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Happening 2014. 10. 29. 17:01
OneNote의 화면캡처 단축키와 Windows8.1의 단축키 충돌에 관련된 추가 내용입니다. 참고로 보시길~
결국은 해결 방법은 이전에 올렸던 내용과 같이 OneNote의 단축키를 Registry에서 변경해서 사용하라는 내용입니다.
UPDATE: On Oct 17, 2013 Microsoft released Windows 8.1. With Windows 8.1 the Windows + Sshortcut now launches Bing Smart Search. To take screen clipping with OneNote on Windows 8.1 use Windows + Shift + S shortcut.
Windows + S will continue to work for OneNote screen clipping in Windows 8 and previous versions of Windows.
———————————-
Today Microsoft released the Consumer Preview for Windows 8.1, also known as Windows Blue. If you try it out you’ll notice a lot of awesome changes. But try out the Windows + S shortcut for screen clipping in OneNote and you’ll find it doesn’t work as expected! Don’t panic, a fix is coming soon and in the meantime, you can use the workaround below.
What is Windows + S?
The most loved keyboard shortcut in OneNote desktop, you can press Windows + S to invoke the screen clipper. This lets you select areas of your screen to send to your notes and puts it on to your clipboard to paste (CTRL + V) anywhere.
You can read about other OneNote shortcuts here: OneNote 2013 Keyboard Shortcuts
What has changed in Windows 8.1?
New in Windows 8.1, Windows + S brings up a Search experience designed to help you find things more quickly from any application. The new OneNote shortcut key will be Windows + Shift + S, but this won’t be available until later in the year as an update. We know how useful the old Windows + S shortcut key is, so we’re providing a workaround in the meantime.
How to set a new screenshot shortcut in Windows 8.1
For now, you can manually set a shortcut key to Windows + A. You’ll have to change the shortcut key through your registry. (Note: Unintended changes in your registry can cause problems, so make sure you follow these instructions exactly.)
1. Use Windows + R to pull up this dialog, and type regedit.
2. Now in the folders on the left, navigate down this path:
HKEY_CURRENT_USERSoftwareMicrosoftOffice15.0OneNoteOptionsOther
Note: The path requires the specific version of Office. In the path above, 15.0 refers to Office 2013. If you’re using Office 2010, type 14.0 in place of 15.0 at the end of the path. Replace 15.0 with12.0 if you’re using Office 2007.
3. In the folder named Other, right-click the white space underneath the files in that folder and select New, then select DWORD (32-bit) Value.
4. In the text entry field that pops up, type ScreenClippingShortcutKey. You just created a new DWORD. (If you are in Office 12.0, this DWORD will already exist.)
5. Right click this DWORD and select Modify, then in the Value field, type 41.
Your new shortcut key has been assigned to Windows + A. Now log off and log on again and you should be all set!
Remember, the Windows + Shift + S fix is coming later this year, but hopefully this will help you out until then!
For a full article on this topic, see an earlier blog post: Changing the OneNote Screen Clipping and New Side Note keyboard shortcuts.
————————- Download OneNote: onenote.com Follow OneNote: twitter.com/msonenote Like OneNote: facebook.com/MicrosoftOneNote
reference : http://blogs.office.com/2013/06/27/windows-8-1-preview-and-a-workaround-for-your-favorite-wins-shortcut-key/
Happening 2014. 10. 29. 16:57
OneNote를 사용하면서 Windows + S 단축키를 이용해서 화면 캡처(Screen Clipping)를 용이하게 사용해왔는데,
Windows 8에서는 OS의 단축키와 충돌이 나서 사용을 할 수 없다.
하여, 방법을 찾아보니 OneNote의 화면캡처 단축키를 Registry에서 변경하여 문제를 해결할 수 있다고 한다.
Microsoft Office 2013 productivity suite allows you to use Windows + S shortcut for screen clipping in OneNote. The combination keys clip note on the screen progressively. However, if your tablet or computer has upgraded to Windows 8.1, the app Hotkeys just stop working.
Windows 8.1 use Windows + S keyboard shortcut to bring up the Charm bar, so it break down the same hotkeys that is configured for OneNote 2013. You could try the following workaround to resolve the problem by creating a new screenshot shortcut.
* Press Windows + R, type regedit, and click on OK button.
* On the left side of Registry Editor, navigate down to the location:
HKEY_CURRENT_USER\Software\Microsoft\Office\15.0\OneNote\Options\Other
or
HKEY_CURRENT_USER\Software\Microsoft\Office\14.0\OneNote\Options\Other (15.0 이 없다고 고민하지 마시길~)
For Office 2010, use 14.0 to replace 15.0 above.
* In the right pane, right-click the blank space, select New > DWORD (32-bit) Value. * In the pop-up dialog, type ScreenClippingShortcutKey for the Value name. * Double-click on the ScreenClippingShortcutKey, and change the Value date from 0 to the below number:
41: you will use Windows Key + A as Screen Clipping Shortcut. (Recommend ^^) 42: Windows + B 57: Win + W
* Click OK to save and reboot your machine. Note: the Windows + Shift + S will fix the issues later.
Reference : http://www.surfacetablethelp.com/2013/09/onenote-screen-clippings-win-s-not-working-in-windows-8-1.html
IT News 2014. 10. 28. 11:26
RDBMS와 NoSQL Solution을 통틀어 주요 특징의 장단점을 기술하고, 4개의 Category 영역에 비율을 명시한 자료.
국내 DB업체로는 Altibase와 TMAXData(이전 Tibero)가 눈에 띄네요.
16 October 2014 ID:G00261660
Analyst(s): Donald Feinberg, Merv Adrian, Nick Heudecker
VIEW SUMMARY
The operational DBMS market continues to grow, with innovative products and features being delivered by both new and traditional vendors. Information management leaders will be particularly interested by the changes in the Leaders quadrant.
Market Definition/Description
The operational database management system (DBMS; see Note 1) market is defined by relational and nonrelational database management products that are suitable for a broad range of enterprise-level transactional applications. These include purchased business applications such as enterprise resource planning (ERP), customer relationship management and customized transactional systems built by an organization's development team. In addition, we include DBMS products that also support interaction data and observation data (see Note 2) as new transaction types. These products are also used both for purchased business applications, such as ERP, catalog management and security event management, and for customized systems.
Additionally, operational DBMSs should provide interfaces to independent programs and tools that permit, and govern the performance of, a variety of concurrent workload types. There is no presupposition that these DBMSs must support the relational model or the full set of data types in use today. Further, there is no requirement that the DBMS be a closed-source product; commercially supported open-source DBMS products are included in this market.
Operational DBMSs must include functionality to support backup and recovery, and have some form of transaction durability — although the atomicity, consistency, isolation and durability (ACID) model is not a requirement. For open-source DBMSs, maintenance and support must be available from a vendor that owns, or has substantial control over, the source code, and must be offered with a full General Public License (GPL) or an alternative.
For this Magic Quadrant, we define operational DBMSs as systems that support multiple structures and data types, such as XML, text, JavaScript Object Notation (JSON), audio, image and video content. They must include mechanisms to isolate workload resources and control various parameters of end-user access within managed instances of data (see Note 3). Emerging technologies, such as cloud-only DBMSs, are not included; nor are highly specialized engines such as graph-only or object databases, which may perform some transactions for small subsets of operational use cases. Products that "add a layer" to and require or embed a complete or near-complete implementation of another commercially marketed product, such as Oracle MySQL, are not included. Finally, "streaming" engines, whose use cases are dominated by immediate event processing, and which are rarely, if ever, used for subsequent management of the data involved, are also excluded.
For the purposes of this analysis, we treat all of a vendor's products as a set. If a vendor markets more than one DBMS product that can be used as an operational DBMS, we describe them in the section specific to that vendor, but we evaluate all of that vendor's products together as a single entity. Strengths and Cautions relating to a specific offering or offerings are noted in the individual vendor sections. It may be important for organizations to evaluate separate offerings as the range of choices broadens, and as purchasers more frequently pursue best-of-breed strategies.
Operational DBMSs may support multiple delivery models, such as stand-alone DBMS software, certified configurations, cloud (public and private) images or versions, and database appliances (see Note 4). These are discussed and evaluated together in the analysis of each vendor.
Magic Quadrant
Figure 1. Magic Quadrant for Operational Database Management Systems
Source: Gartner (October 2014)
Vendor Strengths and Cautions
Actian
Headquartered in Redwood City, California, U.S., Actian offers relational DBMS (RDBMS; Ingres) and embedded (PSQL) engines, both suitable for operational use. Following a recent repositioning, Actian focuses primarily on analytical use cases.
Actian did not respond to Gartner's requests for supplementary information or to review early drafts of this section. Our analysis is therefore based on other credible sources, including publicly available information, previous briefings with Actian, and interactions with users of Gartner's client inquiry service.
Strengths
- Large customer base: Actian claimed to have over 210,000 customers at mid-2013. Broad geographic and industry coverage for Ingres, and a loyal following for PSQL, remain in place in 2014.
- Rich portfolio: Actian's offerings provide modern features, including multiversion concurrency control (MVCC), object and geospatial support, and column-level encryption.
- Embeddable offering: Actian's PSQL gives it a means of entering the small-footprint, minimal-administration market that is so important to mobile and Internet of Things applications.
Cautions
- Complex portfolio, focused elsewhere: Actian's recent positioning efforts are aimed at analytics, not operational use cases.
- Deployment challenges: Actian received the lowest scores from customers surveyed in 2013 for ease of use, and very low scores for ease of implementation. Our interactions with users of Gartner's client inquiry service in 2014 paint the same picture.
- Market focus: Gartner's interactions with clients continue to indicate a lack of momentum from Actian in relation to its operational DBMS and support of its Ingres DBMS.
Aerospike
Headquartered in Mountain View, California, U.S., and founded in 2009, Aerospike markets a hybrid in-memory/flash NoSQL DBMS — a real-time data platform — for the operational transaction market. It is available both as an open-source community version and an Enterprise Edition.
Strengths
- Operational DBMS functionality: Aerospike's offering makes hybrid use of DRAM and flash as addressable memory and not as file system support. Synchronous copies and support for multiple data centers add high availability (HA) and disaster recovery (DR) capabilities. Aerospike's hybrid DBMS structure supports JSON and NoSQL key-value data.
- Marketing and hardware ecosystem: Aerospike has strong partnerships with hardware component vendors for DRAM and flash memory. Its marketing focuses on the market segment that requires high transaction rates with near-100% availability.
- Performance: Aerospike's reference customers supported its claims of high performance by awarding it the highest scores for performance of any vendor in this Magic Quadrant. They also gave it the highest score for ease of doing business.
Cautions
- Lack of full functionality: Aerospike is strong in HA functionality but lacking in some basic SQL and NoSQL functions, although it has added some SQL functionality.
- Competitive positioning: With an increasing number of vendors supporting in-memory DBMSs, Aerospike will need to differentiate itself more clearly. Many vendors have much larger marketing programs — a disadvantage for Aerospike.
- HA/DR and ease of programming: Aerospike's reference customers identified difficulties in programming and the management of HA/DR as weaknesses of its product, probably due to the complexities of DR.
Altibase
Headquartered in Seoul, South Korea and Palo Alto, California, U.S., Altibase offers Altibase HDB, an SQL operational DBMS capable of using in-memory, traditional disk or hybrid storage. Altibase XDB is an in-memory-only DBMS.
Strengths
- Performance and support: Reference customers gave Altibase high marks for the overall performance of its operational DBMS, and for its support, professional services and ease of use.
- Broad use case applicability: In addition to applying its technology to unique billing scenarios in the telecommunications sector and real-time flaw detection in manufacturing scenarios, customers use Altibase HDB for analytics and storage of textual and rich-media content.
- Product maturity: Over 85% of Altibase's reference customers reported no problems with its product.
Cautions
- Limited global penetration: Although successful in Asian markets, Altibase has yet to establish much brand awareness or penetration elsewhere. The company has recently established several partnerships with global organizations.
- Shortage of large reference customers: Reference customers themselves stated that Altibase's limited number of globally recognized reference customers made adoption more difficult.
- Narrow product focus: Altibase does not support alternative consistency models or support multimodel capabilities, such as documents and graphs.
Basho Technologies
Newly headquartered in Seattle, Washington, U.S., Basho Technologies offers Riak, a distributed, masterless key-value store. It is available as a free, open-source, Apache-licensed download, as an Enterprise Edition, and as Riak CS, a multitenant cloud object store. Basho offers an Amazon Simple Storage Service (S3) API and a cloud service.
Strengths
- Resilience: Riak provides multi-data-center distribution and replication with automated balancing; it does not fail upon server failure or network partition.
- Rich features: Riak offers secondary indexes, MapReduce, support for JSON, tunable consistency, multiple programming languages, Apache Solr support and pluggable storage engines.
- Growing paid customer base: Basho's customers include one-third of the Fortune 500 companies in North America and EMEA. It also has a strong community, which contributes to the product.
Cautions
- Single architecture focus: Riak's key-value-only architecture limits its broader adoption and therefore restricts Basho to the Niche Players quadrant in the operational DBMS market.
- Growing competition: Major vendors will continue to add key-value functionality (such as Microsoft Azure Tables and Oracle NoSQL, both already available), which will create additional competition for key-value use cases.
- Recent changes to management team and reorganization of company: These suggest that prospective customers should conduct a careful assessment before making major commitments to Basho, even though its funding is strong and likely to grow.
Cloudera
Headquartered in Palo Alto, California, U.S., Cloudera offers Cloudera Enterprise, a commercial version of Apache Hadoop for which Apache HBase provides the operational DBMS capabilities. Cloudera Enterprise is available on-premises, as an appliance and through various cloud providers.
Strengths
- Support for emerging use cases: 80% of Cloudera's reference customers use Cloudera Enterprise for storing and processing machine-generated data, such as clickstreams and sensor data. Using this observational data in transactions is now possible with HBase.
- Scalability: Cloudera's reference customers repeatedly mentioned Cloudera Enterprise's ability to scale to accommodate massive data volumes.
- Stability and ecosystem: Cloudera has raised over $1.2 billion in venture funding and has developed a large partner ecosystem that encompasses every relevant segment of the enterprise software market.
Cautions
- Challenging implementation and use: Reference customers scored Cloudera lowest of all the vendors in this Magic Quadrant for ease of implementation. It also scored poorly for ease of operation and programming, and support and documentation.
- Lack of differentiation: Cloudera's operational DBMS, Apache HBase, is also offered by its competitors.
- Focus: The operational DBMS is only one component of Cloudera Enterprise. It may have to compete for development and support resources with the rest of the product suite.
Clustrix
Headquartered in San Francisco, California, U.S., Clustrix offers a low-administration, shared-nothing, distributed RDBMS, ClustrixDB, with automatic sharding and replication. It is available as on-premises software and in the cloud. Clustrix also provides a managed database as a service.
Strengths
- Performance: Clustrix provides extreme scale-out clustering for performance and availability. Parallel SQL query execution across a cluster supports hybrid transaction/analytical processing (HTAP) use cases.
- Simplicity: The Clustrix database is designed to be largely self-managed, to reduce operational complexity and total cost of ownership (TCO). Integration is simplified by the implementation of the MySQL wire protocol.
- E-commerce focus: Clustrix recently announced a new focus on applying its scale-out DBMS to e-commerce applications that face scaling challenges.
Cautions
- Lack of multimodel capabilities: ClustrixDB offers no support for data types beyond traditional relational ones. More than half its reference customers have deployed another operational DBMS to support nonrelational use cases.
- Value challenges: Clustrix received low scores from its reference customers for overall value for money. However, all reference customers were using the Clustrix appliance, not a software-only product.
- Poor early performance: Half the Magic Quadrant survey respondents who did not select ClustrixDB stated that this was because its product performed poorly during a proof of concept (POC) exercise.
Couchbase
Headquartered in Mountain View, California, U.S., Couchbase offers an open-source, distributed multimodel (document and key value) NoSQL DBMS, Couchbase Server. It is offered in Community, Enterprise and Lite Editions for on-premises, mobile or cloud deployment.
Strengths
- Rich feature set: Couchbase's March 2014 release, Couchbase Server 2.5.1, offers in-memory object caching, automatic partitioning, limited ACID transaction support, "eventual persistence" (optional nonblocking writes to a caching layer), cross-data-center replication, a synchronized, lightweight, embedded JSON DBMS, and MapReduce support. Couchbase also has a strong technology road map.
- Large customer base and revenue growth: Gartner estimates that Couchbase has over 450 customers in several industries. It also claims to have achieved 400% revenue growth in the past year as its deal sizes have increased.
- Financial and partner strength: Couchbase added a $60 million "E" venture funding round in June 2014, which is helping to fund growth in its international presence. Couchbase has established relationships with several hardware partners and leading system integrators.
Cautions
- Quality: The number of responses from Couchbase's reference customers that reported bugs or unreliable software was significantly above the average for this Magic Quadrant.
- Missing functionality: Of the surveyed Couchbase customers, 48% reported some absent or weak functionality. Some of these functions are on the road map, but not implemented yet.
- Competition: Growing pressure from megavendors and MongoDB, especially in document-oriented use cases, is likely as interest in these use cases grows.
DataStax
Headquartered in Santa Clara, California, U.S., DataStax provides DataStax Enterprise, a commercial version of the open-source Apache Cassandra database. The product is downloadable for on-premises operation, as well as through multiple cloud providers.
Strengths
- Customer satisfaction: Reference customers scored DataStax above average for most metrics, and awarded very high scores for overall DBMS performance and their experience of doing business with this vendor.
- Expanding functionality: DataStax has added in-memory transactions, search capabilities, and support for analytics through Apache Spark and Apache Hadoop. Reference customers identified administration and development tools as positives.
- Vibrant community: DataStax has helped develop a robust open-source community around Apache Cassandra through developer and enterprise conferences.
Cautions
- Weak brand traction: Inquiries from Gartner clients mention DataStax only one-third as often as the open-source project Apache Cassandra. The market has yet to consistently associate DataStax with Apache Cassandra.
- Challenging starts: 53% of the respondents who evaluated DataStax did not select it due to poor performance during POC testing. This may indicate a poor fit between the characteristics of DataStax Enterprise and the piloted use cases.
- Quality between versions: Reference customers identified regression bugs when upgrading to new versions. DataStax must continue to invest in improving its quality.
EnterpriseDB
Headquartered in Boston, Massachusetts, U.S., EnterpriseDB supports and markets the PostgreSQL open-source DBMS, which it packages as an open-source community edition and as Postgres Plus Advanced Server, including the Oracle Compatibility Feature.
Strengths
- Community leadership: EnterpriseDB is the primary contributor to the PostgreSQL community. It is responsible for many of the new features of PostgreSQL by contributing to JSON, materialized views and partitioning.
- Functionality: Gartner clients report that the functionality of EnterpriseDB's Postgres Plus Oracle Compatibility Feature is now more than sufficient to run both mission-critical and non-mission-critical applications. Recently, Infor, a major application platform independent software vendor (ISV), added EnterpriseDB as a DBMS platform choice.
- Stability and compatibility: Reference customers commend the compatibility with Oracle, the stability of the DBMS and the product support.
Cautions
- Open-source dilemma: EnterpriseDB must conform to community-led release cycles for its community editions as they go through the open-source process. This can slow the process of enhancing the base open-source product, but not the enterprise edition.
- Market exposure: EnterpriseDB lacks breadth in its sales and marketing operations, which restricts its ability to communicate its message to potential enterprise customers. According to our survey, those that did not choose EnterpriseDB would have been more likely to choose it if they had been more familiar with it.
- Support and documentation: Reference customers reported a lack of local-language support and weak documentation.
FairCom
FairCom, which was founded in 1979, is headquartered in Columbia, Missouri, U.S. and privately owned. FairCom c-treeACE (Advanced Core Engine), one of the oldest NoSQL DBMSs, is a fully ACID, key-value store with both NoSQL (Indexed Sequential Access Method [ISAM]) interfaces and SQL, and supports transactions with an embedded or stand-alone engine.
Strengths
- Strong technology: c-treeACE is a very strong ACID key-value NoSQL DBMS with SQL capabilities and a long history of stability and innovation. Cross-platform support (Unix/Linux/OS X), scalability and strong HA stand out among its capabilities.
- Customer base: FairCom has a large customer base, encompassing both stand-alone and embedded implementations. The OEM (embedded) base is itself large and produces sustainable revenue that enables investment in research and development (approximately 25% of revenue).
- Satisfied customers: FairCom received some of the highest overall scores in our survey, with high marks for customer support, professional services, performance, ease of doing business, ease of operations and HA. Furthermore, over 75% of its reference customers reported no problems with it.
Cautions
- Marketing presence: FairCom lacks presence in the general DBMS market as it has a relatively small marketing budget. Growth largely comes from within its existing customer base.
- Small, largely unknown vendor: FairCom needs greater brand awareness to compete effectively with other operational DBMS vendors, especially the better known NoSQL vendors.
- Pricing: Reference customers identified FairCom's pricing model as an issue. We believe this is because other NoSQL vendors offer an open-source pricing model.
IBM
Headquartered in Armonk, New York, U.S., IBM offers DB2 for z/OS, Linux, Unix, Microsoft Windows and Informix. Depending on the DBMS, IBM offers multiple deployment models, from hardware bundling and appliances to deployment in IBM's SmartCloud or third-party clouds.
Strengths
- Performance and features: Survey participants rated IBM highly (among the top three vendors) for HA/DR and overall performance. In-memory DB2 with Blu Acceleration reflects IBM's early vision for in-memory DBMSs. NoSQL support includes a MongoDB-compatible JSON API for document-style, cloud delivery via its Cloudant acquisition and via Bluemix, and Resource Description Framework (RDF) for graph models.
- Hardware integration: DB2 for z/OS dynamically routes analytics to the IBM DB2 Analytics Accelerator (IDAA), creating an efficient HTAP architecture in a single environment and reducing mainframe MIPS to cut operating charges. Other IBM products, such as IBM PureData System for Transactions, use integrated hardware and software.
- Global presence: IBM provides support, implementation and services in multiple vertical markets. It has one of the IT industry's largest networks of software, hardware and service partners.
Cautions
- Sales execution: Like other megavendors, IBM is opaque in its reporting of revenue and customer numbers, but Gartner's RDBMS market share figures indicate that IBM declined in 2013, losing second place in the market to Microsoft.
- Complexity and pricing: For the second year in a row, survey participants scored IBM low for pricing model suitability. Value for money was considered average. IBM is, however, expanding its aggressive pricing, bundling and simplification efforts.
- Software quality: Surveyed IBM customers rated it worst of all the vendors in this Magic Quadrant for "software with bugs or unreliable software," and below average for ease of implementation and ease of operation.
InterSystems
Headquartered in Cambridge, Massachusetts, U.S., InterSystems was founded in 1978. It markets Caché, which was originally an object-oriented DBMS but is now a hybrid NoSQL/SQL transaction engine. Caché has a strong position in the healthcare sector.
Strengths
- Strong functionality: Caché supports a wide variety of data types with object, NoSQL and SQL models, and has strong replication capabilities for HA/DR (as evidenced by strong scores from its reference customers). Database management is automated, so it requires fewer staff resources.
- Focused execution: After establishing a solid product and a large ISV ecosystem that is embraced by the healthcare industry, InterSystems is addressing other markets and achieving early success. Strong execution in the healthcare sector is one reason why InterSystems has moved into the Leaders quadrant this year.
- Performance: InterSystems received some of the highest scores from reference customers for the overall performance of Caché and for their experience of doing business with the company. This confirms the impression Gartner gets from other interactions with its customers.
Cautions
- Market perception: Although InterSystems has branched out from the healthcare sector, it is still generally perceived as being a healthcare-only provider. It must pursue a stronger market vision to move into the broader operational DBMS market.
- Marketing: InterSystems is a midsize DBMS vendor with potential for continued growth, especially as 60% of its reference customers plan to purchase more from it. Investment in sales and marketing is necessary if InterSystems is to challenge the broader market leaders.
- Pricing: InterSystems received relatively low scores from reference customers for the suitability of its pricing model.
MapR
Headquartered in San Jose, California, MapR provides the MapR Distribution, including Apache Hadoop. The M7 Enterprise Database Edition includes MapR-DB, an operational DBMS compatible with Apache HBase. It is available on-premises and through various cloud providers.
Strengths
- Reliability and performance: Reference customers gave MapR high scores for its HA/DR capabilities and cluster stability. Several commended the performance of MapR's operational DBMS.
- Support for emerging use cases: 90% of MapR's reference customers use M7 to capture and analyze machine-generated data, such as log files, clickstreams and connected device data.
- Differentiation and focus: MapR differentiates itself from similar vendors through its replacement of the Hadoop Distributed File System (HDFS) with the MapR Data Platform, which exposes Network File System access.
Cautions
- Availability of skills: 60% of the survey respondents did not select MapR owing to concerns about the availability of relevant skills within their organization. Additionally, MapR's scores for ease of programming were well below average.
- After-sale support: Reference customers gave MapR lower scores for its support and documentation and for its professional services.
- Complexity: Several reference customers remarked on the complexity inherent in deploying MapR's product and the overall immaturity of its ecosystem.
MariaDB
MariaDB (formerly SkySQL) is headquartered in Espoo, Finland. It markets two products: MariaDB 10, an open-source, in-memory-capable, multimodel RDBMS based on, and fully compatible with, Oracle MySQL; and MariaDB Enterprise, a commercially supported bundle with enterprise-targeted add-on components. Both products are available for Red Hat Enterprise Linux, CentOS Linux, Ubuntu, Debian (which includes MariaDB in its distributions) and Microsoft Windows. The company is headed by the creators of MySQL.
Strengths
- Rich functionality: MariaDB offers multiple storage engines, tunable persistence, ACID support with the InnoDB/XtraDB engine, graph storage with Open Query Graph (OQGraph), and support for Apache Cassandra and JSON.
- Value: In our survey of reference customers, MariaDB received one of the three highest scores for value for money, as it did for suitability of pricing method. It also received one of the highest scores for "no problems encountered."
- Strong community and partner network: MariaDB is at the heart of a vibrant MySQL user community and ecosystem. It partners with Linux distribution vendors, IBM, Fusion-io, and organizations offering products for special-purpose storage engines, management, backup and HA, as well as service providers.
Cautions
- Increased competition: MariaDB is increasingly visible and will face more competition, especially as Oracle's consent decree with the EU regarding MySQL expires in 20151 and Oracle becomes more aggressive.
- Scale: MariaDB's reference customers mostly quantified the size of their largest databases as being a few hundred gigabytes at most. To compete at the high end against increasing competition, MariaDB will require more terabyte-size reference customers.
- Fragmented offerings: Several customers remarked on the number of separate pieces in MariaDB's software stack; one noted there are "too many independent tools for managing databases."
MarkLogic
Headquartered in San Carlos, California, U.S., MarkLogic offers a document store DBMS in commercial Essential Enterprise, Global Enterprise and Mobile editions, and free, fully-featured developer versions. Its software can be deployed in VMware and Amazon Web Services environments, and, in collaboration with SGI, as the DataRaptor appliance. In 2014, MarkLogic enters the Leaders quadrant for the first time.
Strengths
- Features: MarkLogic's mature enterprise features are extended with tiered storage, HDFS support, backup to Amazon S3, JSON, mobile replication, full text search, geospatial capabilities, Sparql language support, Resource Description Framework (RDF) import support and a converter for MongoDB. Its road map is rich and ambitious.
- Solid customer base and partner network: We estimate that, following recent sizable wins in the financial services sector, MarkLogic now has over 300 commercial customers. It also has a sizable partner ecosystem, which should add momentum to its recent solid growth.
- Customer relationship: Reference customers gave MarkLogic high marks for their experience of doing business with it.
Cautions
- Pricing challenges: Surveyed customers ranked MarkLogic low in terms of value for money and suitability of pricing model. However, MarkLogic altered its pricing structure in 2013 and lowered its prices, and Gartner expects to see this reflected in future surveys.
- Difficult to use: Of the vendors evaluated in this Magic Quadrant, MarkLogic received the lowest overall score from survey respondents for ease of programming. For continued growth, delivery of planned product enhancements and programming language support is essential.
- Geographic concentration on North America: Well over 80% of MarkLogic's customers are in North America. Its overseas expansion efforts must succeed if it is to compete with global providers.
McObject
Headquartered in Issaquah, Washington, U.S., McObject offers eXtremeDB version 5.0, a small-footprint relational in-memory DBMS with extended array and vector support. Since 2001, millions of copies of eXtremeDB have been deployed worldwide in embedded and real-time applications.
Strengths
- Deployment and configuration choices: Typically embedded, eXtremeDB supports Microsoft Windows, Linux, real-time OSs and the Java Native Interface. Clustered configurations are available.
- Functionality: eXtremeDB provides full ACID and tunable persistence, multiversion concurrency control, 64-bit support and hybrid storage for scalability.
- Partnerships: McObject has partnerships with EMC, Fusion-io, HP, IBM and others. Numerous distributors market its product, and it has customers worldwide.
Cautions
- Marketing: eXtremeDB's multiple engines and horizontal and vertical scalability remain little-known in the market.
- Limited targets: eXtremeDB is still seen as being marketed primarily for embedded applications, although this is changing.
- Customer satisfaction: Surveyed customers gave McObject low scores for HA/DR, ease of implementation and ease of operation. These are not all new issues. Given the wide distribution of eXtremeDB version 5.0, they reflect a failure to address continuing challenges.
Microsoft
Headquartered in Redmond, Washington, U.S., Microsoft markets its SQL Server DBMS for the operational DBMS market, as well as Microsoft Azure SQL Database (a database platform as a service) and Microsoft Azure Tables. Microsoft now has in-memory row-store technology for transactions in SQL Server 2014.
Strengths
- Market vision: Microsoft's market-leading vision consists of in-memory computing (SQL Server 2014 now has full transaction in-memory support), NoSQL (with a new document-store DBMS), cloud offerings (both cloud-only and hybrid cloud), use of analytics in transactions (HTAP) and support of mobility. Its vision for in-memory computing and putting the "cloud first" is ahead of its competitors.
- Strong execution: Microsoft SQL Server is an enterprisewide, mission-critical DBMS capable of competing with products from the other large DBMS vendors. Gartner's 2013 market share data shows Microsoft taking second place from IBM in terms of total DBMS revenue.
- Performance and support: Reference customers were very positive, with the performance of SQL Server, documentation, support, ease of installation and operation all rated highly. Only 7% reported problems with the DBMS overall.
Cautions
- Lack of an appliance: Microsoft still lacks an appliance for transactions (one comparable to its SQL Server Parallel Data Warehouse appliance), whereas its major competitors (IBM, Oracle and SAP) all offer one.
- Market image: Although SQL Server is an enterprise-class DBMS, Microsoft continues to struggle to dispel a perception of weakness in this area. Inquiries from Gartner clients demonstrate a continuing perception that SQL Server is not used for mission-critical enterprisewide applications — a view that inhibits wider use of SQL Server as a primary, enterprise-class DBMS.
- HA/DR and pricing issues: Reference customers again found the pricing model for SQL Server unacceptable (they gave it the lowest overall rating of any vendor in this Magic Quadrant) and blamed the price changes that came with SQL Server 2012. Microsoft also received one of the lowest overall scores for ease of implementing HA/DR.
MongoDB
Headquartered in New York City, New York and Palo Alto, California, U.S., MongoDB offers an open-source, document-style DBMS, as well as MongoDB Enterprise, a commercial offering available in various service tiers. MongoDB Enterprise is available through several cloud providers, as well as on-premises.
Strengths
- Customer satisfaction: MongoDB received high scores for every measurement of customer satisfaction in the reference customer survey.
- Improving enterprise capabilities: Recently announced partnerships with analytics and data integration vendors enable MongoDB to tell a well-rounded information management story.
- Operational and management support: Continued investment in the MongoDB Management Service simplifies the running of a large cluster in terms of monitoring, backup and recovery, and provisioning.
Cautions
- Increasingly competitive landscape: Over the past year, several vendors have introduced features that compete with MongoDB's core value proposition. MongoDB will face more pressure to differentiate its offerings against entrenched competitors.
- Growing pains: Although many reference customers reported that MongoDB is easy to get started with, several reported challenges architecting MongoDB for large-scale deployments.
- Trendiness with developers: MongoDB's popularity among developers means it is often selected before application requirements are understood. This can result in a poor fit of DBMS capabilities to the application.
Neo Technology
Neo Technology is headquartered in San Mateo, California, U.S. Neo4j is a native graph-style NoSQL DBMS capable of handling transactions with ACID support and clustering for scalability and HA. Neo became an incorporated company in 2011, but began developing its product much earlier, in 2000. Neo4j, which was first used in a production environment in 2003, is offered as both an open-source version and an Enterprise Edition.
Strengths
- Native graph DBMS: Neo4j is a native graph-style DBMS (as opposed to an existing DBMS to which graph capabilities have been added). It is engineered for performance with transactional ACID capabilities in a single instance and offers tunable consistency across clusters for scalability.
- Growth: Since its founding, Neo has seen strong growth from both its open-source version and Enterprise Edition.
- Performance and ease of use: Reference customers identified performance, ease of operation and implementation, and ease of doing business as strengths of Neo.
Cautions
- Graph model: The graph DBMS model is difficult to understand, which lengthens the learning curve. This problem is exacerbated by growing hype from other vendors about the introduction of a graph model on a nongraph DBMS model.
- Small vendor: Although the Neo4j product is over 10 years old and has grown consistently, Neo remains a small vendor that faces all the issues of risk and stability associated with small vendors.
- Pricing model and HA/DR: Reference customers identified Neo's pricing model as an issue. They also expressed dissatisfaction with the product's HA/DR capabilities.
NuoDB
Headquartered in Cambridge, Massachusetts, U.S., NuoDB provides an operational DBMS designed to scale horizontally and elastically. In addition to being available in on-premises and developer editions, NuoDB's product is available on Amazon Web Services.
Strengths
- Delivery and support: NuoDB received top scores for its support and documentation, professional services and ease of programming. We believe this is enabling it to win contracts to replace other vendors in several locations.
- Rapid deployment: On average, reference customers estimated it took less than five months to deploy NuoDB's product in production environments.
- Support for emerging use cases: 80% of NuoDB's reference customers use it for capturing and analyzing machine-generated data, such as clickstreams, log files and connected device data.
Cautions
- Inconsistent experience: Although NuoDB received several top scores for service delivery, documentation and support, reference customers that weren't full of praise were highly critical. As a new vendor, NuoDB is still perfecting its service and support.
- Slow momentum: NuoDB has not established a footprint in the developer community, which commonly provides informal support and initiates tool development. Gartner clients have yet to show interest in NuoDB during calls to our inquiry service.
- Nascent partner ecosystem: NuoDB's partner program is still developing and has yet to attract the necessary numbers to supplement the company's sales and implementation efforts.
Oracle
Headquartered in Redwood Shores, California, U.S., Oracle markets a complete set of DBMS products for operational systems. These include Oracle Database, Oracle TimesTen, Oracle Berkeley DB, Oracle NoSQL Database and MySQL. In addition to software, several of Oracle's DBMSs are available in engineered systems (appliances).
Strengths
- Broad range of offerings: Oracle has the broadest product portfolio in the market, covering different DBMSs for multiple purposes (RDBMS, NoSQL, streaming data and mobile). Also, it offers delivery in the cloud, on appliances and as stand-alone software. According to Gartner's 2013 market share numbers, Oracle remains in first place for total DBMS revenue market share.
- Functionality: Oracle offers extensive functionality, with many new features (such as the JSON data type and Temporal, which replaces Total Recall) and options such as the Oracle Database In-Memory and Oracle Multitenant options, the latter moving multitenancy to the DBMS layer and reducing support and maintenance. Oracle is also pioneering DBMS functionality on silicon, with new SPARC M7 and T7 chips scheduled for delivery in 2015.
- Solid performance and availability: Reference customers again identified Oracle's DBMS performance and availability as primary reasons for implementation.
Cautions
- Public perception of vision: Oracle's marketing continues to downplay its responses to market trends (such as in-memory functionality) until it announces products. Oracle customers who use Gartner's client inquiry service continue to show confusion and disillusionment in this regard, as they have to make assumptions about Oracle's road maps and vision.
- "Push back" on appliances: Users of Gartner's client inquiry service show a reluctance to purchase products (such as engineered systems) due to perceived "lock in" to Oracle's proprietary systems — some functions, for example, are available only on Oracle hardware and appliances, such as those in Exadata Storage Server software.
- Low cost/value and bugs: Reference customers consider Oracle's products to be expensive and therefore that they have the lowest value proposition. Oracle also received one of the highest scores for bugs reported. Finally, in recent Gartner surveys,2 Oracle received the lowest score for ease of doing business.
Pivotal
Pivotal, a spinoff from EMC, is headquartered in San Francisco, California, U.S. It released Pivotal GemFire XD in April 2014, combining GemFire, its distributed in-memory data grid, and SQLFire, its distributed, memory-optimized SQL database, with Pivotal HD, its Hadoop distribution incorporating Hawq (based on the Greenplum massively parallel processing [MPP] column-store DBMS). It is available with Pivotal CF for cloud-based deployment.
Strengths
- Rich functionality: By combining an in-memory transactional engine with Pivotal HD's Hawq analytic SQL engine, Pivotal enables large HTAP-style combinations of real-time transaction and event processing for closed-loop systems. It offers HA, active-active deployment and rolling upgrade support.
- Flexible usages: GemFire provides native object-oriented and REST interfaces; Hawq provides SQL analytics. GemFire XD supports structured data, geospatial data, objects, JSON and key-value pairs.
- Strong global organization: Pivotal has the resources and installed base of EMC as key assets. They include manufacturing, research, and presale and postsale support worldwide.
Cautions
- Maturity: Integrating multiple products is a complex process, and GemFireXD has been on the market for only a few months. Pivotal received the lowest survey scores for support and documentation, and below-average scores for ease of implementation and ease of operation — though this is not unusual for an early-stage product. Its analytic appliance does not yet have an operational counterpart.
- Pricing and pricing model: Pivotal received very low survey scores for value for money and suitability of pricing method, though its new simplified and flexible pricing model should help to improve matters. Pivotal is one of the world's biggest startups, and its revenue will need to grow rapidly to justify EMC's investment.
- Market awareness: The number of inquiries received by Gartner regarding Pivotal products fell to nearly zero after Pivotal was spun off, and has only recently begun to recover. Pivotal's use of the EMC and VMware sales organization is starting to be felt, which is a positive sign.
SAP
Headquartered in Walldorf, Germany, SAP has several DBMS products that are used for transaction systems: SAP Adaptive Server Enterprise (ASE), SAP SQL Anywhere and SAP Hana. Both SAP ASE and SAP SQL Anywhere are available as software only, while SAP Hana is marketed as an appliance.
Strengths
- Leading vision: SAP remains a leader with its vision for HTAP: It now supports most of the SAP applications that run on Hana on a single in-memory database used for transactions and analytics. SAP reports that over 1,000 customers have purchased part or all of SAP Suite on Hana in just over one year of general availability, which underlines the market's interest in HTAP.
- Strong DBMS offerings: SAP has seen strong growth in SAP Hana, SAP ASE (now certified for SAP applications) is growing strongly, and SAP SQL Anywhere continues to lead the mobility market in terms of functionality.
- Performance: Reference customers again identified performance (scalability and reliability) as a major strength for SAP, awarding it one of the highest scores. Additionally, SAP received the highest score for ease of operation across its DBMS products.
Cautions
- Marketing communications: Interactions with users of Gartner's client inquiry service confirm confusion over SAP's messages about how its DBMS products integrate, where each product can be used, what SAP Hana can and cannot do, and most importantly, whether SAP Hana will be required in the future.
- Lack of skills: As inquiries from Gartner clients make clear, skills to support SAP Hana remain scarce in the market.
- HA/DR problems: SAP's reference customers (and especially users of SAP Hana) reported the lowest level of satisfaction with their vendor's HA/DR capabilities. For the second year, SAP also received the lowest score for clients' experience of doing business with it; similarly, in recent Gartner conference surveys,2 SAP received the second-lowest score for ease of doing business.
TmaxData
Headquartered in Bundang-gu, South Korea, TmaxData provides Tibero, an SQL RDBMS featuring various clustering options, integrated encryption and compatibility with other vendors' DBMS products.
Strengths
- Satisfied customers: Reference customers scored TmaxData above average on most satisfaction measures, and substantially above average for ease of implementation.
- Support for mixed workloads: Tibero Active Cluster (TAC) is aimed at transactional workloads using shared disk clustering, while Tibero InfiniData operates in a shared-nothing environment and integrates with Hadoop for analytics workloads.
- Several pricing options: Tibero is available in three editions. Each offers different core/processor pricing options and features.
Cautions
- Limited geographic traction: Despite opening offices in several countries in 2013 and 2014, TmaxData has yet to gain significant traction outside South Korea.
- Uneven postsale support: Reference customers gave TmaxData low scores for support and documentation, and for professional services.
- Software quality: Although no problems were reported consistently, only half of TmaxData's reference customers reported encountering no problems with its products.
VoltDB
Headquartered in Boston, Massachusetts, U.S., VoltDB markets an in-memory row-store operational RDBMS that is increasingly available via vertical-market partners. VoltDB version 4.3, released in May 2014, is an open-source DBMS available as software only.
Strengths
- Technology and vision: Substantial SQL-92 functionality, in-memory DBMS architecture and precompiled Java stored procedures drive VoltDB's high performance in support of HTAP use cases. Tunable consistency and JSON support have been added to give developers more flexibility.
- Customer satisfaction: VoltDB, the only open-source in-memory DBMS vendor, received above-average scores from reference customers for suitability of pricing and professional services.
- Performance and value: As expected for an in-memory DBMS vendor, VoltDB received high scores from reference customers for overall performance of the product and value for the price paid.
Cautions
- Competitive challenges: VoltDB's small, U.S.-centric sales organization and modest ecosystem are growing, but their small size still limits its ability to reach new customers.
- Feature gaps: Although VoltDB customers are overwhelmingly on the newest release, its reference customers identified "some absent or weak functionality."
- Revenue model: VoltDB's revenue remains relatively modest according to Gartner's estimates. Extra funding will be needed to achieve the needed growth. A recent $8 million of Series "B" round venture capital funding should help.
Vendors Added and Dropped
We review and adjust our inclusion criteria for Magic Quadrants and MarketScopes as markets change. As a result of these adjustments, the mix of vendors in any Magic Quadrant or MarketScope may change over time. A vendor's appearance in a Magic Quadrant or MarketScope one year and not the next does not necessarily indicate that we have changed our opinion of that vendor. It may be a reflection of a change in the market and, therefore, changed evaluation criteria, or of a change of focus by that vendor.
Added
- Cloudera: This Hadoop distribution vendor now supports operational transactions through the use of Apache HBase.
- FairCom: This vendor sells a NoSQL DBMS.
- MapR: This Hadoop distribution vendor now supports operational transactions through the use of Apache HBase.
- MariaDB: This vendor offers a MySQL-compatible, open-source RDBMS.
- Neo Technology: This vendor's graph DBMS engine supports transactions.
- Pivotal: This vendor's platform has an in-memory DBMS to support transactions across multiple sources of data.
- TmaxData: This South Korean vendor, which also competes in North America, offers an RDBMS.
Dropped
- Orient Technologies: This vendor failed to meet the inclusion criteria.
Inclusion and Exclusion Criteria
To be included in this Magic Quadrant, vendors and products had to meet the following criteria.
Software availability: Vendors had to have DBMS software generally available for licensing or supported download for at least a year, as of 1 July 2014. Products that have been commercially available for over 10 years but have not grown in terms of revenue at or near the market rate for several years are excluded.
Software releases: We use the most recent generally available release of the software to evaluate current technical capabilities. We do not consider beta, "early access," "technology preview," "ramp up" or other releases that are not generally available. As regards reference customers and their survey responses, all versions currently used in production are considered. When older versions are in use, we consider whether later releases may have addressed any reported issues, but also the rate at which customers move to newer versions.
Feature availability: Product evaluations include technical capabilities, features and functions present in the product or supported for download through midnight, U.S. Eastern Daylight Time on 1 July 2014. Capabilities, product features and functions released after this date could be included at Gartner's discretion and in a manner Gartner that deemed appropriate to ensure the quality of this publication. We also considered how such later releases could affect the end-user experience.
Customers and revenue: Vendors had to generate a minimum of $20 million U.S. dollars in verifiable annual software revenue or have at least 100 verifiable and distinct customer organizations with operational DBMSs in production. In addition, each vendor had to identify a minimum of 10 reference customers who would respond to Gartner's approved reference customer survey (for this year's Magic Quadrant, the survey questionnaire was in English only). Revenue can be from licenses, support and/or maintenance. Gartner may include additional vendors based on undisclosed references in cases of known use for classified but unspecified use cases.
Support: The vendor had to provide support for its operational DBMS product(s). We also considered products that control or participate in the engineering of open-source DBMSs and their support. We required that a DBMS meet Gartner's definition of a DBMS (see Note 1).
Services: Vendors participating in the operational DBMS market had to demonstrate their ability to deliver the necessary services to support transaction systems via the establishment and delivery of support processes, professional services and/or committed resources and budget.
Geographical availability: Vendors had to demonstrate support for operational DBMS customers in at least two of the following major regions: North America, Latin America, Europe, the Middle East and Africa, and Asia/Pacific.
Excluded products: Product categories excluded from this Magic Quadrant (see also Market Definition/Description) are:
- Embedded-only DBMS products
- Data warehouse-only DBMS products
- DBMS products available only as a cloud service
- Prerelational DBMS products
- Graph-only DBMS products
- Data grid products
- Complex-event processing or streaming-data engines
Evaluation Criteria
Ability to Execute
The Ability to Execute criteria are primarily concerned with vendors' capabilities and maturity. Criteria under this heading also consider products' portability, ability to scale, and ability to run in different operating environments (giving the customer a range of options). Ability to Execute criteria are crucial to customers' satisfaction and success with a product, so reference customer interviews and survey responses are weighted heavily throughout.
Product or service includes the technical attributes of the DBMS(s), as well as features and functions built specifically to manage the DBMS when used as a platform for transactions, interactions and observations. We include HA/DR, performance and scalability, support for multiple deployment options (such as virtualization, cloud and hybrid cloud/on-premises), and support for multiple programming languages and new hardware and memory models. These attributes are evaluated across a variety of database sizes and application workloads. We also consider the automated management, tools and resources necessary to manage a database environment, especially as it scales to more complex application workloads. Finally, we consider the flexibility of the DBMS to incorporate new data types, application types and new requirements for distributing data across multiple servers and geographies.
Overall viability includes corporate aspects such as the skills of personnel, financial stability, investment in research and development, and mergers and acquisitions. It also covers the management's ability to respond to market changes and the company's ability to weather market difficulties (crucial for long-term survival). Vendors are further evaluated on their ability to establish dominance in meeting specific market demands.
Sales execution/pricing covers the price/performance and pricing models of the DBMS products, and the ability of the sales force to manage accounts (based on feedback from interviews, surveys and interactions with users of Gartner's client inquiry service). We also consider the market share of the DBMS products. Also considered are the diversity and innovativeness of packaging and pricing models, including the ability to promote, sell and support the products globally.
Market responsiveness/track record includes the diversity of the vendor's offerings in response to changing market demands — for example, its ability and flexibility to offer appliances, cloud deployment, new data types and new programming requirements. We consider general market perceptions of the vendor and its products. We assess both the vendor's ability to adapt to market changes during the previous 18 months and its flexibility in response to market dynamics over a longer period.
Marketing execution evaluates such activities as lead generation, including traditional methods and Internet-enabled trial software delivery, and the execution of channel development through partnering agreements (including coseller, comarketing and colead management arrangements). Also considered are the vendor's coordination and delivery of education and marketing events throughout the world and across vertical markets, and the creation and support of "community" activities that help to raise awareness and develop skills among buyers and prospective customers.
Customer experience evaluations are based primarily on reference customer surveys and interviews conducted for this report, as well as discussions with users of Gartner's inquiry service during the previous six quarters. We consider the vendor's track record of POCs, customers' perceptions of its product(s), and customers' loyalty to the vendor (this reflects their tolerance of its practices and can indicate their level of satisfaction). Additionally, customer input regarding the applicability of products to limited use cases can be significant, depending on the success or failure of the vendor's approach in the market.
Operations covers the alignment of the vendor's organization, as well as whether and how this enhances its ability to deliver. Aspects considered include field delivery of appliances, manufacturing (including the identification of diverse geographic cost advantages), internationalization of the product (in light of both technical and legal requirements) and adequate staffing.
Table 1. Ability to Execute Evaluation Criteria
Evaluation Criteria |
Weighting |
Product or Service |
High |
Overall Viability |
Low |
Sales Execution/Pricing |
Medium |
Market Responsiveness/Track Record |
High |
Marketing Execution |
Medium |
Customer Experience |
High |
Operations |
Low |
Source: Gartner (October 2014)
Completeness of Vision
Completeness of Vision encompasses a vendor's abilities to understand the functional capabilities needed to support operational environments, to develop a product strategy that meets the market's requirements, to comprehend overall market trends, and to influence or lead the market when necessary. A visionary leadership role is necessary for the long-term viability of both product and company. A vendor's vision may be demonstrated — and improved — by its willingness to extend its influence throughout the market by working with independent third-party application software vendors that deliver both additional functionality for the operational environment and commercial off-the-shelf software. A successful vendor will be able not only to understand the competitive landscape of operational transactions but also to shape the future of this field.
Market understanding assesses a vendor's ability to understand the market and shape its growth and vision. In addition to examining a vendor's core competencies in this market, we considered its awareness of new trends, such as the increasing sophistication of end users, growing scalability needs (especially across server clusters), the cloud as a platform for DBMSs, the demand for in-memory computing and HTAP, use of new consistency models, and the growing desire to use data structures other than relational ones.
Marketing strategy refers to a vendor's marketing themes, research-and-development focus, and ability to choose appropriate target markets and third-party software vendor partnerships to enhance the marketability of its products. For example, we considered whether the vendor encourages and supports ISVs in its efforts to support the DBMS in native mode (via, for instance, comarketing or coadvertising with "value added" partners). This criterion includes the vendor's responses to the market trends identified above and any offers of alternative solutions in its marketing materials and plans.
Sales strategy assesses how a vendor designs and targets its channels and partnerships developed to assist with selling. This is especially important for younger organizations, as a good sales strategy can enable them to greatly increase their market presence, while maintaining lower sales costs (for example, through free downloadable community editions, coselling and joint advertising). This criterion also covers a vendor's strategy for communicating its vision to its field organization and, therefore, to clients and prospective customers. Also included are pricing innovations and strategies, such as new licensing arrangements and cloud-based models for elastic provisioning to support peak demand.
Offering (product) strategy covers the design of product packaging and deployment options, including the availability of cloud versions, developer editions and appliances based on the vendor's DBMS. Vendors should demonstrate a diverse strategy that enables customers to choose what they need to build a complete solution for an operational environment. Also covered are partners' offerings that include technical, marketing, sales and support integration.
Business model covers how a vendor's model of a target market combines with its products and pricing, and whether the vendor can generate profits with this model, judging from its packaging and offerings. Additionally, we consider reviews of publicly announced earnings and forward-looking statements relating to an intended market focus. For private companies and to supplement publicly available information, we use proxies for earnings and new customer growth, such as the number of Gartner clients indicating interest in, or awareness of, a vendor's products during calls to our inquiry service.
Vertical/industry strategy affects a vendor's ability to understand its clients. Aspects such as vertical-market sales teams and partnerships with vertical-market service providers are considered.
Innovation assesses vendors' approach to developing new functionality that aligns with its market and offering strategies by allocating and managing research-and-development spending and leading the market in new directions. Uses of new storage and hardware models are key examples of this.
Geographic strategy, including a vendor's worldwide reach, is assessed by considering its plan to use its own resources in different regions, as well as those of subsidiaries and partners. This criterion considers a vendor's plan for supporting clients throughout the world, around the clock, and in many languages. Anticipation of regional and global economic conditions is also considered.
Table 2. Completeness of Vision Evaluation Criteria
Evaluation Criteria |
Weighting |
Market Understanding |
High |
Marketing Strategy |
Medium |
Sales Strategy |
Medium |
Offering (Product) Strategy |
High |
Business Model |
Low |
Vertical/Industry Strategy |
Medium |
Innovation |
High |
Geographic Strategy |
Low |
Source: Gartner (October 2014)
Quadrant Descriptions
Leaders
Leaders generally demonstrate the most support for a broad range of operational applications, based on support for a wide range of data types and large numbers of concurrent users. These vendors demonstrate consistent customer satisfaction and strong customer support. Many have competed in this market for many years, and have built a wide partner ecosystem for their products. Hence, Leaders generally represent the lowest risk for customers in the areas of performance, scalability, reliability and support. As the market's demands change, so Leaders demonstrate strong vision in support not only of the market's current needs but also of emerging trends. Finally, the messaging, product research and development, and delivery of leaders are in line with today's market and with new trends in both DBMS software and hardware technology.
Challengers
Challengers are stable vendors with strong, established offerings but a relative lack of vision. It is normal for some vendors to have high scores for execution but to lag in terms of the adoption levels and vision needed for leadership. Challengers normally show strong corporate viability and financial stability, and demonstrate strong customer support. However, they lack the vision to support some of the new trends in the operational DBMS market, such as support for interaction and observation data in transactions, or a road map for moving toward in-memory DBMS capabilities. Although they may be lacking in relation to some of the market's innovative concepts, Challengers offer stability, simplicity of installation and support, and strong performance. As with the Niche Players, Gartner considers support of a limited number of data types and hardware models as evidence of limited vision.
Visionaries
Visionaries take a forward-thinking approach to managing the hardware, software and end-user aspects of an operational DBMS environment. Visionaries typically have innovative ideas for new functionality and advanced use of new hardware. They have the requisite number of production customers, but lack the market momentum of Leaders. In this market, Niche Players are often young, small and innovative vendors with great new ideas that are spurring on the more mature vendors and the market in general.
Niche Players
Niche Players generally deliver a highly specialized product with limited market appeal. Frequently, a Niche Player provides an exceptional operational DBMS product, but is isolated or limited to a specific end-user community, region or industry. Although the solution itself may not have limitations, adoption is limited. Niche Players contains vendors from several categories:
- Those with an operational DBMS product that lacks a strong or a large customer base
- Those with an operational DBMS that lacks the breadth of functionality of those of Leaders
- Those with new operational DBMS products that lack general customer acceptance or the proven functionality to move beyond niche status
Niche Players typically offer smaller, specialized solutions that are used for specific operational and transactional applications, depending on the client's needs.
Context
At one time, Gartner viewed the online transaction processing (OLTP) DBMS market as very mature, with few new entrants to challenge the status quo. However, in recent years, the market has changed rapidly, which prompted our redefinition of it in 2013 as the operational DBMS market (see "The OLTP DBMS Market Becomes the Operational DBMS Market"). With the introduction of NoSQL and Hadoop in support of unstructured data in transactions and the viable use of in-memory computing, many organizations are beginning to use these new DBMS engines for specific use cases, such as global scalability of Web applications.
As there are now many new entrants, including small and less mature vendors, the market appears to be split in two, although compared with the situation in 2013, the space between its two groups has reduced. In one group are the innovative new vendors. In the other are the traditional, strong, mature leaders. The reason for most of the vendors being below the midline in the Magic Quadrant is that they support only one or two of the DBMS models (which include key value, graph, relational, table-style and document-style), and only one or two of the multiple data types (such as structured [relational], unstructured, XML, interaction and observation). The Leaders support a wide range of models and data types in a scalable, highly available environment, which is one reason for the considerable space between several of them and the newer vendors.
Another major focus for vendors in this market is support for in-memory computing. Most vendors are beginning to add this functionality to their DBMSs, some with an in-memory-only model. Due to its inherent speed, in-memory computing is becoming necessary for the processing of interaction and observation data integrated into transactions. Most of the traditional vendors have introduced an in-memory DBMS, generally in support of analytics. This will eventually become the preferred model for all DBMSs. The one form of memory not well adapted to data is flash when used as addressable memory and not as a form of fast disk replacement. NAND flash is slower than DRAM, but it is more efficient when used as addressable memory than as a disk block cache.
As the new vendors mature and offer a wider range of functions, the operational DBMS market will become more homogeneous and commoditized (see "IT Market Clock for Database Management Systems, 2014").
This Magic Quadrant deals with the key information management capabilities for transaction processing. It should therefore interest anyone involved in defining, purchasing, building or managing a transaction-processing environment — notably, CIOs, CTOs, infrastructure managers, database and application architects, database administrators and IT purchasing managers.
For this Magic Quadrant, we based our analysis on information gathered from interactions with Gartner clients over the past 12 months and our survey of the vendors' reference customers, performed during July and August 2014.3 We also considered earlier information and any news about vendors' products, customers and finances that arose during the analysis time frame.
Market Overview
The OLTP DBMS market, from which the operational DBMS market evolved, was very mature in the early years of the 21st century. However, as Internet usage and availability grew, so did the applications necessary to support the associated, growing infrastructure. Consequently, over the past five years, many new vendors have entered this market with products to support the specialized applications required by a new and global business arena.
Many drivers of innovation are widely accepted. New forms of data — that were previously difficult or impossible to capture — have become available from connected devices (the Internet of Things), such as smart meter data and machine or device data; we call this "observation data." The pervasive use of personal devices and social media has also become a source of social- and business-related data; we call this "interaction data." These new forms of data must now be used not only for analytics, but also within transactions. Data from vendors' reference customers confirms this change, as 75% of the respondents to our survey use interaction data in transactions, and over 50% use observation data in transaction processing.3
In terms of hardware, new devices, servers, and networking, memory and storage options (to name but a few) have proliferated. These both enable and require new ways to process the data they create or support. For in-memory DBMSs, the amount of memory available on individual servers is already reaching 32TB to 64TB. Organizations need to capture both structured and unstructured data for use in transactions. Furthermore, they must use the data from transactions, observations and interactions in real time for decision processing as part of, not separately from, the transactions. This process is the definition of HTAP (for further details, see "Hype Cycle for In-Memory Computing, 2014").
To support the new operational DBMS market, many new vendors have emerged with innovative DBMS engines that support transactions on a global scale, real-time transactions integrated with analytics, streaming data transactions, and more. These new vendors are single-minded in terms of direction. Once their ideas are proven, more mature vendors will feel obliged to adopt some of this technology.
The new vendors' activities include the use of JSON for data structures in applications; new and less restrictive forms of consistency (allowing for eventual consistency); larger amounts of DRAM for in-memory DBMSs; and new file structures that differ from relational ones, such as those for key-value and document-store file systems. Many of these vendors support these NoSQL systems for simplicity, agile development, support of unstructured data types, scalability and performance. Already, an increasing number of mature DBMS vendors are adopting these technologies in their systems.
Although we exclude cloud-only delivery from this Magic Quadrant, the cloud is being widely adopted as a delivery platform in the operational DBMS market. Over the next few years, we expect most vendors to offer cloud versions of their DBMS products. These will range from simple offerings of support for infrastructure as a service and cloud hosting, to full cloud DBMS platforms with elasticity and multitenant capabilities. As the operational DBMS market matures, cloud deployment — and especially hybrid deployment — will become a criterion of importance as it offers users an additional platform choice.
출처 : http://www.gartner.com/technology/reprints.do?id=1-23A415Q&ct=141020&st=sb
IT News 2014. 6. 13. 13:52
AWS를 이용하여 RDB 서비스를 지원하기 위한 User Guide.
이외에도 Bigdata를 위한 NoSQL 서비스를 지원하기 위한 User Guide도 있으나, 일단 Pass~~
AWS에서 White Paper를 찾으면 아래와 같이 다양한 Guide 문서를 찾을 수 있네요.
Amazon Relational Database Service User Guide .pdf
IT News 2014. 6. 13. 13:44
Remote Collaboration Survey Report ASIA
Remote Collaboration을 진행하는데 있어서 제약과 한계를 극복하는 방법에 대한 내용.
Remote_Collaboration_Survey_Report_ASIA.PDF
출처는 첨부파일 내부에... ^^
Oracle 2014. 2. 12. 20:34
이 문서에서
적용 대상:Oracle Net Services - 버전 11.1.0.6 to 11.2.0.3 [릴리즈 11.1 to 11.2] 이 문서의 내용은 모든 플랫폼에 적용됩니다.
목적
ORA-609 에러는 alert.log 에 보고된다. 이 에러는 간헐적으로 발생하고 몇일 동안 발생하지 않을 수도 있다.
Mon Oct 12 10:03:39 2009 Errors in file e:\app\oracle\diag\rdbms\center\center\trace\center_ora_7464.trc: ORA-00609: could not attach to incoming connection ORA-12537: TNS:connection closed ORA-609 : opiodr aborting process unknown ospid (2436_7464)
데이타베이스 서버에 지역적으로 발생하는 sqlnet.log 화일에 이 에러가 발생할 수 있다.:
Fatal NI connect error 12537, connecting to: (LOCAL=NO)
VERSION INFORMATION: TNS for 64-bit Windows: Version 11.1.0.7.0 - Production Oracle Bequeath NT Protocol Adapter for 64-bit Windows: Version 11.1.0.7.0 - Production Windows NT TCP/IP NT Protocol Adapter for 64-bit Windows: Version 11.1.0.7.0 - Production Time: 12-OCT-2009 10:03:39 Tracing to file: E:\app\oracle\product\11.1.0\db_1\NETWORK\trace\svr1_7464.trc Tns error struct: ns main err code: 12537 TNS-12537: TNS:connection closed ns secondary err code: 12560 nt main err code: 0 nt secondary err code: 0 nt OS err code: 0
listener.log에는 명확한 에러 없이 접속이 맺어졌다는 것을 보여준다. 이것은 리스너가 접속을 서버 프로세스에게 전달해 준 이후에 접속이 실패했기 때문이다.
12-OCT-2009 10:03:39 * (CONNECT_DATA=(SID=ORCL)) * (ADDRESS=(PROTOCOL=tcp)(HOST=123.456.1.123)(PORT=3158)) * establish * ORCL * 0 12-OCT-2009 10:03:39 * (CONNECT_DATA=(SID=ORCL)) * (ADDRESS=(PROTOCOL=tcp)(HOST=123.456.1.123)(PORT=3159)) * establish * ORCL * 0
아래의 Oracle Net 서버 트레이스화일에서 주목할 부분은 화일이름 "svr_7464.trc"이다.
여기서 문제는 접속 패킷을 클라이언트로부터 받을 때 나타난다. ORA-609 에러는 Oracle Net 트레이스에는 나타나지 않는다. ORA-609 에러는 트레이스 snippet에서 ns=12537 을 동반하면서 발생한다.
[000001 12-OCT-2009 10:03:39:116] nscon: doing connect handshake... [000001 12-OCT-2009 10:03:39:116] nscon: recving a packet [000001 12-OCT-2009 10:03:39:116] nsprecv: entry [000001 12-OCT-2009 10:03:39:116] nsprecv: reading from transport... [000001 12-OCT-2009 10:03:39:116] nttrd: entry [000001 12-OCT-2009 10:03:39:163] nttrd: exit [000001 12-OCT-2009 10:03:39:163] ntt2err: entry [000001 12-OCT-2009 10:03:39:163] ntt2err: Read unexpected EOF ERROR on 7104 [000001 12-OCT-2009 10:03:39:163] ntt2err: exit [000001 12-OCT-2009 10:03:39:163] nsprecv: error exit [000001 12-OCT-2009 10:03:39:163] nserror: entry [000001 12-OCT-2009 10:03:39:163] nserror: nsres: id=0, op=68, ns=12537, ns2=12560; nt[0]=507, nt[1]=0, nt[2]=0; ora[0]=0, ora[1]=0, ora[2]=0 [000001 12-OCT-2009 10:03:39:163] nscon: error exit [000001 12-OCT-2009 10:03:39:163] nsdo: nsctxrnk=0 [000001 12-OCT-2009 10:03:39:163] nsdo: error exit [000001 12-OCT-2009 10:03:39:163] nsinh_hoff: error recving request
Alert log와 Oracle Net 트레이스를 통해서 ORA-609 에러를 트레이스하는 다른 방법을 보여주는데, 핸드쉐이크하는 동안에 이슈가 나타나는 것을 보여준다.
아래는 Alert log를 보여준다.:
Mon Dec 21 15:52:15 2009 ORA-609 : opiodr aborting process unknown ospid (21631120_1)
[21-DEC-2009 15:52:15:025] nscon: sending NSPTAC packet [21-DEC-2009 15:52:15:025] nspsend: entry
[21-DEC-2009 15:52:15:031] ntt2err: Read unexpected EOF ERROR on 14 [21-DEC-2009 15:52:15:031] ntt2err: exit [21-DEC-2009 15:52:15:031] nsprecv: error exit [21-DEC-2009 15:52:15:031] nserror: entry [21-DEC-2009 15:52:15:031] nserror: nsres: id=0, op=68, ns=12537, ns2=12560; nt[0]=507, nt[1]=0, nt[2]=0; ora[0]=0, ora[1]=0, ora[2]=0 [21-DEC-2009 15:52:15:031] nsrdr: error exit [21-DEC-2009 15:52:15:031] nsdo: nsctxrnk=0 [21-DEC-2009 15:52:15:031] nsdo: error exit [21-DEC-2009 15:52:15:031] nsnareceive: error exit [21-DEC-2009 15:52:15:031] nserror: entry [21-DEC-2009 15:52:15:031] nserror: nsres: id=0, op=68, ns=12537, ns2=12532; nt[0]=0, nt[1]=0, nt[2]=0; ora[0]=0, ora[1]=0, ora[2]=0 [21-DEC-2009 15:52:15:031] nacomrc: received 12637 bytes [21-DEC-2009 15:52:15:031] nacomrc: failed with error 12637 [21-DEC-2009 15:52:15:031] nacomrc: exit [21-DEC-2009 15:52:15:031] na_receive_packet: failed with error 12637 [21-DEC-2009 15:52:15:031] na_receive_packet: exit [21-DEC-2009 15:52:15:031] na_server: failed with error 12637
인스턴스에 지역적인 sqlnet.log를 통해서 일치하는 에러 메시지를 발견할 수 있다.
다음은 그 예를 보여준다.
Fatal NI connect error 12537, connecting to: (LOCAL=NO)
VERSION INFORMATION: TNS for Solaris: Version 11.2.0.2.0 - Production Oracle Bequeath NT Protocol Adapter for Solaris: Version 11.2.0.2.0 - Production TCP/IP NT Protocol Adapter for Solaris: Version 11.2.0.2.0 - Production Time: 21-DEC-2009 15:52:15 Tracing not turned on. Tns error struct: ns main err code: 12537 TNS-12537: TNS:connection closed ns secondary err code: 12560 nt main err code: 0 nt secondary err code: 0 nt OS err code: 0
Oracle Net 서버 트레이스 안에 event에 매치되는 것을 보여준다.
진단 절차
1. listener.log로부터 접속을 맺는 클라이언트를 찾아낸다. Alert log는 다음과 유사한 ORA-609 에러를 나타낸다. :
Mon Oct 05 12:41:49 2009 ORA-609 : opiodr aborting process unknown ospid (21131406_1)
Listener.log로 가서 이 접속에 일치하는 엔트리를 찾아본다. Listener.log 내 엔트리는 아래의 예처럼 보일 것이다.:
05-OCT-2009 12:41:49 * (CONNECT_DATA=(SID=orcl)) * (ADDRESS=(PROTOCOL=tcp)(HOST=sample.com)(PORT=1234)) * establish * orcl * 0
위 예제에서 클라이언트 주소값이 "sample.com"임을 주목하여야 한다. 한가지 옵션은 그 사이트에서 몇 개의 클라이언트를 위치시키고, 클라이언트 트레이싱을 활성화하는 것이다. 클라이언트 사이드에서 $ORACLE_HOME/network/log 화일을 확인해야 하고 동일한 timestamp 시점에 발생한 timeout 에러에 대해 명시적으로 확인해야 한다.
2. Oracle Net 트레이싱을 클라이언트 레벨 16으로 걸어서 확인한다. 클라이언트 SQLNET.ORA 화일 안에 아래와 같이 추가한다.
DIAG_ADR_ENABLED=off # Diable ADR if version 11g
TRACE_LEVEL_CLIENT = 16 # Enable level 16 trace TRACE_TIMESTAMP_CLIENT = ON # Set timestamp in the trace files TRACE_DIRECTORY_CLIENT = <DIRECTORY> # Control trace file location
TRACE_FILELEN_CLIENT =<n> #Control size of trace set in kilobytes eg 20480 TRACE_FILENO_CLIENT =<n> #Control number of trace files per process
만일 접속 모델이 JDBC Thin이라면 클라이언트 사이드의 Java 트레이싱이 필요하므로, Document 793415.1 How to Perform the Equivalent of SQL*Net Client Tracing with Oracle JDBC Thin Driver 를 참고하도록 한다. 만일 11.2 JDBC Thin 클라이언트가 사용된다면 다음 노트가 활용될 수 있다. Document 1050942.1 How to Trace the Network Packets Exchanged Between JDBC and the RDBMS in Release 11.2
3. Oracle Net 트레이싱을 서버 사이드 레벨 16으로 걸어서 확인한다. 클라이언트 SQLNET.ORA 화일 안에 아래와 같이 추가한다.
DIAG_ADR_ENABLED=off # Diable ADR if version 11g TRACE_LEVEL_SERVER = 16 # Enable level 16 trace TRACE_TIMESTAMP_SERVER = ON # Set timestamp in the trace files TRACE_DIRECTORY_SERVER = <DIRECTORY> # Control trace file location
TRACE_FILELEN_SERVER =<n> #Control size of trace set in kilobytes eg 20480 TRACE_FILENO_SERVER =<n> #Control number of trace files per process
트레이싱을 순환하게 되면 생성되는 트레이스 화일의 갯수와 크기를 조절할 수 있다.
TRACE_FILELEN 파라미터는 트레이스 화일의 크기를 셋팅하기 위해 사용된다. TRACE_FILENO 파라미터는 프로세스 당 트레이스 화일의 갯수를 셋팅하기 위해 사용된다.
중요 노트:
SQLNET.ORA 화일은 프로세스 생성 시 단 한번 읽혀진다. RDBMS 백그라운드 프로세스와 SHARED 서버 디스패처는 sqlnet.ora 화일의 파라미터 변경이 반영될 수 있도록 재기동되어야 한다. 프로세스가 트레이싱되기 위해 기동이 되었다면 트레이스는 프로세스가 멈출 때까지 중단되지 않는다.
Oracle Net 서버 트레이싱을 활성화하면 짧은 시간 동안에 많은 양의 트레이스를 생성시킬 수 있다. 비록 순환적인 트레이싱을 하더라도 각 프로세스는 TRACE_FILENO_SERVER 에 지정한 갯수 만큼의 트레이스를 생성시킬 것이다. 최적의 트레이싱 워크플로우는 트레이싱을 활성화하고 문제를 재현하고 트레이싱을 비활성화시키는 것이다. 그러므로, 트레이싱하는 시간의 양을 제한하는 것이 활성화된다.
TRACE_FILENO_SERVER 를 1로 셋팅하고, TRACE_FILELEN_SERVER 를 20480로 셋팅하게 되면 프로세스 당 생성되는 트레이스의 양을 낮춰주기 위한 솔루션이다. 이렇게 셋팅하면 트레이스 화일이 overwrite되고, failure가 발생한 시점의 중요한 데이타를 유실할 수 있음을 명심해야 한다.
4. Errorstack: 에러가 났을 때를 대비해 errorstack 트레이스를 설정한다. 이것은 Oracle Net 클라이언트 트레이스를 캡춰하는 것이 쉽지 않을 때 적용한다.
SQL> alter session set events '609 errorstack(3)';
에러가 재현되는 동안 몇 개의 트레이스를 수집한다.
SQL> alter session set events '609 off';
만일 에러를 만났을 때에는 다음을 수행한다.:
- 서버에서 SQLNET.LOG 화일을 리뷰한다.
- ALERT. LOG 내에 timestamp를 비교하면서 일치하는 엔트리를 찾아본다.
- SQLNET.LOG 화일 내의 엔트리로부터 Oracle Net server trace 이름을 "Tracing to file"이라는 라인에서 찾을 수 있다.
- server 트레이스를 열어서 Connection ID 값에 대해서 grep 또는 찾아본다.
- 그런 다음, 같은 Connection ID 값에 대해서 클라이언트 트레이스 client 디렉토리를 찾아본다.
매칭되는 클라이언트와 서버 트레이스를 찾게 될 것이다. 이 절차는 이 문서에 자세히 소개되어 있다. Document 374116.1 How to Match Oracle Net Client and Server Trace Files
리뷰를 위해 다음을 업로드한다.:
- 매칭되는 Oracle Net 클라이언트와 서버 트레이스 또는 매칭되는 Javanet 과 서버 트레이스 화일.
- ALERT.LOG 와 LISTENER.LOG 화일. (전체의 로그 화일이 아니라 이슈를 커버하는 시간 대의 로그만 있으면 됨)
- 서버 ORACLE_HOME/network/log 아래의 SQLNET.LOG 화일
- errorstack 트레이스 화일.
알려진 이슈들:
- 종종 ORA-609 에러가 접속이 완전히 이루어지기 전에 클라이언트가 접속이 끊기면서 발생한다. LISTENER.ORA 안에 있는 Timeout 파라미터 INBOUND_CONNECT_TIMEOUT_<listener_name> 과 SQLNET.ORA 화일 안에 있는 SQLNET.INBOUND_CONNECT_TIMEOUT 파라미터를 리뷰할 필요가 있다. 기본 시간인 60초를 사용한다면(명시적인 셋팅 없이), 이 파라미터는 증가시킬 필요가 있다.
- 데이타베이스가 수행 중인 서버 머신에서 네트워크 파라미터 셋팅을 확인한다. 셋팅 값이 모두 맞게 되었는지, DNS 서버가 가용한 상태인지 확인한다.
- 서버 플랫폼이 Microsoft Windows인 경우, 각 서비스가 동일한 계정으로 정상 기동되었는지 데이타베이스와 TNS 리스너를 위한 Windows 서비스를 확인해야 한다.
REFERENCESNOTE:793415.1 - How to Perform the Equivalent of SQL*Net Client Tracing with Oracle JDBC Thin Driver NOTE:1050942.1 - How to Trace the Network Packets Exchanged Between JDBC and the RDBMS in Release 11.2 NOTE:609.1 - ORA-609 TNS-12537 and TNS-12547 in 11g Alert.log |
Oracle 2014. 2. 12. 16:14
- V$Option의 내용
PARAMETER |
VALUE |
Partitioning |
TRUE |
Objects |
TRUE |
Real Application Clusters |
FALSE |
Advanced replication |
TRUE |
Bit-mapped indexes |
TRUE |
Connection multiplexing |
TRUE |
Connection pooling |
TRUE |
Database queuing |
TRUE |
Incremental backup and recovery |
TRUE |
Instead-of triggers |
TRUE |
Parallel backup and recovery |
TRUE |
Parallel execution |
TRUE |
Parallel load |
TRUE |
Point-in-time tablespace recovery |
TRUE |
Fine-grained access control |
TRUE |
Proxy authentication/authorization |
TRUE |
Change Data Capture |
TRUE |
Plan Stability |
TRUE |
Online Index Build |
TRUE |
Coalesce Index |
TRUE |
Managed Standby |
TRUE |
Materialized view rewrite |
TRUE |
Database resource manager |
TRUE |
Spatial |
TRUE |
Automatic Storage Management |
FALSE |
Export transportable tablespaces |
TRUE |
Transparent Application Failover |
TRUE |
Fast-Start Fault Recovery |
TRUE |
Sample Scan |
TRUE |
Duplexed backups |
TRUE |
Java |
TRUE |
OLAP Window Functions |
TRUE |
Block Media Recovery |
TRUE |
Fine-grained Auditing |
TRUE |
Application Role |
TRUE |
Enterprise User Security |
TRUE |
Oracle Data Guard |
TRUE |
Oracle Label Security |
FALSE |
OLAP |
TRUE |
Basic Compression |
TRUE |
Join index |
TRUE |
Trial Recovery |
TRUE |
Data Mining |
TRUE |
Online Redefinition |
TRUE |
Streams Capture |
TRUE |
File Mapping |
TRUE |
Block Change Tracking |
TRUE |
Flashback Table |
TRUE |
Flashback Database |
TRUE |
Transparent Data Encryption |
TRUE |
Backup Encryption |
TRUE |
Unused Block Compression |
TRUE |
Oracle Database Vault |
FALSE |
Result Cache |
TRUE |
SQL Plan Management |
TRUE |
SecureFiles Encryption |
TRUE |
Real Application Testing |
TRUE |
Flashback Data Archive |
TRUE |
DICOM |
TRUE |
Active Data Guard |
TRUE |
Server Flash Cache |
TRUE |
Advanced Compression |
TRUE |
XStream |
TRUE |
Deferred Segment Creation |
TRUE |
IT News 2014. 2. 6. 17:49
Most Popular Programming Languages of 2014
February 3, 2014
Every year we release data on the "Most Popular Programming Languages" based on thousands of data points we've collected by processing over 100,000+ coding tests and challenges by over 2,000+ employers.
This gives us a pretty good idea on what the trends are for the upcoming year in terms of what companies are looking for. It's data we hope will be especially helpful for new computer sciences graduates or coders looking to stay ahead of the curve.
For the third year in a row, Python retains it's #1 dominance followed by Java, C++, and Javascript.
This year's most noticeable changes were a 300% increase in Objective-C submissions, a 100% surge in C#, as well as a 33% increase in Javascript submissions while PHP lost -55%, Perl dropped -16%, and Java shrank -14%.
Another major index to look at is from TIBOE which is a more accurate measure of language market share compared to the CodeEval index which is a much better indicator for language popularity in industry.
This of course shouldn't be the only consideration used in choosing a programming language. Read this recent write up from our friends at CodingforInterviews.com for some more insights.
About CodeEval
CodeEval is an exclusive community of over 24,000+ competitive developers. Members can compete with each other, challenge their friends and build out their profiles to showcase their coding skills. Get started on your first coding challenge here (there are over 129!)
For companies, over 2,000+ employers have company profiles and screen candidates on the CodeEval platform. Join today and create a FREE company profile!
@codeeval #programming2014
- See more at: http://blog.codeeval.com/2014?python#.UvNLSZ2wfs2
IT News 2014. 1. 10. 14:13
Boon JSON parser seems to be the fastestI'll publish object serialization numbers later. Last I checked it was quite a bit faster than the rest.Here are some benchmark numbers parsing various sample JSON files from json.org and a sample that a user of Boon JSON sent in.
To go from a 2K JSON String to a Map, Boon is 2x or so faster. Yes you say, but how is Boon at larger files?
The above graph shows Boon, GSON and Jackson parsing a 1.7 MB string. Boon is up to twice as fast.
Yes you say, but how does boon do at parsing byte[], reader, inputStream, etc.? Pretty much Boon wins in every category for all files that I have tested, which were quite a few.
It took a bit of doing. Boon has an I/O lib which I employed to speed up the inputStream and reader support. Boon also has a relaxed mode JSON parser that allows no-quotes etc., it is just as fast as the strict parser.
The above is not a complete list of tests.
Here are some numbers to go with the graphs.
12/25/13 1.7 MB JSON String
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.s.BoonBenchmark.citmCatalog thrpt 8 5 1 873.970 94.240 ops/s
i.g.j.s.GSONBenchmark.citmCatalog thrpt 8 5 1 410.783 217.476 ops/s
i.g.j.s.JacksonASTBenchmark.citmCatalog thrpt 8 5 1 294.690 47.593 ops/s
i.g.j.s.JacksonObjectBenchmark.citmCatalog thrpt 8 5 1 305.787 29.107 ops/s
i.g.j.s.JsonSmartBenchmark.citmCatalog thrpt 8 5 1 311.063 29.646 ops/s
2K JSON String
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.s.BoonBenchmark.medium thrpt 8 5 1 816416.973 13231.453 ops/s
i.g.j.s.GSONBenchmark.medium thrpt 8 5 1 341148.250 18117.075 ops/s
i.g.j.s.JacksonASTBenchmark.medium thrpt 8 5 1 263167.610 147495.795 ops/s
i.g.j.s.JacksonObjectBenchmark.medium thrpt 8 5 1 282024.617 6922.138 ops/s
i.g.j.s.JsonSmartBenchmark.medium thrpt 8 5 1 296944.993 7852.929 ops/s
1.7 MB JSON byte[]
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.b.BoonBenchmark.citmCatalog thrpt 8 5 1 628.710 91.286 ops/s
i.g.j.b.GSONBenchmark.citmCatalog thrpt 8 5 1 439.203 120.003 ops/s
i.g.j.b.JacksonASTBenchmark.citmCatalog thrpt 8 5 1 381.350 97.841 ops/s
i.g.j.b.JacksonObjectBenchmark.citmCatalog thrpt 8 5 1 402.537 3.634 ops/s
i.g.j.b.JsonSmartBenchmark.citmCatalog thrpt 8 5 1 341.940 18.847 ops/s
2K JSON byte[]
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.b.BoonBenchmark.medium thrpt 8 5 1 648162.887 18697.319 ops/s
i.g.j.b.GSONBenchmark.medium thrpt 8 5 1 260145.827 5934.588 ops/s
i.g.j.b.JacksonASTBenchmark.medium thrpt 8 5 1 289863.140 48969.875 ops/s
i.g.j.b.JacksonObjectBenchmark.medium thrpt 8 5 1 289010.543 11205.881 ops/s
i.g.j.b.JsonSmartBenchmark.medium thrpt 8 5 1 262873.957 3901.193 ops/s
1.7 MB JSON Inputstream
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.inputStream.BoonBenchmark.citmCatalog thrpt 8 5 1 626.907 31.450 ops/s
i.g.j.inputStream.GSONBenchmark.citmCatalog thrpt 8 5 1 426.120 13.946 ops/s
i.g.j.inputStream.JacksonASTBenchmark.citmCatalog thrpt 8 5 1 376.820 115.502 ops/s
i.g.j.inputStream.JacksonObjectBenchmark.citmCatalog thrpt 8 5 1 360.850 89.648 ops/s
2K file JSON Inputstream
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.inputStream.BoonBenchmark.medium thrpt 8 5 1 218730.830 5262.596 ops/s
i.g.j.inputStream.GSONBenchmark.medium thrpt 8 5 1 151255.407 4486.414 ops/s
i.g.j.inputStream.JacksonASTBenchmark.medium thrpt 8 5 1 156512.527 107512.401 ops/s
i.g.j.inputStream.JacksonObjectBenchmark.medium thrpt 8 5 1 160793.407 4056.790 ops/s
1.7 MB JSON Reader
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.r.BoonBenchmark.citmCatalog thrpt 8 5 1 615.313 63.716 ops/s
i.g.j.r.GSONBenchmark.citmCatalog thrpt 8 5 1 411.847 18.978 ops/s
i.g.j.r.JacksonASTBenchmark.citmCatalog thrpt 8 5 1 264.727 118.541 ops/s
i.g.j.r.JacksonObjectBenchmark.citmCatalog thrpt 8 5 1 246.783 93.409 ops/s
i.g.j.r.JsonSmartBenchmark.citmCatalog thrpt 8 5 1 151.097 3.502 ops/s
2k JSON Reader
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.r.BoonBenchmark.medium thrpt 8 5 1 185075.093 6528.567 ops/s
i.g.j.r.GSONBenchmark.medium thrpt 8 5 1 134025.760 3385.134 ops/s
i.g.j.r.JacksonASTBenchmark.medium thrpt 8 5 1 107676.323 60674.421 ops/s
i.g.j.r.JacksonObjectBenchmark.medium thrpt 8 5 1 116903.500 3206.994 ops/s
i.g.j.r.JsonSmartBenchmark.medium thrpt 8 5 1 77898.710 2434.773 ops/s
Other JSON.org examples: webxml json.org example
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.s.BoonBenchmark.webxml thrpt 8 5 1 421016.033 13428.790 ops/s
i.g.j.s.GSONBenchmark.webxml thrpt 8 5 1 143801.263 7870.384 ops/s
i.g.j.s.JacksonASTBenchmark.webxml thrpt 8 5 1 125981.563 36753.717 ops/s
i.g.j.s.JacksonObjectBenchmark.webxml thrpt 8 5 1 130069.577 25055.300 ops/s
i.g.j.s.JsonSmartBenchmark.webxml thrpt 8 5 1 132422.153 10254.167 ops/s
Boon 3X faster sgml json.org example
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.s.BoonBenchmark.sgml thrpt 8 5 1 1846015.410 101291.991 ops/s
i.g.j.s.GSONBenchmark.sgml thrpt 8 5 1 988186.433 35337.393 ops/s
i.g.j.s.JacksonASTBenchmark.sgml thrpt 8 5 1 680502.597 289591.197 ops/s
i.g.j.s.JacksonObjectBenchmark.sgml thrpt 8 5 1 709969.980 29621.959 ops/s
i.g.j.s.JsonSmartBenchmark.sgml thrpt 8 5 1 796387.753 22697.397 ops/s
Boon 2x faster actionLabel json.org example
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.s.BoonBenchmark.actionLabel thrpt 8 5 1 1109285.703 78440.576 ops/s
i.g.j.s.GSONBenchmark.actionLabel thrpt 8 5 1 429742.283 10097.416 ops/s
i.g.j.s.JacksonASTBenchmark.actionLabel thrpt 8 5 1 421132.630 10514.598 ops/s
i.g.j.s.JacksonObjectBenchmark.actionLabel thrpt 8 5 1 403535.453 16382.734 ops/s
i.g.j.s.JsonSmartBenchmark.actionLabel thrpt 8 5 1 453847.673 25607.331 ops/s
Boon over 2x faster menu json.org example
Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.s.BoonBenchmark.menu thrpt 8 5 1 2582429.350 700873.986 ops/s
i.g.j.s.GSONBenchmark.menu thrpt 8 5 1 1240234.083 22312.822 ops/s
i.g.j.s.JacksonASTBenchmark.menu thrpt 8 5 1 1242132.793 19273.775 ops/s
i.g.j.s.JacksonObjectBenchmark.menu thrpt 8 5 1 1141071.207 36489.605 ops/s
i.g.j.s.JsonSmartBenchmark.menu thrpt 8 5 1 1463778.480 57490.408 ops/s
Boon 2x faster. Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.s.BoonBenchmark.widget thrpt 8 5 1 1485476.970 79222.003 ops/s
i.g.j.s.GSONBenchmark.widget thrpt 8 5 1 810153.490 20079.953 ops/s
i.g.j.s.JacksonASTBenchmark.widget thrpt 8 5 1 724349.650 284735.196 ops/s
i.g.j.s.JacksonObjectBenchmark.widget thrpt 8 5 1 705271.907 42304.730 ops/s
i.g.j.s.JsonSmartBenchmark.widget thrpt 8 5 1 728506.560 29680.028 ops/s
Boon is damn fast. It has many modes to fit various mediums depending on your goals (small footprint, direct byte parse, etc.). Don't worry, boon is not hard to use. It just works. Benchmark Mode Thr Count Sec Mean Mean error Units
i.g.j.b.BoonAsciiBytes.actionLabel thrpt 8 5 1 302902.677 21981.467 ops/s
i.g.j.b.BoonAsciiBytes.citmCatalog thrpt 8 5 1 628.150 26.607 ops/s
i.g.j.b.BoonAsciiBytes.medium thrpt 8 5 1 320658.760 38751.800 ops/s
i.g.j.b.BoonAsciiBytes.menu thrpt 8 5 1 2081501.213 113660.611 ops/s
i.g.j.b.BoonAsciiBytes.sgml thrpt 8 5 1 998463.200 31916.216 ops/s
i.g.j.b.BoonAsciiBytes.small thrpt 8 5 1 11095898.987 534428.831 ops/s
i.g.j.b.BoonAsciiBytes.webxml thrpt 8 5 1 148348.463 5512.808 ops/s
i.g.j.b.BoonAsciiBytes.widget thrpt 8 5 1 879580.747 14598.011 ops/s
i.g.j.b.BoonBenchMarkLax.actionLabel thrpt 8 5 1 806689.270 28745.917 ops/s
i.g.j.b.BoonBenchMarkLax.citmCatalog thrpt 8 5 1 633.087 77.455 ops/s
i.g.j.b.BoonBenchMarkLax.medium thrpt 8 5 1 569042.093 61404.916 ops/s
i.g.j.b.BoonBenchMarkLax.menu thrpt 8 5 1 2600248.763 105320.234 ops/s
i.g.j.b.BoonBenchMarkLax.sgml thrpt 8 5 1 1476412.973 284184.058 ops/s
i.g.j.b.BoonBenchMarkLax.small thrpt 8 5 1 13336195.790 1442531.930 ops/s
i.g.j.b.BoonBenchMarkLax.webxml thrpt 8 5 1 270060.157 6539.573 ops/s
i.g.j.b.BoonBenchMarkLax.widget thrpt 8 5 1 1262768.937 51676.215 ops/s
i.g.j.b.BoonBenchMarkUTF8Bytes.actionLabel thrpt 8 5 1 185209.077 670100.163 ops/s
i.g.j.b.BoonBenchMarkUTF8Bytes.citmCatalog thrpt 8 5 1 379.917 30.037 ops/s
i.g.j.b.BoonBenchMarkUTF8Bytes.medium thrpt 8 5 1 217107.220 5247.417 ops/s
i.g.j.b.BoonBenchMarkUTF8Bytes.menu thrpt 8 5 1 1319969.417 79745.189 ops/s
i.g.j.b.BoonBenchMarkUTF8Bytes.sgml thrpt 8 5 1 688184.650 34033.100 ops/s
i.g.j.b.BoonBenchMarkUTF8Bytes.small thrpt 8 5 1 7486431.520 1228519.698 ops/s
i.g.j.b.BoonBenchMarkUTF8Bytes.webxml thrpt 8 5 1 104078.393 15332.908 ops/s
i.g.j.b.BoonBenchMarkUTF8Bytes.widget thrpt 8 5 1 526663.853 214399.644 ops/s
i.g.j.b.BoonCharArray.actionLabel thrpt 8 5 1 407056.423 149970.346 ops/s
i.g.j.b.BoonCharArray.citmCatalog thrpt 8 5 1 391.130 55.374 ops/s
i.g.j.b.BoonCharArray.medium thrpt 8 5 1 320601.040 83669.815 ops/s
i.g.j.b.BoonCharArray.menu thrpt 8 5 1 1686792.320 112046.346 ops/s
i.g.j.b.BoonCharArray.sgml thrpt 8 5 1 1052574.220 44541.919 ops/s
i.g.j.b.BoonCharArray.small thrpt 8 5 1 8071292.173 663678.327 ops/s
i.g.j.b.BoonCharArray.webxml thrpt 8 5 1 181207.910 32126.919 ops/s
i.g.j.b.BoonCharArray.widget thrpt 8 5 1 878541.030 137067.187 ops/s
i.g.j.b.BoonFastParser.actionLabel thrpt 8 5 1 601141.330 77361.337 ops/s
i.g.j.b.BoonFastParser.citmCatalog thrpt 8 5 1 429.987 198.559 ops/s
i.g.j.b.BoonFastParser.medium thrpt 8 5 1 462712.293 118751.410 ops/s
i.g.j.b.BoonFastParser.menu thrpt 8 5 1 1981728.817 239514.140 ops/s
i.g.j.b.BoonFastParser.sgml thrpt 8 5 1 1117030.450 209863.168 ops/s
i.g.j.b.BoonFastParser.small thrpt 8 5 1 10197156.600 169372.770 ops/s
i.g.j.b.BoonFastParser.webxml thrpt 8 5 1 230100.983 62048.894 ops/s
i.g.j.b.BoonFastParser.widget thrpt 8 5 1 1242538.033 169654.975 ops/s
i.g.j.b.BoonStringDirect.actionLabel thrpt 8 5 1 461358.763 45184.611 ops/s
i.g.j.b.BoonStringDirect.citmCatalog thrpt 8 5 1 332.883 25.544 ops/s
i.g.j.b.BoonStringDirect.medium thrpt 8 5 1 323354.063 18819.168 ops/s
i.g.j.b.BoonStringDirect.menu thrpt 8 5 1 1668149.967 52797.831 ops/s
i.g.j.b.BoonStringDirect.sgml thrpt 8 5 1 933777.700 77093.442 ops/s
i.g.j.b.BoonStringDirect.small thrpt 8 5 1 7111685.283 205942.968 ops/s
i.g.j.b.BoonStringDirect.webxml thrpt 8 5 1 154376.677 50416.916 ops/s
i.g.j.b.BoonStringDirect.widget thrpt 8 5 1 575450.757 45103.058 ops/s
출처 : http://rick-hightower.blogspot.kr/2013/12/boon-json-parser-seems-to-be-fastest.html
Oracle 2013. 11. 15. 18:39
TRIM
Syntax Description of the illustration trim.gif
Purpose TRIM enables you to trim leading or trailing characters (or both) from a character string. If trim_character or trim_source is a character literal, then you must enclose it in single quotation marks.
If you specify LEADING , then Oracle Database removes any leading characters equal to trim_character . If you specify TRAILING , then Oracle removes any trailing characters equal to trim_character . If you specify BOTH or none of the three, then Oracle removes leading and trailing characters equal to trim_character . If you do not specify trim_character , then the default value is a blank space. If you specify only trim_source , then Oracle removes leading and trailing blank spaces. The function returns a value with datatype VARCHAR2 . The maximum length of the value is the length of trim_source . If either trim_source or trim_character is null, then the TRIM function returns null.
Both trim_character and trim_source can be VARCHAR2 or any datatype that can be implicitly converted to VARCHAR2 . The string returned is of VARCHAR2 datatype if trim_source is a character datatype and a LOB if trim_source is a LOB datatype. The return string is in the same character set as trim_source . Examples This example trims leading zeros from the hire date of the employees in the hr schema: SELECT employee_id,
TO_CHAR(TRIM(LEADING 0 FROM hire_date))
FROM employees
WHERE department_id = 60
ORDER BY employee_id;
EMPLOYEE_ID TO_CHAR(T
----------- ---------
103 3-JAN-90
104 21-MAY-91
105 25-JUN-97
106 5-FEB-98
107 7-FEB-99
LTRIMSyntax Description of the illustration ltrim.gif
Purpose LTRIM removes from the left end of char all of the characters contained in set . If you do not specify set , then it defaults to a single blank. If char is a character literal, then you must enclose it in single quotation marks. Oracle Database begins scanning char from its first character and removes all characters that appear in set until reaching a character not in set and then returns the result.
Both char and set can be any of the datatypes CHAR , VARCHAR2 , NCHAR , NVARCHAR2 , CLOB , or NCLOB . The string returned is of VARCHAR2 datatype if char is a character datatype, NVARCHAR2 if char is a national character datatype, and a LOB if char is a LOB datatype. The following example trims the redundant first word from a group of product names in the oe.products table: SELECT product_name, LTRIM(product_name, 'Monitor ') "Short Name"
FROM products
WHERE product_name LIKE 'Monitor%';
PRODUCT_NAME Short Name
-------------------- ---------------
Monitor 17/HR 17/HR
Monitor 17/HR/F 17/HR/F
Monitor 17/SD 17/SD
Monitor 19/SD 19/SD
Monitor 19/SD/M 19/SD/M
Monitor 21/D 21/D
Monitor 21/HR 21/HR
Monitor 21/HR/M 21/HR/M
Monitor 21/SD 21/SD
Monitor Hinge - HD Hinge - HD
Monitor Hinge - STD Hinge - STD
RTRIMSyntax Description of the illustration rtrim.gif
Purpose RTRIM removes from the right end of char all of the characters that appear in set . This function is useful for formatting the output of a query.
If you do not specify set , then it defaults to a single blank. If char is a character literal, then you must enclose it in single quotation marks. RTRIM works similarly to LTRIM . Both char and set can be any of the datatypes CHAR , VARCHAR2 , NCHAR , NVARCHAR2 , CLOB , or NCLOB . The string returned is of VARCHAR2 datatype if char is a character datatype, NVARCHAR2 if expr1 is a national character datatype, and a LOB if char is a LOB datatype. The following example trims all the right-most occurrences of period, slash, and equal sign from a string: SELECT RTRIM('BROWNING: ./=./=./=./=./=.=','/=.') "RTRIM example" FROM DUAL;
RTRIM exam
----------
BROWNING:
reference : Oracle® Database SQL Language Reference - Oracle11gR1
그동안 너무 RTRM과 LTRIM만 사용한듯....
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