) AS time_ms,
        SUM(a.tm_delta_db_time) AS db_time_ms
    FROM dba_hist_active_sess_history a
        JOIN dba_hist_snapshot s
        ON a.dbid = s.dbid AND a.snap_id = s.snap_id AND a.instance_number = s.instance_number
    WHERE a.SQL_EXEC_START >= SYSDATE-1
    GROUP BY
        a.dbid,
        a.snap_id,
        a.user_id,
        a.session_id,
        a.session_serial#,
        a.sql_id,
        a.sample_id,
        a.service_hash,
        a.client_id,
        a.machine,
        a.port,
        s.begin_interval_time,
        a.instance_number
) r
    JOIN dba_users b
    ON r.user_id = b.user_id
    JOIN dba_hist_sqltext s
    ON r.dbid = s.dbid AND r.sql_id = s.sql_id
WHERE
    s.command_type NOT IN (
      6, 7, /* system cmds */
      47, /* declare cmd */
      170, 189
    )
    AND b.username NOT IN ('C##ADP$SERVICE','C##API','C##CLOUD$SERVICE','C##CLOUD_OPS','C##DV_ACCT_ADMIN','C##DV_OWNER','C##OMLIDM','GRAPH$METADATA','GRAPH$PROXY_USER','GSMADMIN_INTERNAL','ORACLE_OCM','OML$MODELS','OML$PROXY','REMOTE_SCHEDULER_AGENT','SH','SYS$UMF','SYSBACKUP','SYSDG','SYSKM','SYSRAC','DWH_STG','ADMIN','ODIREP_WLS_RUNTIME','ODIREP_ODI_REPO','ODIREP_STB','ODI_IAU_VIEWER','ODI_IAU','ODI_ODI_REPO','ODIREP_WLS','ODIREP_IAU_VIEWER','ODI_OPSS','ODIREP_IAU_APPEND','ODI_WLS','ODI_WLS_RUNTIME','ODIREP_OPSS','ODIREP_IAU','ODI_IAU_APPEND','ODI_STB','DWH_ODI_TMP','SYSTEM', 'SYS', 'OLAPSYS', 'LBACSYS', 'OWBSYS', 'OWBSYS_AUDIT', 'APPQOSSYS', 'SYSMAN', 'WMSYS', 'EXFSYS', 'CTXSYS', 'ORDSYS', 'MDSYS');
;
GRANT SELECT ON <SCHEMA>.MV_KADA_DB_LOG to <KADA USER>;

-- OACS usage

CREATE VIEW <SCHEMA>.V_KADA_OACS_LOGICAL
AS
    SELECT ID, USER_NAME, SESSION_ID, SAW_SRC_PATH, PRESENTATION_NAME
    FROM USAGE_TRACKING.LOGICAL_QUERIES
    WHERE START_DT >= SYSDATE-2
;
GRANT SELECT ON <SCHEMA>.V_KADA_OACS_LOGICAL to <KADA USER>;

CREATE VIEW <SCHEMA>.V_KADA_OACS_PHYSICAL
AS
    SELECT ID, LOGICAL_QUERY_ID, QUERY_BLOB, TIME_SEC, ROW_COUNT, START_DT, START_HOUR_MIN
    FROM USAGE_TRACKING.PHYSICAL_QUERIES
    WHERE START_DT >= SYSDATE-2
;
GRANT SELECT ON <SCHEMA>.V_KADA_OACS_PHYSICAL to <KADA USER>;

You have the option to create a wallet if you are using Oracle Cloud for authentication, otherwise username and password will suffice.

If you are using TNSNAMES ensure the tnsnames.ora file is up to date with the correct entries to be referenced.

You can connect 3 ways.

Host/servicename
TNSNAME in the tnsnames.ora file
A connection descriptor

Step 2: Create the Source in K

Create an Oracle Analytics source in K

Go to Settings, Select Sources and click Add Source
Select “Load from File system” option
Give the source a Name - e.g. Oracle Analytics Production
Add the Host name for the Oracle Analytics Server
Click Finish Setup

Step 3: Getting Access to the Source Landing Directory

Collector Method

Step 4: Install the Collector

It is recommended to use a python environment such as pyenv or pipenv if you are not intending to install this package at the system level.

Some python packages also have dependencies on the OS level packages, so you may be required to install additional OS packages if the below fails to install.

You can download the latest Core Library and whl via Platform Settings → Sources → Download Collectors

Run the following command to install the collector

pip install kada_collectors_extractors_<version>-none-any.whl

You will also need to install the common library kada_collectors_lib for this collector to function properly.

pip install kada_collectors_lib-<version>-none-any.whl

You may require an ODBC package for the OS to be installed as well as an oracle client library package if do you not have one already, see Oracle Instant Client - Free tools and libraries for connecting to Oracle Database | Oracle Australia

Step 5: Configure the Collector

The collector requires a set of parameters to connect to and extract metadata from Oracle Analytics

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE
username	string	Username to log into Oracle	“myuser”
password	string	Password to log into Oracle
dsn	string	Datasource Name for Oracle, this can be one of the following forms <tnsname> <host/servicename>	“preprod” “local.example.com/oraservice”
oracle_client_path	string	Full path to the location of the Oracle Client libraries, this can be left empty if you do not use any specific client library.	“/tmp/drivers/lib/oracleinstantclient_11_9”
wallet_path	string	If you use Oracle wallets, then this is the location of the wallet, ensure that the sqlora.net file references the wallet locaton correctly. If you do not use wallets, leave this blank.	“/tmp/drivers/oracle/wallet”
output_path	string	Absolute path to the output location where files are to be written	“/tmp/output”
mask	boolean	To enable masking or not	true
compress	boolean	To gzip the output or not	true

These parameters can be added directly into the run or you can use pass the parameters in via a JSON file. The following is an example you can use that is included in the example run code below.

kada_oracle_analytics_extractor_config.json

Step 6: Run the Collector

The following code is an example of how to run the extractor. You may need to uplift this code to meet any code standards at your organisation.

This can be executed in any python environment where the whl has been installed. It will produce and read a high water mark file from the same directory as the execution called oracle_analytics_hwm.txt and produce files according to the configuration JSON.

This is the wrapper script: kada_oracle_analytics_extractor.py

Advance options:

If you wish to maintain your own high water mark files elsewhere you can use the above section’s script as a guide on how to call the extractor. The configuration file is simply the keyword arguments in JSON format. Refer to this document for more information Collector Integration General Notes | Storing HWM in another location

If you are handling external arguments of the runner yourself, you’ll need to consider additional items for the run method. Refer to this document for more information Collector Integration General Notes | The run method

username: username to sign into server
password: password to sign into server
dsn: server address or tnsname if using a wallet or odbc library
oracle_client_path: library path for the Oracle Instant Client
wallet_path: where the p12 and sso for the Oracle wallet is
output_path: full or relative path to where the outputs should go
mask: To mask the META/DATABASE_LOG files or not
compress: To gzip output files or not

Step 7: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, linkages, events etc) will be generated. These files will appear in the output_path directory you set in the configuration details

High Water Mark File

A high water mark file is created in the same directory as the execution called oracle_analytics_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

If you want prefer file managed hwm, you can edit the location of the hwn by following these instructions Collector Integration General Notes | Storing High Water Marks (HWM)

Step 8: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.

You can use Azure Storage Explorer if you want to initially do this manually. You can push the files using python as well (see Airflow example below)

Example: Using Airflow to orchestrate the Extract and Push to K

Collector Method