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How to Convert BMP to JPG: 2 Easy Ways!

VitalSoftTech - Tue, 2019-10-22 08:45

Are you looking to convert BMP to JPG? There are plenty of ways you can do that, through software installed in your laptop or through online BMP to JPG converters. We’ll take a look at some of the way you can make a conversion in this article! But first, let’s take a look at what […]

The post How to Convert BMP to JPG: 2 Easy Ways! appeared first on VitalSoftTech.

Categories: DBA Blogs

srvctl config all

Michael Dinh - Tue, 2019-10-22 08:19

Learned something new today and not sure if it’s new feature.

Seems a lot easier to gather clusterware configuration using one command.

Works with srvctl version: 18.0.0.0.0 or higher.

19c

oracle@ol7-19-rac2 ~]$ echo $ORACLE_HOME
/u01/app/19.0.0/grid

[oracle@ol7-19-rac2 ~]$ srvctl -version
srvctl version: 19.0.0.0.0

[oracle@ol7-19-rac2 ~]$ srvctl config all

Oracle Clusterware configuration details
========================================

Oracle Clusterware basic information
------------------------------------
  Operating system          Linux
  Name                      ol7-19-cluster
  Class                     STANDALONE
  Cluster nodes             ol7-19-rac1, ol7-19-rac2
  Version                   19.0.0.0.0
  Groups                    SYSOPER: SYSASM:dba SYSRAC:dba SYSDBA:dba
  OCR locations             +DATA
  Voting disk locations     DATA
  Voting disk file paths    /dev/oracleasm/asm-disk3

Cluster network configuration details
-------------------------------------
  Interface name  Type  Subnet           Classification
  eth1            IPV4  192.168.56.0/24  PUBLIC
  eth2            IPV4  192.168.1.0/24   PRIVATE, ASM

SCAN configuration details
--------------------------

SCAN "ol7-19-scan" details
++++++++++++++++++++++++++
  Name                ol7-19-scan
  IPv4 subnet         192.168.56.0/24
  DHCP server type    static
  End points          TCP:1521

  SCAN listeners
  --------------
  Name              VIP address
  LISTENER_SCAN1    192.168.56.105
  LISTENER_SCAN2    192.168.56.106
  LISTENER_SCAN3    192.168.56.107


ASM configuration details
-------------------------
  Mode             remote
  Password file    +DATA
  SPFILE           +DATA

  ASM disk group details
  ++++++++++++++++++++++
  Name  Redundancy
  DATA  EXTERN

Database configuration details
==============================

Database "ora.cdbrac.db" details
--------------------------------
  Name                ora.cdbrac.db
  Type                RAC
  Version             19.0.0.0.0
  Role                PRIMARY
  Management          AUTOMATIC
  policy
  SPFILE              +DATA
  Password file       +DATA
  Groups              OSDBA:dba OSOPER:oper OSBACKUP:dba OSDG:dba OSKM:dba OSRAC:dba
  Oracle home         /u01/app/oracle/product/19.0.0/dbhome_1
[oracle@ol7-19-rac2 ~]$

18c

[oracle@rac1 Desktop]$ srvctl -version
srvctl version: 18.0.0.0.0

[oracle@rac1 Desktop]$ srvctl config all

Oracle Clusterware configuration details                                        
========================================                                        

Oracle Clusterware basic information                                            
------------------------------------                                            
  Operating system         Linux                                           
  Name                     scan                                            
  Class                    STANDALONE                                      
  Cluster nodes            rac1, rac2                                      
  Version                  18.0.0.0.0                                      
  Groups                   SYSOPER:dba SYSASM:dba SYSRAC:dba SYSDBA:dba    
  Cluster home             /u01/app/18.0.0/grid                            
  OCR locations            +CRS                                            
  Voting disk locations    /dev/asm-disk8, /dev/asm-disk9, /dev/asm-disk7  

Cluster network configuration details                                           
-------------------------------------                                           
  Interface name  Type  Subnet           Classification  
  eth1            IPV4  10.1.1.0/24      PRIVATE, ASM    
  eth0            IPV4  192.168.11.0/24  PUBLIC          

SCAN configuration details                                                      
--------------------------                                                      

SCAN "scan.localdomain" details                                                 
+++++++++++++++++++++++++++++++                                                 
  Name                scan.localdomain  
  IPv4 subnet         192.168.11.0/24   
  DHCP server type    static            
  End points          TCP:1521          

  SCAN listeners                                                                
  --------------                                                                
  Name        VIP address    
  LISTENER    192.168.11.60  


ASM configuration details                                                       
-------------------------                                                       
  Mode             remote  
  Password file    +RAC    
  SPFILE           +RAC    

  ASM disk group details                                                        
  ++++++++++++++++++++++                                                        
  Name  Redundancy  
  CRS   NORMAL      
  DATA  EXTERN      
  FRA   EXTERN      
  RAC   EXTERN      

Database configuration details                                                  
==============================                                                  

Database "ora.uptst.db" details                                                 
-------------------------------                                                 
  Name                ora.uptst.db                                                   
  Type                RAC                                                            
  Version             18.0.0.0.0                                                     
  Role                PRIMARY                                                        
  Management          AUTOMATIC                                                      
  policy                                                                             
  SPFILE              +DATA                                                          
  Password file       +DATA                                                          
  Groups              OSDBA:dba OSOPER:dba OSBACKUP:dba OSDG:dba OSKM:dba OSRAC:dba  
  Oracle home         /u01/app/oracle/product/18.0.0/db_home1                        

Database "ora.uptst2.db" details                                                
--------------------------------                                                
  Name                 ora.uptst2.db                                        
  Type                 RAC                                                  
  Version              12.1.0.2.0                                           
  Role                 PRIMARY                                              
  Management policy    AUTOMATIC                                            
  SPFILE               +DATA                                                
  Password file        +DATA                                                
  Groups               OSDBA:dba OSOPER:dba OSBACKUP:dba OSDG:dba OSKM:dba  
  Oracle home          /u01/app/oracle/product/12.1.0.2_1                   
[oracle@rac1 Desktop]$ 

The Juilliard School and Rutgers Tap Oracle Cloud to Manage Educational Back-office System

Oracle Press Releases - Tue, 2019-10-22 07:00
Press Release
The Juilliard School and Rutgers Tap Oracle Cloud to Manage Educational Back-office System Leading colleges/universities entrust complex global HR, finance and student operations to Oracle Cloud

Redwood Shores, Calif.—Oct 22, 2019

To manage complex environments globally, leading higher education institutions The Juilliard School and Rutgers University are turning to Oracle Cloud. Both institutions, looking to accelerate student success through more efficient operations across multiple schools and campuses, have chosen Oracle Enterprise Resource Planning (ERP) Cloud, Oracle Human Capital Management (HCM) Cloud, and Oracle Student Cloud.

Founded in 1905, The Juilliard School is a world-famous leader in performing arts education. The school’s mission is to provide the highest caliber of artistic education for gifted musicians, dancers, and actors from around the world. Located in New York City, Juilliard offers undergraduate and graduate degrees in dance, drama and music. Beyond its New York campus, Juilliard is now defining new directions in global performing arts education through its new branch campus in Tianjin, China, The Tianjin Juilliard School, and the global K-12 curricula.

Adopting a global operating model created challenges and complexity for Juilliard’s IT staff. Oracle Cloud provides a unified platform that will enable Juilliard to deliver an exceptional experience in both English and Chinese, and importantly, can be managed with existing IT resources. Once deployed, the Oracle Student Cloud solution will support approximately 2,000 Juilliard students across both campuses and 800 faculty and staff using the ERP, Student, and HCM Cloud solutions in New York.

“The opening of The Tianjin Juilliard School will broaden access to Juilliard’s world-class performing arts education across China and Asia,” said Carl Young, CIO of The Juilliard School. “With Oracle Cloud we will be able to provide our campuses in New York and Tianjin with unified, modern, multi-lingual systems that will have a significant impact on information management, which will ultimately help us better serve our students.”

Rutgers is a leading national research university and the state of New Jersey’s preeminent, comprehensive public institution of higher education. Established in 1766, the university is the eighth oldest higher education institution in the United States and home to more than 150 undergraduate majors, more than 400 graduate programs, and 300 research centers and institutes.

The daunting scale of Rutgers—with more than 70,000 students across 29 different schools and colleges at several campus locations—means it faced different, yet similarly formidable, issues of complexity. As part of a bold five-year plan to modernize and unify its key systems, Rutgers chose Oracle Cloud. 

“We strive to provide new and better experiences for our students,” said Michele Norin, senior vice president and chief information officer at Rutgers University. “The size and scale of Rutgers presents many difficulties as we work to manage and develop our systems. Oracle Cloud helps us meet our goals, stay on the forefront of higher education as it evolves, and enable our students to be more successful.”

Oracle Student Cloud is an integral component to an institution’s efforts to reduce educational costs while enhancing student outcomes and success. Oracle SFP Cloud streamlines the financial aid processes and delivers invaluable, data-backed insights into student successes, freeing administrators to focus more on supporting the academic needs of its students. Oracle ERP Cloud provides the financial solutions needed for institutions to enhance productivity, reduce costs and improve controls. Oracle HCM Cloud supports the changing business requirements around talent management and talent acquisition while furthering workforce productivity.

Contact Info
Katie Barron
Oracle
+1.202.904.1138
katie.barron@oracle.com
Kristin Reeves
Oracle
+1.925.787.6744
kris.reeves@oracle.com
About Oracle

The Oracle Cloud offers a complete suite of integrated applications for Sales, Service, Marketing, Human Resources, Finance, Supply Chain and Manufacturing, plus Highly Automated and Secure Generation 2 Infrastructure featuring the Oracle Autonomous Database. For more information about Oracle (NYSE: ORCL), please visit us at www.oracle.com.

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Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

Talk to a Press Contact

Katie Barron

  • +1.202.904.1138

Kristin Reeves

  • +1.925.787.6744

FAR SYNC FAILOVER

Tom Kyte - Tue, 2019-10-22 00:46
Dear Sir, In case of an outage on the primary database, the standard failover procedure applies, and after some time primary server available then how it going to sync all 3 server. please help me to understand this. Thanks Pradeep
Categories: DBA Blogs

Oracle query running slow

Tom Kyte - Tue, 2019-10-22 00:46
Hi Team, we have a SQL query which is a source query for the ETL load job, this take around 3 hours to run, could you please help us how we can make it run faster. The row count of the tables involved are as follows. D_PERSON 4618595 ...
Categories: DBA Blogs

Keep pooI

Tom Kyte - Tue, 2019-10-22 00:46
when I assign any of the segment to keep pool , It is nessesary to set appropriate DB_KEEP_CACHE_SIZE as per the sizes of the segment. or it will dynamically set DB_KEEP_CACHE_SIZE?
Categories: DBA Blogs

Limit parallelism

Tom Kyte - Tue, 2019-10-22 00:46
Hi ASKTOM team, I am not very good with parallelism, so have a question about DW database. These are my current settings: <code> parallel_adaptive_multi_user boolean TRUE parallel_automatic_tuning boolean FALSE parallel_degree_li...
Categories: DBA Blogs

Log Files in Oracle External Tables

Tom Kyte - Tue, 2019-10-22 00:46
My External Tables were working fine before i accidentally deleted all the .log and .bad files from the default location. Now I am getting below error ORA-29913: error in executing ODCIEXTTABLEOPEN callout ORA-29400: data cartridge error KUP-...
Categories: DBA Blogs

(Theoretical) Confusion with roles and public synonyms

Tom Kyte - Tue, 2019-10-22 00:46
Hi Tom, <b>Confusion1:- </b> Suppose I have three user accounts in my database: A, B and C. 'A' user has the privilege to create a role. Suppose there is a table named 'employee' in schema 'A' and 'A' issues:- 1. create role GiveAccess; 2...
Categories: DBA Blogs

Stat gather impact on production environment

Tom Kyte - Mon, 2019-10-21 12:45
On OLTP production environment, during huge transaction period, what is an impact if we run the stat gather of used schema for transaction???, It will missed any indexes, and other operation issues???
Categories: DBA Blogs

Best practice for "archiving" legacy tables and their data

Tom Kyte - Mon, 2019-10-21 12:45
Hi, I recently removed the last piece of front-end functionality that relied on a table, and am certain that that table and its data is no longer needed for the application to function. We'll have more similar tables in this situation in the near ...
Categories: DBA Blogs

Fetch across commit

Tom Kyte - Mon, 2019-10-21 12:45
what do you mean by 'Fetch across commit'
Categories: DBA Blogs

Is there a maximum number of schemas that can be included in a datapump par file?

Tom Kyte - Mon, 2019-10-21 12:45
I've been tasked with migrating a very large warehouse database (9TB) from hardware in one data center to new hardware in a different data center. For various reasons, the method I've selected for the migration is datapump. I'm breaking up the data...
Categories: DBA Blogs

Moat Wins Adweek “Reader’s Choice” Award for Best Brand Safety/Verification Solution

Oracle Press Releases - Mon, 2019-10-21 06:00
Blog
Moat Wins Adweek “Reader’s Choice” Award for Best Brand Safety/Verification Solution

By Kurt Kratchman, GVP, Product Development, Oracle Data Cloud—Oct 21, 2019

 

If you want to find the best product or service, you should ask the people who know that product or service best for their recommendations.

Want a reliable dishwasher? Check out the online reviews from appliance installers and repairmen. In the market for a new car?  Explore the consumer satisfaction surveys from thousands of current owners. Searching for the perfect pizza in Pittsburgh? Pick up a copy of the “Best of Pittsburgh” poll in the local City Paper.

The same holds true in digital advertising, which is why we are so honored and delighted to announce that Moat by Oracle Data Cloud is the winner of the 2019 Adweek “Readers’ Choice: Best of Tech” Award for Brand Safety/Verification.

The awards are based on an Adweek reader survey in which more than 50,000 votes were cast by executives from leading brands and agencies for their favorite technology providers and solutions. These experts know our industry the best, as they evaluate and make mission-critical decisions on a daily basis about which technologies, products, and solutions they should use to support their brands.

Equally important, these executives measure success by the real-world impact of their marketing investments. Therefore, they dedicate significant time and resources to finding technology partners who can help keep their brands safe, ensure their ads are viewable, and protect their campaigns from invalid traffic and fraud, which are the cornerstone of our Moat products.

Reinforcing Moat’s industry leadership, MediaPost editor-in-chief Joe Mandese noted in a column last week that Moat “has become the closest thing to a de facto currency for digital audience attention.”

Moat offers a range of products to help customers ensure their advertising dollars are well-spent, including our industry-leading Moat Analytics and Pre-Bid by Moat, which help identify brand-safe, fraud-safe, and viewable inventory before an ad is placed. Part of that solution is Oracle’s Contextual Intelligence, which provides real-time content review and classification across the majority of the addressable footprint of the open web. That functionality gives advertisers greater control over the safety and suitability of content near their ads.

Moat customers, along with their marketing peers, added their strong voice to the vote results. “To be effective, advertising needs to be viewable by real people in brand-safe environments, which is why rigorous standards for ad measurement, verified by third party independent providers is so important,” said Yale Cohen, EVP Digital Investment Ad Standards, Publicis Media. “As a long-term Moat customer, we congratulate the Oracle Data Cloud team on this award.”

“As a Publisher, it is critical we provide our clients with the confidence that their ad dollars are being spent wisely on our platforms,” added Nicole Lesko, SVP Ad Product & Revenue for Meredith Corporation. “As a customer of Moat, we have come to rely on their best-in-class measurement to ensure our inventory is bot-free, viewable and safe across channels.”

Beyond Moat’s win, Oracle Data Cloud’s recognition in the Adweek survey extended across its full product suite, with additional top-three finishes against scores of companies in the categories of Data Supplier (Oracle Data Cloud) and Measurement and Analytics: Digital and Mobile Display Advertising (Moat). In fact, Oracle Data Cloud secured a top-three finish in every one of its product categories from Audience to Measurement and Context.

As a data-driven company, we are delighted that Adweek’s analysis of votes by agency and brand executives supports what hundreds of their peers already know: If you want to protect your brand with the best viewability, fraud, and brand safety solutions, there’s only one Moat.

For more information about Moat by Oracle Data Cloud and the full range of solutions offered, including brand safety, viewability and IVT measurement, please visit Moat.com.

Index Rebuild and analyze

Tom Kyte - Mon, 2019-10-21 03:45
Hello Tom , I have a query regarding Index rebuild . what according to you should be time lag between index rebuilds. We are rebuilding indexes every week .but we found it is causing lot of fragmentation. is there any way we could find out whet...
Categories: DBA Blogs

Views of Views

Tom Kyte - Mon, 2019-10-21 03:45
I remember hearing some time ago that creating views based upon other existing views should be avoided as it can often confuse the optimiser and result in full table scans. I expect that this is just another urban myth however I would be intereste...
Categories: DBA Blogs

Inserts with APPEND Hint.

Tom Kyte - Mon, 2019-10-21 03:45
<code>insert /*+ append */ into t select rownum,mod(rownum,5) from all_objects where rownum <=1000 call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- -----...
Categories: DBA Blogs

Kill a session from database procedure

Tom Kyte - Mon, 2019-10-21 03:45
How i can kill a session from a stored database procedure. There is some way to do this?
Categories: DBA Blogs

Solr Sharding – Concepts & Methods

Yann Neuhaus - Sun, 2019-10-20 03:06

A few weeks ago, I published a series of blog on the Alfresco Clustering, including Solr Sharding. At that time, I planned to first explain what is really the Solr Sharding, what are the different concepts and methods around it. Unfortunately, I didn’t get the time to write this blog so I had to post the one related to Solr even before explaining the basics. Today, I’m here to rights my wrong! Obviously, this blog has a focus on Alfresco related Solr Sharding since that’s what I do.

I. Solr Sharding – Concepts

The Sharding in general is the partitioning of a set of data in a specific way. There are several possibilities to do that, depending on the technology you are working on. In the scope of Solr, the Sharding is therefore the split of the Solr index into several smaller indices. You might be interested in the Solr Sharding because it improves the following points:

  • Fault Tolerance: with a single index, if you lose it, then… you lost it. If the index is split into several indices, then even if you are losing one part, you will still have all others that will continue working
  • High Availability: it provides more granularity than the single index. You might want for example to have a few small indices without HA and then have some others with HA because you configured them to contain some really important nodes of your repository
  • Automatic Failover: Alfresco knows automatically (with Dynamic Registration) which Shards are up-to-date and which ones are lagging behind so it will choose automatically the best Shards to handle the search queries so that you get the best results possible. In combination with the Fault Tolerance above, this gives the best possible HA solution with the less possible resources
  • Performance improvements: better performance in indexing since you will have several Shards indexing the same repository so you can have less work done by each Shards for example (depends on Sharding Method). Better performance in searches since the search query will be processes by all Shards in parallel on smaller parts of the index instead of being one single query on the full index

Based on benchmarks, Alfresco considers that a Solr Shard can contain up to 50 to 80 000 000 nodes. This is obviously not a hard limit, you can have a single Shard with 200 000 000 nodes but it is more of a best practice if you want to keep a fast and reliable index. With older versions of Alfresco (before the version 5.1), you couldn’t create Shards because Alfresco didn’t support it. So, at that time, there were no other solutions than having a single big index.

There is one additional thing that must be understood here: the 50 000 000 nodes soft limit is 50M nodes in the index, not in the repository. Let’s assume that you are using a DB_ID_RANGE method (see below for the explanation) with an assumed split of 65% live nodes, 20% archived nodes, 15% others (not indexed: renditions, other stores, …). So, if we are talking about the “workspace://SpacesStore” nodes (live ones), then if we want to fill a Shard with 50M nodes, we will have to use a DB_ID_RANGE of 100*50M/65 = 77M. Basically, the Shard should be more or less “full” once there are 77M IDs in the Database. For the “archive://SpacesStore” nodes (archived ones), it would be 100*50M/20 = 250M.

Alright so what are the main concepts in the Solr Sharding? There are several terms that need to be understood:

  • Node: It’s a Solr Server (a Solr installed using the Alfresco Search Services). Below, I will use “Solr Server” instead because I already use “nodes” (lowercase) for the Alfresco Documents so using “Node” (uppercase) for the Solr Server, it might be a little bit confusing…
  • Cluster: It’s a set of Solr Servers all working together to index the same repository
  • Shard: A part of the index. In other words, it’s a representation (virtual concept) of the index composed of a certain set of nodes (Alfresco Documents)
  • Shard Instance: It’s one Instance of a specific Shard. A Shard is like a virtual concept while the Instance is the implementation of that virtual concept for that piece of the index. Several Shard Instances of the same Shard will therefore contain the same set of Alfresco nodes
  • Shard Group: It’s a collection of Shards (several indices) that forms a complete index. Shards are part of the same index (same Shard Group) if they:
    • Track the same store (E.g.: workspace://SpacesStore)
    • Use the same template (E.g.: rerank)
    • Have the same number of Shards max (“numShards“)
    • Use the same configuration (Sharding methods, Solr settings, …)

Shard is often (wrongly) used in place of Shard Instance which might lead to some confusion… When you are reading “Shard”, sometimes it means the Shard itself (the virtual concept), sometimes it’s all its Shard Instances. This is these concepts can look like:
Solr Sharding - Concepts

II. Solr Sharding – Methods

Alfresco supports several methods for the Solr Sharding and they all have different attributes and different ways of working:

  • MOD_ACL_ID (ACL v1): Alfresco nodes and ACLs are grouped by their ACL ID and stored together in the same Shard. Different ACL IDs will be assigned randomly to different Shards (depending on the number of Shards you defined). Each Alfresco node using a specific ACL ID will be stored in the Shard already containing this ACL ID. This simplifies the search requests from Solr since ACLs and nodes are together, so permission checking is simple. If you have a lot of documents using the same ACL, then the distribution will not be even between Shards. Parameters:
    • shard.method=MOD_ACL_ID
    • shard.instance=<shard.instance>
    • shard.count=<shard.count>
  • ACL_ID (ACL v2): This is the same as the MOD_ACL_ID, the only difference is that it changes the method to assign to ACL to the Shards so it is more evenly distributed but if you still have a lot of documents using the same ACL then you still have the same issue. Parameters:
    • shard.method=ACL_ID
    • shard.instance=<shard.instance>
    • shard.count=<shard.count>
  • DB_ID: This is the default Sharding Method in Solr 6 which will evenly distribute the nodes in the different Shards based on their DB ID (“alf_node.id“). The ACLs are replicated on each of the Shards so that Solr is able to perform the permission checking. If you have a lot of ACLs, then this will obviously make the Shards a little bit bigger, but this is usually insignificant. Parameters:
    • shard.method=DB_ID
    • shard.instance=<shard.instance>
    • shard.count=<shard.count>
  • DB_ID_RANGE: Pretty much the same thing as the DB_ID but instead of looking into each DB ID one by one, it will just dispatch the DB IDs from the same range into the same Shard. The ranges are predefined at the Shard Instance creation and you cannot change them later, but this is also the only Sharding Method that allows you to add new Shards dynamically (auto-scaling) without the need to perform a full reindex. The lower value of the range is included and the upper value is excluded (for Math lovers: [begin-end[ ;)). Since DB IDs are incremental (increase over time), performing a search query with a date filter might end-up as simple as checking inside a single Shard. Parameters:
    • shard.method=DB_ID_RANGE
    • shard.range=<begin-end>
    • shard.instance=<shard.instance>
  • DATE: Months will be assigned to a specific Shard sequentially and then nodes are indexed into the Shard that was assigned the current month. Therefore, if you have 2 Shards, each one will contain 6 months (Shard 1 = Months 1,3,5,7,9,11 // Shard 2 = Months 2,4,6,8,10,12). It is possible to assign consecutive months to the same Shard using the “shard.date.grouping” parameter which defines how many months should be grouped together (a semester for example). If there is no date on a node, the fallback method is to use DB_ID instead. Parameters:
    • shard.method=DATE
    • shard.key=exif:dateTimeOriginal
    • shard.date.grouping=<1-12>
    • shard.instance=<shard.instance>
    • shard.count=<shard.count>
  • PROPERTY: A property is specified as the base for the Shard assignment. The first time that a node is indexed with a new value for this property, the node will be assigned randomly to a Shard. Each node coming in with the same value for this property will be assigned to the same Shard. Valid properties are either d:text (single line text), d:date (date only) or d:datetime (date+time). It is possible to use only a part of the property’s value using “shard.regex” (To keep only the first 4 digit of a date for example: shard.regex=^\d{4}). If this property doesn’t exist on a node or if the regex doesn’t match (if any is specified), the fallback method is to use DB_ID instead. Parameters:
    • shard.method=PROPERTY
    • shard.key=cm:creator
    • shard.instance=<shard.instance>
    • shard.count=<shard.count>
  • EXPLICIT_ID: Pretty much similar to the PROPERTY but instead of using the value of a “random” property, this method requires a specific property (d:text) to define explicitly on which Shard the node should be indexed. Therefore, this will require an update of the Data Model to have one property dedicated to the assignment of a node to a Shard. In case you are using several types of documents, then you will potentially want to do that for all. If this property doesn’t exist on a node or if an invalid Shard number is given, the fallback method is to use DB_ID instead. Parameters:
    • shard.method=EXPLICIT_ID
    • shard.key=<property> (E.g.: cm:targetShardInstance)
    • shard.instance=<shard.instance>
    • shard.count=<shard.count>

As you can see above, each Sharding Method has its own set of properties. You can define these properties in:

  • The template’s solrcore.properties file in which case it will apply to all Shard Instance creations
    • E.g.: $SOLR_HOME/solrhome/templates/rerank/conf/solrcore.properties
  • The URL/Command used to create the Shard Instance in which case it will only apply to the current Shard Instance creation
    • E.g.: curl -v “http://host:port/solr/admin/cores?action=newCore&…&property.shard.method=DB_ID_RANGE&property.shard.range=0-50000000&property.shard.instance=0

Summary of the benefits of each method:
Solr Sharding - Benefits

First supported versions for the Solr Sharding in Alfresco:
Solr Sharding - Availability

Hopefully, this is a good first look into the Solr Sharding. In a future blog, I will talk about the creation process and show some example of what is possible. If you want to read more on the subject, don’t hesitate to take a look at the Alfresco documentation, it doesn’t explain everything, but it is still a very good starting point.

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Why cost for TABLE ACCESS BY INDEX ROWID to high for only one row

Tom Kyte - Sat, 2019-10-19 15:45
Dear Tom, I have problem with query on table have function base index. create index : <code> create index customer_idx_idno on Customer (lower(id_no)) ; --- id_no varchar2(40) </code> <b>Query 1:</b> execute time 0.031s but cost 5,149, 1 row ...
Categories: DBA Blogs

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