Viva Las Vegas!

Viva Las Vegas!

It was our second time at the IOUG Collaborate conference, and we loved it! With thousands of database and application professionals attending, with such a rich agenda of interesting sessions, how could anybody go wrong?! Meals, activities, and other logistics were very well organized. Despite the large number of participants, lines moved quickly, Wi-Fi connections were reliable, and everything went smoothly. Most importantly, the quality of the talks was excellent all around. Our sense was that attention was concentrated on applications rather than infrastructure, which was not surprising to us.

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Why Your DR Solution Doesn’t Protect Against Rolling Disasters (Don’t Say We Didn’t Warn You)

Why Your DR Solution Doesn’t Protect Against Rolling Disasters (Don’t Say We Didn’t Warn You)

You may think that your organization’s disaster recovery solution is full-proof, but you’re probably wrong. In fact, even synchronous replication cannot ensure zero data loss and rapid recovery in a rolling disaster scenario. A rolling disaster usually starts with a power outage or a problem with network communication lines. It is an event in which replication to the secondary (disaster recovery) site fails before the primary (production) data center fails. During the interim between replication failure and failure of the primary data center, the primary data center’s production servers continue to produce data that is not being replicated (unless you have Axxana’s Phoenix). Suffice it to say, when data is created and not replicated—even for a few seconds—data loss is guaranteed.

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AWR Generating & Setting

Oracle database is gathering statistics periodically (snapshots), these statistics can be used for analyzing database performance. These statistics are kept in the Automatic Workload Repository (AWR).

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Using Standby as an Alternate for Far Sync (12c): Limitations and Considerations

Using Standby as an Alternate for Far Sync (12c): Limitations and Considerations

The recommended Oracle® Data Guard configuration is in Maximum Availability mode, when using Oracle Far Sync, which is located near the primary site: Primary Database > Far Sync Instance – Network input/output (I/O) is synchronous (Sync). Far Sync Instance > Standby Database – Network I/O is asynchronous (Async). Primary Database > Standby Database  – As an alternate (when Far Sync is not reachable), network I/O is asynchronous (Async).

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Oracle 12c Release 2 New Features for Active Data Guard

Oracle 12c Release 2 New Features for Active Data Guard

At Oracle Open World 2016 I collected the main improvements and changes are going to be implemented in Oracle Database 12 Release 2: Data Guard Creation with dbca, supports automatic creation of Standby database and Far Sync instance not for RAC (at this point) with the command: dbca -silent -createDuplicateDB     -PrimartDBConnectionString myprymary.domin:1523/chicago.domain     -gdbName chicago.domain -sid boston     -initParams instance_name=boston -createAsStandby     -customScripts /tmp/test.sql Multi-Instance Redo Apply, Parallel, multi-instance recovery – when standby is RAC, all its instances will use the MRP0 process for applying Supports Diagnostic Pack on Active Data Guard AWR+ADDM Reports for Standby are kept in the Primary Supports Tuning Pack and SQL Plan Analyzer Fast Failover – Data Guard take over session draining (switchover to dallas_dr wait;) Read-only sessions connected to Active Data Guard Remain connected during the failover / switchover Become Read/Write after Active Data Guard becomes the primary Password file is managed and transported via the Redo mechanism Alternate prioritization – you will be able to group some destinations and give them a priority over some other destinations. This enables you to decide what will happen when the main destination is back (failback) Support In-Memory Column Store – redo data contains all the information needed for the standby to benefit from the In-Memory Column Store feature. Yossi Nixon Chief Database Architect  

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Disaster Recovery Using Cellular Networks – Are These Six Misconceptions Keeping You from Zero Data Loss and Near-Zero RTO?

Disaster Recovery Using Cellular Networks – Are These Six Misconceptions Keeping You from Zero Data Loss and Near-Zero RTO?

Although disaster scenarios may be carefully addressed in the DRP, meeting the overall recovery time objective (RTO)—especially in asynchronous replication environments—is much less assured. This uncertainty is mainly due to data loss. When you enter the realm of data loss, figuring out what you have lost and then trying to reconcile the data can slow data recovery time to a crawl. Even seconds of data loss can entail hours of downtime as you work to recover data—costing your organization dearly in terms of lost revenue, reputation damage, labor costs, and much more.

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Protect Your Company’s Most Critical Data – Without Breaking into a Sweat

Protect Your Company’s Most Critical Data – Without Breaking into a Sweat

You know the story:  99% (usually less) of your data is protected, and you got management to waive 100% protection by using the old “it’s just impossible” or “it’s too expensive” excuse. Even so, you never want to be the one who bears the news, “Sorry, we lost data, and it can’t be recovered…and by the way, it wasn’t just any 1% of data; it was the data that is most critical to our business.” Once disaster strikes, no one remembers those management waivers. They remember names. And guess whose name will be at the top of the list…

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Disaster Recovery – From Practice to Theory?

Disaster Recovery – From Practice to Theory?

I was recently invited to give a talk at a Research institution about the products we are developing in Axxana. This forced me to step back and look at Disaster Recovery from a more “rigorous” point of view. Here are some key observations. First, let me start with a disclaimer. Our initial focus is on transactional environments where data loss translates very directly into lost revenue, reputation damage and in some extreme cases could lead to business closure. We do not focus on large scale systems that provide “eventual consistency,” but on classical environments that have focused on strong consistency models.

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