Entries by Arsalan

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Oracle 19c Tips & Techniques

In 19c or higher, it is no longer necessary to enable ‘Shared Servers’ on the catalog database, so Oracle recommends that (if you are using Oracle Native Encryption with sharding) you disable Shared Servers on the catalog database. This can be done by setting the database parameter shared_servers to “0” and restarting Oracle. Note that […]

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Oracle Machine Learning for R (OML4R) is an R API that makes the open-source R statistical programming language and environment ready for enterprise and big data. Designed for significant data problems, OML4R integrates R with Oracle Database. R users can run R commands and scripts for statistical and graphical analyses on data stored in the […]


GIPCD Trace Files Grows Too Fast

There have been a few issues related to the grid inter-process communication(GIPC) daemon. Since this lets redundant interconnect usage, it would produce many networks interconnect messages. Previously, I carried out a cyclic cleanup of the ‘gipcd’ related trace/logs. You can purge these huge trace files if your Clusterware is well and has no issues. If […]

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Oracle Sharding on Oracle Container Engine for Kubernetes

Oracle Sharding on Oracle Container Engine for Kubernetes (OKE) uses StatefulSet to provide stable, unique network identifiers and stable, persistent storage so you can create and manage your Oracle Sharding replica set natively in OKE with Oracle supported helm and chart templates. In addition, data is stored on persistent volume, so all the data is […]

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Automated Generation of Materialized Views

The paper below describes an automated system that generates, selects, verifies, and maintains materialized views in the Oracle RDBMS; it presents a novel technique, called the extended covering subexpression algorithm, for the automated generation of materialized views. An extensive set of experiments is described that demonstrates the feasibility and efficiency of this approach. This system […]

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The following figure illustrates the phases, and the iterative nature, of a machine learning project. The process flow shows that a machine learning project does not stop when a particular solution is deployed. Instead, the results trigger new business questions, which can be used to develop more focused models.


Formatted view on V$LOCK that created via $ORACLE_HOME/rdbms/admin/catblock.sql SESSION_ID == SID in V$LOCK LOCK_TYPE, LOCK_ID1, LOCK_ID2 formatted value of TYPE, ID1, ID2 from V$LOCK MODE_HELD and MODE_REQUESTED formatted value of LMODE and REQUEST from V$LOCK LAST_CONVERT == CTIME of V$LOCK BLOCKING_OTHERS formatted value of BLOCK from V$LOCK Reference: https://docs.oracle.com/en/database/oracle/oracle-database/19/refrn/DBA_LOCK.html#GUID-B19FF191-AC89-498D-B2C1-2A18F63DC03D

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V$TRANSACTION lists the active transactions in the system. (i) The following columns together point to a transaction. (i.e.) The combination of the following should give a unique transaction id for that database. XIDUSN – Undo segment number XIDSLOT – NUMBER Slot number XIDSQN – NUMBER Sequence number (ii) The following columns explain the number of […]