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In a previous post I described the setup of MiniShift on my laptop in order to run OpenShift for test purpose. I even pulled the Oracle Database image from the Docker Store. But the goal is to import it into OpenShift to deploy it from the Image Stream.
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Here is a quick post you may google into if you got the following error when running postupgrade_fixups.sql after an upgrade:
ERROR - Cannot open the preupgrade_messages.properties file from the directory object preupgrade_dir
DECLARE
*
ERROR at line 1:
ORA-29283: invalid file operation
ORA-06512: at "SYS.DBMS_PREUP", line 3300
ORA-06512: at "SYS.UTL_FILE", line 536
ORA-29283: invalid file operation
ORA-06512: at "SYS.UTL_FILE", line 41
ORA-06512: at "SYS.UTL_FILE", line 478
ORA-06512: at "SYS.DBMS_PREUP", line 3260
ORA-06512: at "SYS.DBMS_PREUP", line 9739
ORA-06512: at line 11
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In a previous post I’ve created a Google Cloud Spanner database and inserted a few rows from the GUI. This is definitely not a solution fo many rows and here is a post about using the command line.
If I start the Google Shell from the icon on the Spanner page for my project, everything is set. But if I run it from elsewhere, using the https://console.cloud.google.com/cloudshell as I did in A free persistent Google Cloud service with Oracle XE I have to set the project:
franck_pachot@cloudshell:~$ gcloud config set project superb-avatar-210409
Updated property [core/project].
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Google Cloud Spanner is a distributed relational database focused on scalability without compromising consistency and integrity. It is available only as a managed service in Google Cloud. Basically, the idea is to keep the scalability advantages of NoSQL database (like Bigtable) but adding transactions, relational tables, SQL, structured data,… as in the relational databases we love for decades.
The commercial pitch includes all the NoSQL buzzwords, with the addition of the legacy properties of SQL databases:
Cloud Spanner is a fully managed, mission-critical, relational database service that offers transactional consistency at global scale, schemas, SQL (ANSI 2011 with extensions), and automatic, synchronous replication for high availability.
Here I’m testing something that is not mentioned, but is taken for granted with all SQL databases: the ability to add numbers without erroneous arithmetic results.
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In pgSentinel: the sampling approach for PostgreSQL I mentioned that one of the advantages of the ASH approach is the ability to drill down from an overview of the database activity, down to the details where we can do some tuning. The idea is to always focus on the components which are relevant to our tuning goal:
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Here is the first test I did with the beta of pgSentinel. This Active Session History sampling is a new approach to Postgres tuning. For people coming from Oracle, this is something that has made our life a lot easier to optimize database applications. Here is a quick example showing how it links together some information that are missing without this extension.
The installation of the extension is really easy (nore details on Daniel’s post):
cp pgsentinel.control /usr/pgsql-10/share/extension
cp pgsentinel--1.0.sql /usr/pgsql-10/share/extension
cp pgsentinel.so /usr/pgsql-10/lib
If you follow this blog, you should know how I like Orachrome Lighty for Oracle, for its efficiency to monitor database performance statistics. Today Orachrome released the beta version of Lighty for Postgres: https://orachrome.com/news/la-beta-de-lighty-for-postgresql-est-ouverte/
The Cloud is perfect to do short tests with more resources than my laptop, especially the predictability of performance, then I started a Bitnami Postgres Compute service on the Oracle Cloud and did some tests with pgbench and pgio.
The installation is easy:
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