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In September 2018, IBM announced a brand unusual product, IBM Db2 AI for z/OS. This simulated intelligence engine monitors data access patterns from executing SQL statements, uses desktop studying algorithms to resolve upon most dependable patterns and passes this counsel to the Db2 question optimizer to exist used with the aid of subsequent statements.computing device getting to know on the IBM z Platform
In can also of 2018, IBM introduced version 1.2 of its computing device discovering for z/OS (MLz) product. here's a hybrid zServer and cloud application suite that ingests performance facts, analyzes and builds fashions that characterize the health popularity of quite a lot of warning signs, screens them over time and gives precise-time scoring capabilities.
a few facets of this product offering are aimed toward supporting a group of mannequin builders and managers. as an example:
This desktop discovering suite changed into at first aimed at zServer-based mostly analytics functions. some of the first evident choices become zSystem efficiency monitoring and tuning. gadget management Facility (SMF) records that are immediately generated through the working system deliver the uncooked facts for device aid consumption similar to faultfinding processor usage, I/O processing, memory paging etc. IBM MLz can compile and sustain these records over time, and construct and discipline models of gadget conduct, score these behaviors, determine patterns not conveniently foreseen by means of people, enhance key efficiency indications (KPIs) and then feed the model effects again into the device to affect system configuration changes that can multiply performance.
The next step turned into to set aside into outcome this suite to research Db2 performance records. One solution, called the IBM Db2 IT Operational Analytics (Db2 ITOA) reply template, applies the computing device discovering expertise to Db2 operational records to profit an understanding of Db2 subsystem fitness. it might probably dynamically build baselines for key performance warning signs, supply a dashboard of these KPIs and give operational corpse of workers precise-time insight into Db2 operations.
whereas usual Db2 subsystem efficiency is a vital component in universal utility health and performance, IBM estimates that the DBA champion personnel spends 25% or greater of its time, " ... fighting access route problems which cause performance degradation and service gain an outcome on.". (See Reference 1).AI comes to Db2
trust the plight of modern DBAs in a Db2 environment. In modern day IT world they ought to aid one or extra great data applications, cloud application and database services, application setting up and configuration, Db2 subsystem and utility efficiency tuning, database definition and administration, catastrophe recovery planning, and greater. query tuning has been in actuality on account that the origins of the database, and DBAs are always tasked with this as smartly.
The coronary heart of query route analysis in Db2 is the Optimizer. It accepts SQL statements from functions, verifies authority to access the records, reviews the places of the objects to exist accessed and develops a list of candidate information access paths. These entry paths can comprise indexes, table scans, a lot of desk relate strategies and others. in the records warehouse and great records environments there are usually further selections purchasable. One of these is the actuality of summary tables (on occasion referred to as materialized question tables) that comprise pre-summarized or aggregated statistics, as a consequence allowing Db2 to sustain away from re-aggregation processing. a different option is the starjoin entry course, common in the statistics warehouse, the area the order of desk joins is changed for efficiency motives.
The Optimizer then reports the candidate access paths and chooses the access path, "with the lowest cost." charge in this context skill a weighted summation of useful resource utilization including CPU, I/O, reminiscence and other elements. finally, the Optimizer takes the bottom cost entry path, shops it in reminiscence (and, optionally, within the Db2 listing) and starts off entry direction execution.
big statistics and statistics warehouse operations now encompass utility suites that allow the enterprise analyst to consume a graphical interface to build and exploit a miniature information mannequin of the information they requisite to analyze. The applications then generate SQL statements based on the users’ requests.
The issue for the DBA
with a view to achieve decent analytics on your dissimilar data outlets you requisite an excellent understanding of the facts necessities, an understanding of the analytical functions and algorithms attainable and a excessive-performance facts infrastructure. sadly, the quantity and placement of records sources is increasing (both in dimension and in geography), statistics sizes are transforming into, and functions proceed to proliferate in number and complexity. How may still IT managers champion this atmosphere, especially with probably the most skilled and mature workforce nearing retirement?
understand also that a huge section of decreasing the total charge of possession of these techniques is to come by Db2 applications to sprint sooner and more correctly. This usually interprets into using fewer CPU cycles, doing fewer I/Os and transporting less facts across the community. since it's frequently problematic to even determine which applications may odds from efficiency tuning, one strategy is to automate the detection and correction of tuning considerations. here is the area desktop gaining knowledge of and synthetic intelligence will also exist used to excellent effect.Db2 12 for z/OS and synthetic Intelligence
Db2 version 12 on z/OS uses the computing device researching facilities mentioned above to accumulate and sustain SQL question textual content and access direction particulars, as well as actual performance-related ancient guidance akin to CPU time used, elapsed times and outcomes set sizes. This providing, described as Db2 AI for z/OS, analyzes and outlets the facts in laptop studying models, with the mannequin evaluation consequences then being scored and made attainable to the Db2 Optimizer. The subsequent time a scored SQL commentary is encountered, the Optimizer can then consume the mannequin scoring statistics as enter to its entry path option algorithm.
The outcome should still exist a discount in CPU consumption as the Optimizer uses mannequin scoring input to select stronger entry paths. This then lowers CPU charges and speeds software response times. a major skills is that the consume of AI utility does not require the DBA to gain information science talents or abysmal insights into query tuning methodologies. The Optimizer now chooses the most dependable entry paths based mostly no longer simplest on SQL question syntax and facts distribution statistics but on modelled and scored ancient efficiency.
This may also exist particularly faultfinding in case you reclaim information in varied places. for instance, many analytical queries against great facts require concurrent entry to inescapable information warehouse tables. These tables are often known as dimension tables, and they comprise the information aspects constantly used to wield subsetting and aggregation. for example, in a retail atmosphere accept as sincere with a desk known as StoreLocation that enumerates every sustain and its location code. Queries towards store sales statistics might also requisite to composite or summarize income by pass of area; therefore, the StoreLocation desk will exist used by some massive data queries. in this atmosphere it's ordinary to hold the dimension tables and duplicate them continuously to the massive facts application. in the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).
Now esteem about SQL queries from both operational applications, facts warehouse users and massive data company analysts. From Db2's perspective, All these queries are equal, and are forwarded to the Optimizer. youngsters, in the case of operational queries and warehouse queries they may still surely exist directed to entry the StoreLocation table in the warehouse. nevertheless, the query from the traffic analyst towards great records tables should still doubtless entry the replica of the desk there. This effects in a proliferations of skills entry paths, and extra labor for the Optimizer. luckily, Db2 AI for z/OS can supply the Optimizer the counsel it needs to create wise access route decisions.the pass it Works
The sequence of movements in Db2 AI for z/OS (See Reference 2) is often here:
There are additionally various consumer interfaces that provide the administrator visibility to the status of the accrued SQL remark performance information and mannequin scoring.abstract
IBM's desktop studying for zOS (MLz) offering is getting used to high-quality outcome in Db2 version 12 to enhance the efficiency of analytical queries in addition to operational queries and their linked applications. This requires administration attention, as you must examine that your enterprise is ready to consume these ML and AI conclusions. How will you measure the prices and benefits of the usage of desktop gaining knowledge of? Which IT champion workforce should exist tasked to reviewing the result of model scoring, and perhaps approving (or overriding) the consequences? How will you assessment and warrant the assumptions that the application makes about entry path decisions?
In different words, how well were you cognizant your information, its distribution, its integrity and your existing and proposed entry paths? this will verify the area the DBAs spend their time in supporting analytics and operational software performance.
# # #
John Campbell, IBM Db2 unique EngineerFrom "IBM Db2 AI for z/OS: raise IBM Db2 software efficiency with computing device studying"https://www.worldofdb2.com/events/ibm-db2-ai-for-z-os-increase-ibm-db2-utility-performance-with-ma
Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
Feb 19, 2019 (Heraldkeeper by the consume of COMTEX) -- international ERP utility Market by means of producers, areas, class and application, Forecast to 2023
Wiseguyreports.Com adds "ERP utility – Market Demand, boom, opportunities and evaluation of exact Key avid gamers to 2023" To Its research Database
Geographically, this record is segmented into a couple of key areas, with construction, consumption, revenue (M USD), market share and multiply rate of ERP utility in these areas, from 2012 to 2023 (forecast), coveringNorth the united states (united states, Canada and Mexico)Europe (Germany, France, UK, Russia and Italy)Asia-Pacific (China, Japan, Korea, India and Southeast Asia)South the united states (Brazil, Argentina, Columbia)middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)global ERP software market competitors by suitable producers, with production, rate, income (value) and market share for each and every company; the privilege players includingSAPOracleSageInforMicrosoftEpicorKronosConcur (SAP)IBMTotvsUNIT4YonYouNetSuiteKingdeeWorkday
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On the basis of product, this report shows the creation, revenue, expense, market share and growth expense of every category, basically split intoOn-premise ERPCloud ERPOn the groundwork on the conclusion users/applications, this file focuses on the fame and outlook for main applications/end clients, consumption (earnings), market share and boom expense of ERP utility for each and every utility, includingManufactureLogistics IndustryFinancialTelecommunicationsEnergyTransportation
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principal Key points in desk of content material
global ERP utility Market by producers, regions, classification and application, Forecast to 20231 report Overview1.1 Definition and Specification1.2 document Overview1.2.1 manufacturers Overview1.2.2 regions Overview1.2.3 classification Overview1.2.4 software Overview1.three Industrial Chain1.3.1 ERP software simple Industrial Chain1.3.2 Upstream1.three.3 Downstream1.four trade Situation1.four.1 Industrial Policy1.4.2 Product Preference1.four.3 financial/Political Environment1.5 SWOT analysis
four producers Profiles/Analysis4.1 SAP4.1.1 SAP Profiles4.1.2 SAP Product Information4.1.three SAP ERP application company Performance4.1.4 SAP ERP utility company evolution and Market Status4.2 Oracle4.2.1 Oracle Profiles4.2.2 Oracle Product Information4.2.three Oracle ERP application company Performance4.2.four Oracle ERP utility company evolution and Market Status4.3 Sage4.3.1 Sage Profiles4.three.2 Sage Product Information4.3.three Sage ERP software traffic Performance4.3.four Sage ERP utility enterprise construction and Market Status4.4 Infor4.four.1 Infor Profiles4.4.2 Infor Product Information4.4.three Infor ERP utility company Performance4.4.four Infor ERP application company pile and Market Status4.5 Microsoft4.5.1 Microsoft Profiles4.5.2 Microsoft Product Information4.5.3 Microsoft ERP application enterprise Performance4.5.4 Microsoft ERP utility company construction and Market Status4.6 Epicor4.6.1 Epicor Profiles4.6.2 Epicor Product Information4.6.three Epicor ERP software traffic Performance4.6.4 Epicor ERP application company construction and Market Status4.7 Kronos4.7.1 Kronos Profiles4.7.2 Kronos Product Information4.7.3 Kronos ERP software traffic Performance4.7.4 Kronos ERP software traffic evolution and Market Status4.8 Concur (SAP)four.8.1 Concur (SAP) Profiles4.8.2 Concur (SAP) Product Information4.eight.three Concur (SAP) ERP software traffic Performance4.8.4 Concur (SAP) ERP application enterprise pile and Market Status4.9 IBM4.9.1 IBM Profiles4.9.2 IBM Product Information4.9.three IBM ERP utility traffic Performance4.9.four IBM ERP software traffic pile and Market Status4.10 Totvs4.10.1 Totvs Profiles4.10.2 Totvs Product Information4.10.three Totvs ERP utility traffic Performance4.10.four Totvs ERP utility traffic evolution and Market Status4.11 UNIT44.12 YonYou4.13 Sage4.14 Infor4.15 Microsoft
12 Market Forecast 2019-202412.1 sales (okay contraptions), earnings (M USD), Market share and boom cost 2019-202412.1.1 world ERP software sales (ok instruments), salary (M USD) and Market share by using regions 2019-202412.1.2 global ERP application income (k units) and multiply cost 2019-202412.1.3 Asia-Pacific ERP application income (k units), earnings (M USD) and growth expense 2019-202412.1.four Asia-Pacific ERP software earnings (ok gadgets), salary (M USD) and boom expense 2019-202412.1.5 Europe ERP application sales (ok contraptions), revenue (M USD) and growth cost 2019-202412.1.6 South america ERP application income (k units), salary (M USD) and boom fee 2019-202412.1.7 core East and Africa ERP utility revenue (k devices), salary (M USD) and multiply rate 2019-202412.2 revenue (k instruments), income (M USD) via kinds 2019-202412.2.1 ordinary Market Performance12.2.2 On-premise ERP sales (okay devices), earnings (M USD) and boom cost 2019-202412.2.3 Cloud ERP earnings (ok instruments), revenue (M USD) and multiply fee 2019-202412.3 income through application 2019-202412.3.1 ordinary Market Performance12.3.2 Manufacture earnings and and multiply cost 2019-202412.3.3 Logistics traffic income and and growth expense 2019-202412.three.4 monetary earnings and and boom rate 2019-202412.3.5 Telecommunications revenue and and multiply cost 2019-202412.4 rate (USD/Unit) and raw Profit12.four.1 global ERP utility fee (USD/Unit) style 2019-202412.four.2 global ERP application raw earnings vogue 2019-2024
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I’ve just completed IBM DB2 for Linux, Unix and Windows (LUW) coverage here on consume The Index, Luke as preparation for an upcoming training I’m giving. This blog post describes the major differences I’ve institute compared to the other databases I’m covering (Oracle, SQL Server, PostgreSQL and MySQL).Free & Easy
Well, let’s countenance it: it’s IBM software. It has a pretty long history. You would probably not anticipate that it is light to install and configure, but in fact: it is. At least DB2 LUW Express-C 10.5 (LUW is for Linux, Unix and Windows, Express-C is the free community edition). That might exist another surprise: there is a free community edition. It’s not open source, but it’s free as in free beer.No light Explain
The first problem I stumbled upon is that DB2 has no light pass to pomp an execution plan. No kidding. Here is what IBM says about it:
Explain a statement by prefixing it with clarify plot for
This stores the execution plot in a set of tables in the database (you’ll requisite to create these tables first). This is pretty much like in Oracle.
Display a stored clarify plot using db2exfmt
This is a command line tool, not something you can plunge from an SQL prompt. To sprint this appliance you’ll requisite shell access to a DB2 installation (e.g. on the server). That means, that you cannot consume this appliance over an regular database connection.
There is another command line appliance (db2expln) that combines the two steps from above. Apart from the fact that this procedure is not exactly convenient, the output you come by an ASCII art:Access Plan: ----------- Total Cost: 60528.3 Query Degree: 1 Rows RETURN ( 1) Cost I/O | 49534.9 ^HSJOIN ( 2) 60528.3 68095 /-----+------\ 49534.9 10000 TBSCAN TBSCAN ( 3) ( 4) 59833.6 687.72 67325 770 | | 1.00933e+06 10000 TABLE: DB2INST1 TABLE: DB2INST1 SALES EMPLOYEES Q2 Q1
Please note that this is just an excerpt—the full output of db2exfmt has 400 lines. Quite a lot information that you’ll hardly ever need. Even the information that you requisite All the time (the operations) is presented in a pretty unreadable pass (IMHO). I’m particularly thankful that All the numbers you observe above are not labeled—that’s really the icing that renders this “tool” totally useless for the occasional user.
However, according to the IBM documentation there is another pass to pomp an execution plan: “Write your own queries against the clarify tables.” And that’s exactly what I did: I wrote a view called last_explained that does exactly what it’s designation suggest: it shows the execution plot of the eventual statement that was explained (in a non-useless formatting):Explain Plan ------------------------------------------------------------ ID | Operation | Rows | Cost 1 | return | | 60528 2 | HSJOIN | 49535 of 10000 | 60528 3 | TBSCAN SALES | 49535 of 1009326 ( 4.91%) | 59833 4 | TBSCAN EMPLOYEES | 10000 of 10000 (100.00%) | 687 Predicate Information 2 - relate (Q2.SUBSIDIARY_ID = DECIMAL(Q1.SUBSIDIARY_ID, 10, 0)) relate (Q2.EMPLOYEE_ID = DECIMAL(Q1.EMPLOYEE_ID, 10, 0)) 3 - SARG ((CURRENT DATE - 6 MONTHS) < Q2.SALE_DATE) Explain plot by Markus Winand - NO WARRANTY http://use-the-index-luke.com/s/last_explained
I’m pretty positive many DB2 users will elucidate that this presentation of the execution plot is confusing. And that’s OK. If you are used to the pass IBM presents execution plans, just stick to what you are used to. However, I’m working with All kinds of databases and they All gain a pass to pomp the execution plot similar to the one shown above—for me this format is much more useful. Further, I’ve made a useful selection of data to display: the row signify estimates and the predicate information.
You can come by the source of the last_explained view from here or from GitHub (direct download). I’m earnest about the no warranty part. Yet I’d like to know about problems you gain with the view.Emulating Partial Indexes is Possible
Partial indexes are indexes not containing All table rows. They are useful in three cases:
To preserve space when the index is only useful for a very tiny fraction of the rows. Example: queue tables.
To establish a specific row order in presence of constant non-equality predicates. Example: WHERE x IN (1, 5, 9) ORDER BY y. An index like the following can exist used to avoid a sort operation:CREATE INDEX … ON … (y) WHERE x IN (1, 5, 9)
To implement unique constraints on a subset of rows (e.g. only those WHERE active = 'Y').
However, DB2 doesn’t champion a where clause for indexes like shown above. But DB2 has many Oracle-compatibility features, one of them is EXCLUDE NULL KEYS: “Specifies that an index entry is not created when All parts of the index key hold the null value.” This is actually the hard-wired behaviour in the Oracle database and it is commonly exploited to emulate partial indexes in the Oracle database.
Generally speaking, emulating partial indexes works by mapping All parts of the key (all indexed columns) to NULL for rows that should not finish up in the index. As an example, let’s emulate this partial index in the Oracle database (DB2 is next):CREATE INDEX messages_todo ON messages (receiver) WHERE processed = 'N'
The solution presented in SQL Performance Explained uses a role to map the processed rows to NULL, otherwise the receiver value is passed through:CREATE OR REPLACE FUNCTION pi_processed(processed CHAR, receiver NUMBER) RETURN NUMBER DETERMINISTIC AS BEGIN IF processed IN ('N') THEN return receiver; ELSE return NULL; finish IF; END; /
It’s a deterministic role and can thus exist used in an Oracle function-based index. This won’t labor with DB2, because DB2 doesn’t allow user defined-functions in index definitions. However, let’s first complete the Oracle example.CREATE INDEX messages_todo ON messages (pi_processed(processed, receiver));
This index has only rows WHERE processed IN ('N')—otherwise the role returns NULL which is not set aside in the index (there is no other column that could exist non-NULL). Voilà: a partial index in the Oracle database.
To consume this index, just consume the pi_processed role in the where clause:SELECT message FROM messages WHERE pi_processed(processed, receiver) = ?
This is functionally equivalent to:SELECT message FROM messages WHERE processed = 'N' AND receiver = ?
So far, so ugly. If you Go for this approach, you’d better requisite the partial index desperately.
To create this approach labor in DB2 they requisite two components: (1) the EXCLUDE NULL KEYS clause (no-brainer); (2) a pass to map processed rows to NULL without using a user-defined role so it can exist used in a DB2 index.
Although the second one might appear to exist hard, it is actually very simple: DB2 can achieve expression based indexing, just not on user-defined functions. The mapping they requisite can exist accomplished with regular SQL expressions:CASE WHEN processed = 'N' THEN receiver ELSE NULL END
This implements the very selfsame mapping as the pi_processed role above. remember that CASE expressions are first class citizens in SQL—they can exist used in DB2 index definitions (on LUW just since 10.5):CREATE INDEX messages_not_processed_pi ON messages (CASE WHEN processed = 'N' THEN receiver ELSE NULL END) EXCLUDE NULL KEYS;
This index uses the CASE expression to map not to exist indexed rows to NULL and the EXCLUDE NULL KEYS feature to obviate those row from being stored in the index. Voilà: a partial index in DB2 LUW 10.5.
To consume the index, just consume the CASE expression in the where clause and check the execution plan:SELECT * FROM messages WHERE (CASE WHEN processed = 'N' THEN receiver ELSE NULL END) = ?; Explain Plan ------------------------------------------------------- ID | Operation | Rows | Cost 1 | return | | 49686 2 | TBSCAN MESSAGES | 900 of 999999 ( .09%) | 49686 Predicate Information 2 - SARG (Q1.PROCESSED = 'N') SARG (Q1.RECEIVER = ?)
Oh, that’s a sizable disappointment: the optimizer didn’t hold the index. It does a full table scan instead. What’s wrong?
If you gain a very immediate Look at the execution plot above, which I created with my last_explained view, you might observe something suspicious.
Look at the predicate information. What happened to the CASE expression that they used in the query? The DB2 optimizer was smart enough rewrite the expression as WHERE processed = 'N' AND receiver = ?. Isn’t that great? Absolutely!…except that this smartness has just ruined my attempt to consume the partial index. That’s what I meant when I said that CASE expressions are first class citizens in SQL: the database has a pretty superb understanding what they achieve and can transform them.
We requisite a pass to apply their magic NULL-mapping but they can’t consume functions (can’t exist indexed) nor can they consume CASE expressions, because they are optimized away. Dead-end? Au contraire: it’s pretty light to hurl an optimizer. All you requisite to achieve is to obfuscate the CASE expression so that the optimizer doesn’t transform it anymore. Adding zero to a numeric column is always my first attempt in such cases:CASE WHEN processed = 'N' THEN receiver + 0 ELSE NULL END
The CASE expression is essentially the same, I’ve just added zero to the RECEIVER column, which is numeric. If I consume this expression in the index and the query, I come by this execution plan:ID | Operation | Rows | Cost 1 | return | | 13071 2 | FETCH MESSAGES | 40000 of 40000 | 13071 3 | RIDSCN | 40000 of 40000 | 1665 4 | SORT (UNQIUE) | 40000 of 40000 | 1665 5 | IXSCAN MESSAGES_NOT_PROCESSED_PI | 40000 of 999999 | 1646 Predicate Information 2 - SARG ( CASE WHEN (Q1.PROCESSED = 'N') THEN (Q1.RECEIVER + 0) ELSE NULL finish = ?) 5 - START ( CASE WHEN (Q1.PROCESSED = 'N') THEN (Q1.RECEIVER + 0) ELSE NULL finish = ?) desist ( CASE WHEN (Q1.PROCESSED = 'N') THEN (Q1.RECEIVER + 0) ELSE NULL finish = ?)
The partial index is used as intended. The CASE expression appears unchanged in the predicate information section.
I haven’t checked any other ways to emulate partial indexes in DB2 (e.g., using partitions like in more recent Oracle versions).
As always: just because you can achieve something doesn’t denote you should. This approach is so ugly—even more gruesome than the Oracle workaround—that you must desperately requisite a partial index to warrant this maintenance nightmare. Further it will desist working whenever the optimizer becomes smart enough to optimize +0 away. However, then you just requisite set aside an even more gruesome obfuscation in there.INCLUDE Clause Only for Unique Indexes
With the comprise clause you can add extra columns to an index for the sole purpose to allow in index-only scan when these columns are selected. I knew the comprise clause before because SQL Server offers it too, but there are some differences:
In SQL Server comprise columns are only added to the leaf nodes of the index—not in the root and arm nodes. This limits the repercussion on the B-tree’s depth when adding many or long columns to an index. This also allows to bypass some limitations (number of columns, total index row length, allowed data types). That doesn’t appear to exist the case in DB2.
In DB2 the comprise clause is only telling for unique indexes. It allows you to enforce the uniqueness of the key columns only—the comprise columns are just not considered when checking for uniqueness. This is the selfsame in SQL Server except that SQL Server supports comprise columns on non-unique indexes too (to leverage the above-mentioned benefits).
The NULLS FIRST and NULLS eventual modifiers to the order by clause allow you to specify whether NULL values are considered as larger or smaller than non-NULL values during sorting. Strictly speaking, you must always specify the desired order when sorting nullable columns because the SQL yardstick doesn’t specify a default. As you can observe in the following chart, the default order of NULL is indeed different across various databases:
Figure A.1. Database/Feature Matrix
In this chart, you can also observe that DB2 doesn’t champion NULLS FIRST or NULLS LAST—neither in the order by clause no in the index definition. However, note that this is a simplified statement. In fact, DB2 accepts NULLS FIRST and NULLS eventual when it is in line with the default NULLS order. In other words, ORDER BY col ASC NULLS FIRST is valid, but it doesn’t change the result—NULLS FIRST is anyways the default. selfsame is sincere for ORDER BY col DESC NULLS LAST—accepted, but doesn’t change anything. The other two combinations are not telling at All and submit a syntax error.SQL:2008 FETCH FIRST but not OFFSET
DB2 supports the fetch first … rows only clause for a while now—kind-of impressive considering it was “just” added with the SQL:2008 standard. However, DB2 doesn’t champion the offset clause, which was introduced with the very selfsame release of the SQL standard. Although it might Look like an capricious omission, it is in fact a very wise creep that I deeply respect. offset is the root of so much evil. In the next section, I’ll clarify how to live without offset.
Side node: If you gain code using offset that you cannot change, you can still activate the MySQL compatibility vector that makes limit and offset available in DB2. laughable enough, combining fetch first with offset is then still not viable (that would exist yardstick compliant).Decent Row-Value Predicates Support
SQL row-values are multiple scalar values grouped together by braces to form a separate ratiocinative value. IN-lists are a common use-case:WHERE (col_a, col_b) IN (SELECT col_a, col_b FROM…)
This is supported by pretty much every database. However, there is a second, hardly known use-case that has pretty poor champion in today’s SQL databases: key-set pagination or offset-less pagination. Keyset pagination uses a where clause that basically says “I’ve seen everything up till here, just give me the next rows”. In the simplest case it looks like this:SELECT … FROM … WHERE time_stamp < ? ORDER BY time_stamp DESC FETCH FIRST 10 ROWS ONLY
Imagine you’ve already fetched a bunch of rows and requisite to come by the next few ones. For that you’d consume the time_stamp value of the eventual entry you’ve got for the bind value (?). The query then just return the rows from there on. But what if there are two rows with the very selfsame time_stamp value? Then you requisite a tiebreaker: a second column—preferably a unique column—in the order by and where clauses that unambiguously marks the area till where you gain the result. This is where row-value predicates foster in:SELECT … FROM … WHERE (time_stamp, id) < (?, ?) ORDER BY time_stamp DESC, id DESC FETCH FIRST 10 ROWS ONLY
The order by clause is extended to create positive there is a well-defined order if there are equal time_stamp values. The where clause just selects what’s after the row specified by the time_stamp and id pair. It couldn’t exist any simpler to express this selection criteria. Unfortunately, neither the Oracle database nor SQLite or SQL Server understand this syntax—even though it’s in the SQL yardstick since 1992! However, it is viable to apply the selfsame logic without row-value predicates—but that’s rather inconvenient and light to come by wrong.
Even if a database understands the row-value predicate, it’s not necessarily understanding these predicates superb enough to create proper consume of indexes that champion the order by clause. This is where MySQL fails—although it applies the logic correctly and delivers the privilege result, it does not consume an index for that and is thus rather slow. In the end, DB2 LUW (since 10.1) and PostgreSQL (since 8.4) are the only two databases that champion row-value predicates in the pass it should be.
The fact that DB2 LUW has everything you requisite for convenient keyset pagination is also the judgement why there is absolutely no judgement to complain about the missing offset functionality. In fact I believe that offset should not gain been added to the SQL yardstick and I’m gratified to observe a vendor that resisted the prick to add it because its became section of the standard. Sometimes the yardstick is wrong—just sometimes, not very often ;) I can’t change the standard—all I can achieve is teaching how to achieve it privilege and start campaigns like #NoOffset.
Figure A.2. Database/Feature Matrix
If you like my pass of explaining things, you’ll admire my reserve “SQL Performance Explained”.
Chances are, you gain never heard of Amanda… in the sense of open source that is. And if you gain not heard of Amanda, then chances are you gain not heard of Zmanda either. I will clarify both, and I will give you my view of why it is valuable for you to at least exist cognizant of these products and their relation to data protection. Whether you should invest in either depends on many factors that will become transparent shortly.
Let's start with Amanda. Amanda is the most current open source data protection product in the market today, at least based on the number of free downloads: 250,000 or more. like most free downloads, these usually foster from universities -- both students and IT folks -- and scientific labs. But, they also comprise individuals from corporations that are experimenting with open source. In a nutshell, Amanda is a client/server data protection software that runs on a Linux server (backup server) and protects clients that sprint Windows, Linux or Unix (only a few variants at the moment). It was developed originally at the University of Maryland and then dropped into the world of open source. Since it was distributed to the open source community, hundreds of programmers gain contributed to its development, bug fixes and its universal supervision and feeding. As a result, the usage of the product has continued to climb dramatically over the past few years.You can consume Amanda for free. You can modify it and set aside it back in the ether for free. But, like All open source software, if the software just stopped running in the middle of the night because your client application server was not yet supported, superb luck trying to come by support. Or anything else. Your best bet would exist to area your request on one of many Web sites where users and developers profit each other out.
But, unlike Linux operating systems (where there are companies like RedHat and SUSE, which is now Novell) or Linux-based databases (where there are companies like mySQL), Amanda did not gain a "for profit" sponsor until recently. In late 2005, a newly-formed company was charged with working to create Amanda a more usable product that would exist able to champion enterprises of All sizes. In keeping with the open source model, Zmanda has grabbed leadership of this space and is feverishly encouraging additional programmers -- some internal to the company, but most belonging to other companies/organizations -- to enhance Amanda so it can effectively compete with Symantec NetBackup, EMC Networker, CommVault Galaxy, Tivoli and others that plunge in the enterprise-class data protection software category. Even within the eventual six months, Amanda has foster a long way. But, it also has a long pass to Go before I would esteem it a full member of this class. Should you therefore ignore it? No. However, the judgement I am writing this column is to create you cognizant that, under the privilege set of circumstances, Amanda is worth considering.
Enter Zmanda. The company has released a specific version of Amanda (two versions, actually) that they champion under the classic open source subscription model. You pay only for subscription and champion and not for the product itself, just like any other open source product. Of course, the total thought is to expense it such that the total cost of ownership is significantly (as in one-half to one-fourth the cost) lower than other commercial products.
But before you jump into the fray, exact yourself the following questions:
I am positive that as you Look into these options you will gain other questions that are specific to your organization's needs. Version 2.50 of Zmanda does gain champion for Windows and Linux, but not for All current flavors of Unix. It should champion databases and other applications in the future but does not privilege now. It also lacks a GUI and does not yet champion All the unusual innovations that they gain seen in the world of disk champion (like VTL and CDP). But, it does gain disk support. It also has some features that I wish they had in the other commercial offerings, like a non-proprietary data format and like having the aptitude to achieve a recovery without requiring the vendor's software. Of course, its Linux champion is excellent.
In my view, actual innovation occurs when there is a monetary incentive and there is a discontinuity in the technology curve. That is why they gain seen the massive transformation in data protection software in the past five years. SATA was the technology that opened up opportunities that just were not available before. But, before that, one could create a pretty reasonable controversy that data protection software from All the major vendors had become pretty bloated, and the rate of innovation was very slow. Adding champion for a unusual tape library does not signify as innovation in my book. It is precisely at such times, when differentiation between vendors' products is low, that open source starts to create a lot of sense. Thousands of programmers start developing and creating a simpler, less cumbersome product with adequate functionality for many companies that don't requisite it all. Also, they are cost-sensitive and like the freedom.
That is how mySQL and, of course, Linux itself got going. Now it is Zmanda. But unlike the other segments, data protection is now experiencing phenomenal innovation. So, Amanda's (and therefore, Zmanda's) challenge will exist to not only create the primitive tape-based functionality but also to add All the unusual juicy disk-based functionality that is coming in waves currently. I suspect it is up for the challenge but at least exist cognizant that there could exist a lag before you observe All of these features.
It was bound to happen. If database, J2EE, server virtualization and security tools got an open source counterpart, how far behind could data protection be? If you gain simpler needs, cost is a major issue and you crave that license from the sizable vendor -- for whatever judgement -- then you should check out this unusual space. But my advice: achieve not sprint a production environment without the champion that comes with Zmanda. Amanda may exist free, but she can exist pain without the support.
About the author: Arun Taneja is the founder and consulting analyst for the Taneja Group. Taneja writes columns and answers questions about data management and related topics.
In-DepthIT Skills Poised To Pay
Advances in mobility, cloud, sizable Data, DevOps and digital delivery, plus the shift to more rapid release cycles of software and services, are enabling businesses to become more agile. IT workforce research and analyst firm Foote Partners assesses the IT skills gap these trends are creating, their repercussion on salaries and where the exact for expertise is headed.
It's difficult to find an employer not struggling to foster up with a unique tech staffing model that balances three things: the urgencies of unusual digital innovation strategies, combating ever deepening security threats, and keeping integrated systems and networks running smoothly and efficiently. The staffing challenge has moved well beyond simply having to select between contingent workers, full-time tech professionals, and a variety of cloud computing and managed services options (Infrastructure as a Service [IaaS], Platform as a Service [PaaS], Software as a Server [SaaS]). Over the next few years, managers will continue to exist tasked with leading a massive transformation of the technology and tech-business hybrid workforce to focus on quickly and predictably delivering a wide variety of operational and revenue-generating infrastructure solutions involving Internet of Things (IoT) products and services, sizable Data advanced analytics, cybersecurity, and unusual mobile and cloud computing capabilities. Consequently, tech professionals and developers must align their skills and interests accordingly to profit their employers meet existing and forthcoming digital transformation imperatives that are forcing deep, accelerated changes in technology organizations.
As cloud infrastructure becomes more capable of economically delivering performance and data at capacities and speeds once never imagined, organizations of All sizes are seeking tech professionals and developers with the proper skills, knowledge, and competencies to create more agile and responsive environments.
At the selfsame time, they're grappling to ensure reliability of existing infrastructure where any amount of downtime is less acceptable than ever. Along with that is an onslaught of cybersecurity attacks occurring more frequently that gain many IT managers saying they can't find adequate labor to profit them protect their existing networks and endpoints. The latest reminder was in the spotlight following the most powerful denial of service (DoS) attack to date in late October resulting from unprotected endpoints on surveillance cameras. IoT, machine-to-machine communications and telematics gain introduced unusual complexities ranging from the requisite to better secure the devices and the delivery points to which they connect. Meanwhile, the growing IoT landscape is unleashing an exponential flood of unusual data from hundreds of millions of devices, and organizations requisite to blend their IT and operational systems and find people with sizable Data analytics skills to wield the cloud-based machine learning infrastructure that's now emerging. This generational shift in IT will set aside a premium on, or create a baseline requirement for, IT professionals willing to supervene the money and observe where their skills will exist most applicable. Whether you're a manager looking to ensure your staff can deliver on these changes or an IT professional deciding on a career direction, workforce requirements and customer expectations are changing.
If you're in the latter camp, it's valuable to understand that the supply-and-demand aspect that drives compensation is also a affecting target. IT pay has a long history of volatility and in 2016 they gain seen even sharper swings in those premiums. Based on hiring patterns, the following overriding trends will drive market exact for IT professionals who gain the experience, drive and skills to deliver solutions:
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