NoSQL (Not only SQL) term covers different database technologies that were developed to handle huge unstructured data volumes and the frequency in which this data is accessed as well as performance and processing needs. NoSQL is NOT a database that we used to think. In my understanding NoSQL is a range of technologies, approaches and even projects directed to realization of unstructured – so-called Big Data models – that have considerable differences to traditional Relational Database Management Systems (RDBMS). The main goal of NoSQL storage systems is to resolve unstructured data manipulation issues that are not efficiently managed by RDBMS. Note, I’m not saying RDBMS can not manage the same unstructured stuff. On the contrary, with new features the modern RDBMS can do more and more in the Big Data area but obviously not as efficient as some NoSQL solutions.
In practice, “NoSQL” means non-relational database, but this concept of NoSQL does not disclaim SQL language completely often however querying these databases in data-model specific way. Anyway, the underlying query execution strategy is usually very different from the way a traditional RDBMS would use.
Many of the NoSQL platforms allow for RESTful interfaces to the data, while others offer query APIs. NoSQL storage systems are often used in combination with Hadoop Distributed File System that enables large scale computations on clustered servers.
Important to mention that NoSQL databases are not General-Purpose Database Management Systems, but they are very specific to application use cases. Also, NoSQL engines are not compatible to each other and NOT to the General-Purpose DBMSs.
See below some common attributes of noSQL databases:
• Specialized databases for various scenarios
• They manage Unstructured Data (Big Data)
• Often as pure data stores offering just standard APIs for developers
• Non-Relational data model is optimized for the type of data being stored
• Underlying query execution done in data-model specific way
• Usually do NOT support ANSI SQL and ACID standards
• Usually do NOT support transactions and data consistency across entire DB
• Usually optimized for high performance data loads and reads
• Data Updates are often slow
• Table Joins are usually NOT supported
• Usually do NOT include comprehensive Database Manageability features
The data in NoSQL DB engines are represented in a view of hash-tables, tries, documents etc. Based on this representation approach they are divided into four major categories or types: Key-Value Stores, Document Databases, Column Stores and Graph databases. See their description below:
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The 28th edition of DOAG 2015 annual Conference + Exhibition was held November 17 – 20, 2015 in Nuremberg Conference Center. Participants had the opportunity to attend the exibition and daily lecture programs with more than 500 talks and international top speakers, plus a wide choice of workshops, and community activities.
Interseting key notes including one with Andrew Mendelsohn, Executive Senior Vice President at Oracle, who shared the information about new developments in Oracle database 12c.
This was a great opportunity for everyone to expand the knowledge and benefit from the know-how of the Oracle community.
Thanks for all the organizers and participants.
I was speaking there also with a database related topic called:
Official Abstract of my DOAG presentation:
How to design an Oracle database system to minimize planned interruptions? That depends on the requirements, goals, SLAs etc. The presentation will follow top-down approach. First we will describe major types of planned maintenance, prioritize those and then based on the system availability requirements find the best cost-effective technics to address those. A bit of planning, strategy and of course modern OS and database technics including latest Oracle 12c features will follow.
Timelines and location: Thursday, 2015-11-19, room Shanghai
The presentation consists of 3 major parts including Linux and SQL code examples:
– System downtimes and high availability basics
– Reducing planned downtime approach and methodology
– Technical part: system configurations, technics, new Oracle 12c features
See the slightly adjusted presentation material below:
– Presentation: Reduce planned database down time with Oracle technology
– Scripts: will follow
Please share your ideas, expririence or ask questions in the Comments to this post below.
You can also review my previouse DOAG 2014 presentation including live demo: Live adventure – From my PC to Oracle remote database
Some a few photos below… Read more »
I remember when long time ago one database consultant confused my manager saying that our Oracle 9i database had poor performance just taking into account a slow response from dba_segments data dictionary view. That was a nasty trick to blame a DBA and the Oracle database for poor performance at that time. In fact there were a few Oracle bugs related to those performance issues after switching from dictionary to locally managed tablesspaces at that time. Recently I’ve noticed similar performance degradation on Oracle 11gR2 (188.8.131.52 and 184.108.40.206) by querying DBA_SEGMENTS or USER_SEGMENTS data dictionary views involving the columns BYTES, BLOCKS, or EXTENTS. Queries on DBA_TS_QUOTAS or USER_TS_QUOTES on columns BYTES or BLOCKS were also slow.
Even if you personally do not care about these dictionary views they are still very important since they are used by some Oracle internal components and the other database tools including Oracle Enterprise Manager (OEM) Cloud Control and its Database Home Page. Thus, I’ll describe below the problematic of those data dictionary views and the way how to fix their performance issues.
First of all do not wonder why queries against those views often seem to slow. DBA_SEGEMENTS for example is a very complex view that is built on another SYS_DBA_SEGS view. In summary DBA_SEGMENTS view on Oracle 11gR2 consists of the following components:
– 25 columns
– around 110 lines of SQL code
– 3 UNION ALL clauses
– A lot of joins between following tables: sys.user$, sys.ts$, sys.undo$, sys.seg$, sys.file$
This post will help to analyze Oracle database instance slowdown that can happen due to considerable row cache lock (enqueue) wait events. It’s is based on a real case of a database hang that I worked on recently. I must admit this type of situation does not appear often but it’s very dangerous since it can considerably slow down a database instance or even freeze it for a short period of time. In most cases SQL against ASH view and Systemstate dumps can help to nail down the problem unless this is an Oracle bug.
Usually it occurs suddenly and disappears quickly. See an example ASH graph below with brown peak that represents this type of concurrency: row cache lock wait events.
What is a Row Cache Enqueue Lock?
The Row Cache or Data Dictionary Cache is a memory area in the shared pool that holds data dictionary information. Row cache holds data as rows instead of buffers. A Row cache enqueue lock is a lock on the data dictionary rows. It is used primarily to serialize changes to the data dictionary and to wait for a lock on a data dictionary cache. The enqueue will be on a specific data dictionary object. This is called the enqueue type and can be found in the v$rowcache view. Waits on this event usually indicate some form of DDL occurring, or possibly recursive operations such as storage management, sequence numbers incrementing frequently, etc. Diagnosing the cause of the contention
Diagnosing the cause of the contention
My next post is an answer to the below question about Oracle SQL CASE expression and the way how it manipulates with NULL values compared to DECODE function. This topic in fact is very common in Oracle database developers’ area.
Could you please help me to clarify below mention doubt.
select decode (null,null,’true’,’false’) as value from dual
select case null when null then ‘true’ else ‘false’ end as Case_Test from dual
OUTPUT :- FALSE
Why above mention queries are giving different output and what is the difference between case and decode?
CASE expression treats NULLs in Oracle database SQL a bit differently compared to DECODE. You have to understand that the Boolean expressions usually result in TRUE or FALSE , but NULLs introduce a third possible result which is : UNKNOWN . At the same time NULL is not the same as UNKNOWN. See an example below:
sum + NULL results in NULL (this is a Scalar expression.)
NULL = NULL results in UNKNOWN (this is a Boolean expression.)
sum < NULL results in UNKNOWN (this is a Boolean expression.)
Ok. Now let’s explore the Oracle SQL CASE expression itself.
CASE introduces two ways of conditional expressions: Simple CASE and Searched CASE. Read more »