Sum microsystem java

8. If we have a function that can be called with theright number of arguments, we don't need to write an anonymous function at all; we can just pass our function by name.In this example, there is some function that adds two integers together, and it's calledInteger::sum. Let's swap it in forour anonymous function: reduce(Integer::sum, 0, list1)reduce(Integer::sum, 0, list1)$62 ==> 6">jshell> reduce(Integer::sum, 0, list1)reduce(Integer::sum, 0, list1)$62 ==> 6Very nice!Wagging our tailOne really cool thing about reduce is that it's a so-called tail-recursive function, meaning that it returns thereturn value of the recursive call directly (or the base case), without needing to fiddle with it. Compare the recursivecall in reduce:return reduce(f, f.apply(acc, first(xs)), rest(xs))to the recursive calls in map and filter:// mapreturn cons(f.apply(first(xs)), map(f, rest(xs)))// filterreturn p.apply(x) ? cons(x, filter(p, rest(xs))) : filter(p, rest(xs))You see how both of them return the result of cons-ing something onto the value returned by the recursive call (in atleast one branch of filter)? That means that the function needs to hold onto the first item of xs in the stack inorder to cons it onto the return value of the recursive call.It turns out this doesn't matter in Java, as Java lacks something calledtail call optimisation (TCO), wherein the compiler translates a tail callinto standard for loop-style iteration. It's pretty neat, but the Java compiler can't do it (yet) because,according to Java architect Brian Goetz,in JDK classes [...] there are a number of security sensitive methods that rely on counting stack frames between JDKlibrary code and calling code to figure out who's calling them.This would break if anything changes the number of frames on the stack (such as eliminating frames on recursive callsthrough tail call optimisation). The good news is that apparently the JDK no longer relies on counting stack frames forthese methods, so TCO will eventually happen, though it's sadly not a priority.And why should we care about TCO? Well, in addition to making code run faster by eliminating a bunch of function calls,TCO lets us call recursive functions on really big lists, which would otherwise cause the JVM to bail with aStackOverflowException when it consumes the maximum allowable number of stack frames, which is limited by the amountof memory the JVM has been given for the stack (which can be controlled by the -Xss command-line argument whenstarting the JVM). Before you ask why not just give the JVM a huge amount of memory for the stack, let's understand thatthe StackOverflowException is actually our friend, as it prevents infinite recursion caused by buggy code fromconsuming all available memory on our machine, causing Linux's out of memory killer (or the equivalent on otheroperating systems) from killing potentially random programs on our machine until things start swapping and then ourmachine slows to a crawl and we can't move our mouse over to the IDE window in order to fix the bug. What a drag!We have taken this brief detour for a reason, which shall now be revealed.Better (but more confusing) map and filterTo prepare ourselves for

Connection. Class.forName("com.microsoft.sqlserver.jdbc.SQLServerDriver"); con = DriverManager.getConnection(connectionUrl); // Create and execute an SQL statement that returns some data. String SQL = "SELECT Country , SUM(UnitPrice * Quantity) Total " + "FROM value " + "GROUP BY Country " + "WITH (SRC=' stmt = con.createStatement(); rs = stmt.executeQuery(SQL); // Iterate through the data in the result set and display it. while (rs.next()) { System.out.println(rs.getString(1) + " " + rs.getString(2)); } } // Handle any errors that may have occurred. catch (Exception e) { e.printStackTrace(); } finally { if (rs != null) try { rs.close(); } catch (Exception e) {} if (stmt != null) try { stmt.close(); } catch (Exception e) {} if (con != null) try { con.close(); } catch (Exception e) {} } }} Conclusion In this article we discussed how to connect to JDBC-ODBC Bridge in JAVA and integrate data without any coding. Click here to Download JDBC-ODBC Bridge Connector for JAVA and try yourself see how easy it is. If you still have any question(s) then ask here or simply click on live chat icon below and ask our expert (see bottom-right corner of this page).More integrationsOther application integration scenarios for JDBC-ODBC BridgeOther connectors for JAVA Download JDBC-ODBC Bridge Connector for JAVA Documentation Common Searches: How to connect JDBC-ODBC Bridge in JAVA? How to get JDBC-ODBC Bridge data in JAVA? How to read JDBC-ODBC Bridge data in JAVA? How to load JDBC-ODBC Bridge data in JAVA? How to import JDBC-ODBC Bridge data in JAVA? How to pull JDBC-ODBC Bridge data

Area as well as by product, which means that there will be multiple data fetching actions (result set function) at the same time, then the delayed cursor mechanism will not work. In this case, do we have to create another cursor to traverse again?No, SPL provides the multipurpose traversal mechanism, enabling us to accomplish multiple types of calculations in one traversal by creating a synchronous channel on the cursor. For example:AB1=file(“orders.txt”).cursor@t(product,area,amount)2cursor A1=A2.groups(area;max(amount))3cursor=A3.groups(product;sum(amount))4cursor=A4.select(amount>=50).total(count(1))Having created the cursor, use the cursor statement to create a channel on it, and attach operations on the channel. We can create multiple channels, if the cursor parameter is not written in the subsequent statements, it indicates the same cursor will be used.With the help of the mechanisms such as cursor, delayed cursor, and multipurpose traversal (channel), SPL can easily handle big data computing. Currently, SPL provides a variety of external storage computing functions and can meet almost all big data computing requirements, which makes SPL far superior to Java and Python.Parallel computingFor big data computing, the performance is critical. We know that parallel computing can effectively improve computing efficiency. Besides the delayed cursor and multipurpose traversal that can guarantee the performance to a certain extent, SPL provides a multi-thread parallel processing mechanism to speed up calculation. Likewise, this mechanism is easy to use.AB1=file(“orders.txt”)2fork to(4)=A1.cursor@t(area,amount;A2:4)3return B2.groups(area;sum(amount):amount)4=A2.conj().groups(area;sum(amount))The fork statement will start multiple threads to execute their own code blocks in parallel, and the number of threads is determined by the parameter following the fork. Moreover, the fork statement will assign these parameters to each thread in turn. When all threads are executed, the calculation result of each thread will be collected and concatenated for further operation.Compared with Java and Python, SPL is much more convenient while using fork to start multiple threads for parallel computing. However, such SPL code is still a bit cumbersome, especially for counting the data of common single table and, attention should also be given that it may need to change the function (from count to sum) when re-aggregating the results returned from threads. To solve this problem, SPL provides a simpler syntax: multi-cursor, which can directly generate parallel cursors.A1=file(“orders.txt”)2=A1.cursor@tm(area,amount;4)3=A2.groups(area;sum(amount):amount)Using the @m option can create parallel multi-cursor, and the subsequent usage is the same as that of single cursor, and SPL will automatically execute parallel computing and re-aggregate the results.Multipurpose traversal can also be implemented on multi-cursor.AB1=file("orders.txt").cursor@tm(area,amount;4)2cursor A1=A2.groups(area;sum(amount):amount)3cursor=A3.groups(product;sum(amount):amount)By means of simple and easy-to-use parallel computing,

AA side chain length)listXL a list of experimentally determined cross-linked lysine residues.Remove all non-protein atoms in xyz.pdb and assign protein atoms a van der Waals radius sum of SURFNET atom radii + solvent radiusSelect a random lysine pair (Ki,Kj) from listXL,Check Euclidean distance (Euc) of (Ki,Kj). Continue, if Euc > maxdist, disregard otherwise and go back to 3.Generate a grid of size maxdist and grid spacing 1 Angstroem centered at AAaSet Integer.MAX_VALUE as distance for all grid cells and label grid cells as residing in theproteinsolventboundary between protein and solvent.Label grid cells residing in (Ki,Kj) as solventSet distance dist = 0.0 for central grid cell of Ki and store grid cell in the active list listactiveStart breadth-first search. Iterate through listactiveCheck that grid cell i is labeled as solventFind all immediate neighbors listneighbourIterate through listneighbour1. Check that grid cell j is labeled as solvent2. Compute new distance for grid cell j as the sum of the distance in grid cell i and the Euclidean distance between grid cell i and j3. If distance sum in 9.3.2 is smaller than the current distance in grid cell j, store the distance sum as new distance for grid cell j and add grid cell j to the new active list listnew_active,Go back to step 9. with listactive = listnew_active.#NOTESAs the SAS distance is based on a grid calculation, the default heap size ofthe Java VM with 64MB is likely to be too small. You can increase the heapsize with java -Xmx512mYou can obtain PyMOL here for free.Beware that in order for PyMOL to recognize the script file, the file musthave the name ending .pml.You can load the script directly at the startup of PyMOL, i.e. with thecommand#CONTACTabdullah.kahraman@uzh.ch#LICENCEXwalk executable and libraries are available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License via the Xwalk website.Anyone is free:to copy, modify, distribute the software;Under the following conditions:the original authors must be given credit by citing the original Xwalk paper:Kahraman A., Malmström L., Aebersold R. (2011). Xwalk: Computing andVisualizing Distances in Cross-linking Experiments. Bioinformatics,doi:10.1093/bioinformatics/btr348.the software or derivate works must not be used for commercial purposesderivate works must

T || LENGTH(S) | Returns length of string || LOWER(S) | Converts string to lower case || LTRIM(S [, T]) | Removes leading characters from S that occur in T || MINUTE(T) | Extracts minute of hour from time or timestamp T || MONTH(D) | Extracts month from date or timestamp D (first month is 1) || NULLIF(X, Y) | Returns NULL if X and Y are equal, otherwise X || ROUND(N) | Rounds N to nearest whole number || RTRIM(S, [, T]) | Removes trailing characters from S that occur in T || SECOND(T) | Extracts seconds value from time or timestamp T || SUBSTRING(S, N [, L]) | Extracts substring from S starting at index N (counting from 1) with length L || TRIM(S, [, T]) | Removes leading and trailing characters from S that occur in T || UPPER(S) | Converts string to upper case || YEAR(D) | Extracts year from date or timestamp D |Additional functions are defined from java methods using the function.NAME driver property.| Aggregate Function | Description ||-----------------------|-------------|| AVG(N) | Average of all values|| COUNT(N) | Count of all values|| MAX(N) | Maximum value|| MIN(N) | Minimum value|| SUM(N) | Sum of all values|For queries containing ORDER BY, all records are read into memory and sorted.For queries containing GROUP BY plus an aggregate function, all records are readinto memory and grouped. For queries that produce a scrollable result set, allrecords up to the furthest accessed record are held into memory. For other queries,this driver holds only one record at a time in memory.Driver PropertiesThe driver also supports a number of parameters that change the default behaviour of the driver.These properties arecharsettype: Stringdefault: Java defaultDefines the character set name of the files being read, such as UTF-16. See the Java Charset documentation for a

U| (actual and formal argument lists differ in length))| prices.stream().filter(price -> VAT.rate(price.currency()) > 0).map(Price::amount).reduce(0, Long::sum)| ^---------------------------------------------------------------------------------------^">prices.stream().filter(Price::hasVAT).map(Price::amount).reduce(0, Long::sum)| Error:| no suitable method found for reduce(int,Long::sum)| method java.util.stream.Stream.reduce(java.lang.Long,java.util.function.BinaryOperator) is not applicable| (argument mismatch; int cannot be converted to java.lang.Long)| method java.util.stream.Stream.reduce(U,java.util.function.BiFunction,java.util.function.BinaryOperator) is not applicable| (cannot infer type-variable(s) U| (actual and formal argument lists differ in length))| prices.stream().filter(price -> VAT.rate(price.currency()) > 0).map(Price::amount).reduce(0, Long::sum)| ^---------------------------------------------------------------------------------------^It's also annoying that we even need map here. We could use our version of reduce like this: acc + price.amount(), 0, prices)">reduce((Long acc, Price price) -> acc + price.amount(), 0, prices)But Stream.reduce requires the reducing function to be aBinaryOperator,which takes two arguments of type T, meaning that we first need to map over our filtered prices to get their amount,and then sum them up.Surely we can do better, right?Indeed we can!Enter collectorsStreams have another cool terminal operation called collect. collect basically combines map and reduce, andthe Java standard library has loads of built-inCollectors that we canuse for common operations. For example, to solve the problem at hand:prices.stream() .filter(Price::hasVAT) .collect(Collectors.summingLong(Price::amount)Let's try it out: prices.stream().filter(Price::hasVAT).collect(Collectors.summingLong(Price::amount))prices.stream().filter(Price::hasVAT).collect(Collectors.summingLong(Price::amount))$100 ==> 11000">jshell> prices.stream().filter(Price::hasVAT).collect(Collectors.summingLong(Price::amount))prices.stream().filter(Price::hasVAT).collect(Collectors.summingLong(Price::amount))$100 ==> 11000Collectors can be combined in cool ways as well. In fact, there is one thing about our "solution" to this problem thathas been setting off the old Spidey Sense: we're summing up prices of different currencies, which sounds like a recipefor disaster. Let's fix this by summing up prices of the same currency:var prices = Arrays.asList( new Price(10000, "SEK"), new Price(1000, "EUR"), new Price(1000, "USD"), new Price(15000, "SEK"), new Price(32000, "SEK"), new Price(20000, "EUR"))prices.stream() .filter(Price::hasVAT) .collect(Collectors.groupingBy(Price::currency, Collectors.summingLong(Price::amount))Let's see what this does: prices.stream().filter(Price::hasVAT).collect(Collectors.groupingBy(Price::currency, Collectors.summingLong(Price::amount)))prices.stream().filter(Price::hasVAT).collect(Collectors.groupingBy(Price::currency, Collectors.summingLong(Price::amount)))$108 ==> {EUR=21000, SEK=57000}">jshell> prices.stream().filter(Price::hasVAT).collect(Collectors.groupingBy(Price::currency, Collectors.summingLong(Price::amount)))prices.stream().filter(Price::hasVAT).collect(Collectors.groupingBy(Price::currency, Collectors.summingLong(Price::amount)))$108 ==> {EUR=21000, SEK=57000}OK, that is super cool! And we accomplished it in one compact expression, using higher order functions, operatingon immutable data.My friends, we have just done some real functional programming in Java!