Optimization is a very common problem in data analytics. Given a set of variables (which one has control), how to pick the right value such that the benefit is maximized. More formally, optimization is about determining a set of variables x1, x2, … that maximize or minimize an objective function f(x1, x2, …). Unconstrained optimization In an unconstraint case, the variable can be freely selected within its full range. A typical solution is to compute the gradient vector of the objective function [∂f/∂x1, ∂f/∂x2, …] and set it to [0, 0, …]. Solve this equation and output the result x1, x2 … which will give the local maximum. In R, this can be done by a numerical analysis method. > f <- function(x){(x[1] - 5)^2 + (x[2] - 6)^2} > initial_x <- c(10, 11) > x_optimal <- optim(initial_x, f, method="CG") > x_min <- x_optimal$par > x_min [1] 5 6 Equality constraint optimization Moving onto the constrained case, lets say x1, x2 … are not independent and then have to related to each other in some particular way: g1(x1, x2, …) = 0, g2(x1, x2, …) = 0. The optimization problem can be expressed as … Maximize objective function: f(x1, x2, …) Subjected to equality constraints: g1(x1, x2, …) = 0 g2(x1, x2, …) = 0 A typical solution is to turn the constraint optimization problem into an unconstrained optimization problem using Lagrange multipliers. Define a new function F as follows ... F(x1, x2, …, λ1, λ2, …) = f(x1, x2, …) + λ1.g1(x1, x2, …) + λ2.g2(x1, x2, …) + … Then solve for ... [∂F/∂x1, ∂F/∂x2, …, ∂F/∂λ1, ∂F/∂λ2, …] = [0, 0, ….] Inequality constraint optimization In this case, the constraint is inequality. We cannot use the Lagrange multiplier technique because it requires equality constraint. There is no general solution for arbitrary inequality constraints. However, we can put some restriction in the form of constraint. In the following, we study two models where constraint is restricted to be a linear function of the variables: w1.x1 + w2.x2 + … >= 0 Linear Programming Linear programming is a model where both the objective function and constraint function is restricted as linear combination of variables. The Linear Programming problem can be defined as follows ... Maximize objective function: f(x1, x2) = c1.x1 + c2.x2 Subjected to inequality constraints: a11.x1 + a12.x2 <= b1 a21.x1 + a22.x2 <= b2 a31.x1 + a32.x2 <= b3 x1 >= 0, x2 >=0 As an illustrative example, lets walkthrough a portfolio investment problem. In the example, we want to find an optimal way to allocate the proportion of asset in our investment portfolio. StockA gives 5% return on average StockB gives 4% return on average StockC gives 6% return on average To set some constraints, lets say my investment in C must be less than sum of A, B. Also, investment in A cannot be more than twice of B. Finally, at least 10% of investment in each stock. To formulate this problem: Variable: x1 = % investment in A, x2 = % in B, x3 = % in C Maximize expected return: f(x1, x2, x3) = x1*5% + x2*4% + x3*6% Subjected to constraints: 10% < x1, x2, x3 < 100% x1 + x2 + x3 = 1 x3 < x1 + x2 x1 < 2 * x2 > library(lpSolve) > library(lpSolveAPI) > # Set the number of vars > model <- make.lp(0, 3) > # Define the object function: for Minimize, use -ve > set.objfn(model, c(-0.05, -0.04, -0.06)) > # Add the constraints > add.constraint(model, c(1, 1, 1), "=", 1) > add.constraint(model, c(1, 1, -1), ">", 0) > add.constraint(model, c(1, -2, 0), "<", 0) > # Set the upper and lower bounds > set.bounds(model, lower=c(0.1, 0.1, 0.1), upper=c(1, 1, 1)) > # Compute the optimized model > solve(model) [1] 0 > # Get the value of the optimized parameters > get.variables(model) [1] 0.3333333 0.1666667 0.5000000 > # Get the value of the objective function > get.objective(model) [1] -0.05333333 > # Get the value of the constraint > get.constraints(model) [1] 1 0 0 Quadratic Programming Quadratic programming is a model where both the objective function is a quadratic function (contains up to two term products) while constraint function is restricted as linear combination of variables. The Quadratic Programming problem can be defined as follows ... Minimize quadratic objective function: f(x1, x2) = c1.x12 + c2. x1x2 + c2.x22 - (d1. x1 + d2.x2) Subject to constraints a11.x1 + a12.x2 == b1 a21.x1 + a22.x2 == b2 a31.x1 + a32.x2 >= b3 a41.x1 + a42.x2 >= b4 a51.x1 + a52.x2 >= b5 Express the problem in Matrix form: Minimize objective ½(DTx) - dTx Subject to constraint ATx >= b First k columns of A is equality constraint As an illustrative example, lets continue on the portfolio investment problem. In the example, we want to find an optimal way to allocate the proportion of asset in our investment portfolio. StockA gives 5% return on average StockB gives 4% return on average StockC gives 6% return on average We also look into the variance of each stock (known as risk) as well as the covariance between stocks. For investment, we not only want to have a high expected return, but also a low variance. In other words, stocks with high return but also high variance is not very attractive. Therefore, maximize the expected return and minimize the variance is the typical investment strategy. One way to minimize variance is to pick multiple stocks (in a portfolio) to diversify the investment. Diversification happens when the stock components within the portfolio moves from their average in different direction (hence the variance is reduced). The Covariance matrix ∑ (between each pairs of stocks) is given as follows: 1%, 0.2%, 0.5% 0.2%, 0.8%, 0.6% 0.5%, 0.6%, 1.2% In this example, we want to achieve a overall return of at least 5.2% of return while minimizing the variance of return To formulate the problem: Variable: x1 = % investment in A, x2 = % in B, x3 = % in C Minimize variance: xT∑x Subjected to constraint: x1 + x2 + x3 == 1 X1*5% + x2*4% + x3*6% >= 5.2% 0% < x1, x2, x3 < 100% > library(quadprog) > mu_return_vector <- c(0.05, 0.04, 0.06) > sigma <- matrix(c(0.01, 0.002, 0.005, + 0.002, 0.008, 0.006, + 0.005, 0.006, 0.012), + nrow=3, ncol=3) > D.Matrix <- 2*sigma > d.Vector <- rep(0, 3) > A.Equality <- matrix(c(1,1,1), ncol=1) > A.Matrix <- cbind(A.Equality, mu_return_vector, diag(3)) > b.Vector <- c(1, 0.052, rep(0, 3)) > out <- solve.QP(Dmat=D.Matrix, dvec=d.Vector, Amat=A.Matrix, bvec=b.Vector, meq=1) > out$solution [1] 0.4 0.2 0.4 > out$value [1] 0.00672 > Integration with Predictive Analytics Optimization is usually integrated with predictive analytics, which is another important part of data analytics. For an overview of predictive analytics, here is my previous blog about it. The overall processing can be depicted as follows: In this diagram, we use machine learning to train a predictive model in batch mode. Once the predictive model is available, observation data is fed into it at real time and a set of output variables is predicted. Such output variable will be fed into the optimization model as the environment parameters (e.g. return of each stock, covariance ... etc.) from which a set of optimal decision variable is recommended.

for those that are not familiar with sonar , ( i hope this post will make you at least try it or see it in action at http://nemo.sonarsource.org ) you can take a look at an earlier post i’ve written some time ago. in one sentence sonar is an open source platform that allows you to track and improve the quality of your source code. one of the key aspects when talking about software quality is the test coverage or code coverage which is how much of your source code is tested by unit tests. sonar integrates with the most popular open source code coverage tools ( jacoco , cobetura , emma ) and the well-known commercial clover by attlassian. by default it uses the jacoco (java code coverage) engine and you’ll shortly find out why before we move on, i’d like to give many kudos to evgeny mandrikov . this article is inspired by one of his older post s and its intention is to present a more updated comparison of the supported code coverage tools by sonar and point out some differences regarding their results and the way they work. recently sonar changed its default code coverage tool to jacoco and this post tries to explain the reasons behind that decision. some of the information is borrowed by evgeny’s post and the image is also taken from evgeny’s presentation about jacoco . so thanks a lot evgeny! now let’s go to the meat. for the comparison you’ll see, i’ve used the latest available sonar version 3.3, maven 2.2.1, java 1.6 and all analysis launched in a windows 7 machine (intel core i3-2120 cpu @ 3.30ghz) with 8gb ram. the projects were carefully selected ( a small, medium-sized and a large one – not that large as java code base but large enough to extract some results ). i ran five analysis for each open source code coverage tool ( i excluded the commercial clover from my comparison version ) and another five by disabling the code coverage mechanism. so that’s a total of 60 analysis ). in the following tables you can find some information about the code coverage tools and some basic metrics about the selected projects. pay attention to the date of the latest stable release. emma hasn’t been updated since dinosaurs era and cobertura is almost three years inactive. one might think that this isn’t an issue if they are stable and don’t need any new release. well, the truth is that both of them have bugs that frustrate end-users and there’s no one to fix them. on the other hand jacoco is continuously evolving and improving… the results of the analysis are displayed next. some important notices. emma doesn’t support branch coverage that’s why you’re not seeing any metrics. furthermore there are differences in the results of line and branch coverage, which are more concrete for larger projects. for instance in sonar jira plugin all three tools produce the same results whereas in sonar analysis and commons lang projects you can see that the numbers are not the same. now take a look at a graph that illustrates in a more readable way which tool is the fastest. it seems that emma and jacoco need the same amount of time to compute their metrics… but… as we already mentioned there’s a huge difference. there’s no branch coverage in emma reports. cobertura is always slower than jacoco so again the winner is jacoco. of course you can get even faster results by running a sonar analysis without computing code coverage metrics one last thing: jacoco, as the following figure shows is the only tool that analyses bytecode on-the-fly which is more . cobertura and emma run an offline analysis and use a class loader whereas jacoco has its own java agent for analysis code. this configuration allows jacoco to be very flexible, possible integrated with many other tools and frameworks and can be used with any language in a jvm environment. so, to sum up, if you’re using sonar ( if you don’t , you should ), then it strongly advisable to keep the default code coverage engine ( jacoco) , unless you have really important reasons for that. finally don’t forge to check sonar’s community 2013 unofficial survey and the upcoming book about sonar by manning publications. the release date is in about 3-4 months but you can get an early access version here . as always, feel free to comment or suggest improvements about the article and its content.

ENUM is a new enumerated data type introduced in CUBRID 9.0. Like in all programming languages, the ENUM type is a data type composed of a set of static, ordered values. Users can define numeric and string values for ENUM columns. Working with ENUM types Creating an ENUM column is done by specifying a static list of possible values: CREATE TABLE person( name VARCHAR(255), gender ENUM('Male', 'Female') ); CUBRID understands the ENUM type as an ordered set of constants which, in the above example, is a set of {NULL: NULL, 1: 'Male', 2: 'Female”}. To assign a value to the gender column, users may either use the index of the value ({NULL, 1, 2}) or the actual constant literal ({NULL}, {'Male'}, {'Female'}). CUBRID restricts the values that can be assigned to this column to only values from this set + NULL. Moreover, ENUM column is case-sensitive, i.e. it will raise an error if you try to enter 'female' in lower case. Also, an empty string is allowed if it is defined as one of the elements of the ENUM column. In our examples, it is not allowed. csql> INSERT INTO person(name, gender) VALUES('Eugene', 'Male'); 1 row affected. 1 command(s) successfully processed. csql> INSERT INTO person(name, gender) VALUES('Anne', 2); 1 row affected. 1 command(s) successfully processed. csql> SELECT * FROM person; === === name gender ============================================ 'Anne' 'Female' 'Eugene' 'Male' 2 rows selected. Any attempt to insert a value outside of the defined set will result in a coercion error. In the below case, trying to insert an empty string raises an error because it is not in the set of allowed values defined in the person table. csql> INSERT INTO person(name, gender) VALUES('John', 'N/A'); IN line 1, COLUMN 44, ERROR: before ' ); ' Cannot coerce 'N/A' TO type enum. 0 command(s) successfully processed. csql> INSERT INTO person(name, gender) VALUES('John', 4); IN line 1, COLUMN 45, ERROR: before ' ); ' Cannot coerce 4 TO type enum. 0 command(s) successfully processed. csql> INSERT INTO person(name, gender) VALUES('John', ''); IN line 1, COLUMN 44, ERROR: before ' ); ' Cannot coerce '' TO type enum. 0 command(s) successfully processed. Why you should use the ENUM type There are three important reasons for which you should consider using the ENUM type: Reduce storage space. Reduce join complexity. Create cheap values constraints. Storage Space CUBRID uses only 1 byte per tuple when 255 or less ENUM elements are defined or 2 bytes for 256~65535 elements. This is because, rather that storing the constant literal of the value, CUBRID stores the index in the ordered set of that value. For very large tables, this might prove to be a significant storage space save. Take, for example, a table with 1,000,000,000 records which has an ENUM column defined as ('Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'). If you use a VARCHAR type instead of the ENUM type to store these values, the column would require anywhere between 5GB and 9GB of storage space. Using the ENUM type, you can reduce the required space to 2 bytes per tuple, adding up to a total of 2GB. Reduce join complexity JOIN way The same effect of the ENUM type can be achieved by creating a one to many relationship on two or more tables. Considering the example above, you can store values for days of the week like this: CREATE TABLE days_of_week( id SHORT PRIMARY KEY, name VARCHAR(9) ); CREATE TABLE opening_hours( week_day SHORT, opening_time TIME, closing_time TIME, FOREIGN KEY fk_dow (week_day) REFERENCES days_of_week(id) ); Then, when you wish to display the name of the week day, you would execute a query like: SELECT d.name day_name, o.opening_time, o.closing_time FROM days_of_week d, opening_hours o WHERE d.id = o.week_day ORDER BY d.id; === === day_name opening_time closing_time ================================================== 'Monday' 09:00:00 AM 06:00:00 PM 'Tuesday' 09:00:00 AM 06:00:00 PM 'Wednesday' 09:00:00 AM 06:00:00 PM ... ENUM way You can achieve the same effect using an ENUM column: CREATE TABLE opening_hours( week_day ENUM ('Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'), opening_time TIME, closing_time TIME ); And there’s no JOIN required to select opening hours: SELECT week_day, opening_time, closing_time FROM opening_hours ORDER BY week_day; === === week_day opening_time closing_time ================================================== 'Monday' 09:00:00 AM 06:00:00 PM 'Tuesday' 09:00:00 AM 06:00:00 PM 'Wednesday' 09:00:00 AM 06:00:00 PM ... This can prove to be very useful, especially if your queries join several tables. Value constraints ENUM columns behave like foreign key relationships in the sense that values from an ENUM column are restricted to the values specified in the column definition. For a short list of values, this is more efficient than creating foreign key relationships. While foreign key relationships use index scans to enforce the restriction, ENUM columns just have to go through a list of predefined values which is faster even for small indexes. Why/When you should NOT use the ENUM type Even though ENUM is a great feature, there are cases when you’d better not use it. For example: When ENUM type is not fixed When ENUM type has a long list of values When your application does not know the list of ENUM values ENUM type is not reusable Portability is a concern When ENUM type is not fixed If you’re not sure if the ENUM type holds all possible values for that column, you should consider using a one to many relationship instead. The only way in which an ENUM column can be changed to handle more values is by using an ALTER statement. This is a very expensive operation in any RDBMS and requires administrator rights. Also, ALTER statements are maintenance operations and should, as much as possible, be performed offline. When ENUM type has a long list of values ENUM types should not be used if you cannot limit a set of possible values to a few elements. When your application does not know the list of ENUM values There are only two ways of getting a list of values you have defined for an ENUM type: parsing the output of SHOW CREATE TABLE statement: csql> SHOW CREATE TABLE opening_hours; === === TABLE CREATE TABLE ============================================ 'opening_hours' 'CREATE TABLE [opening_hours] ([week_day] ENUM('Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'), [opening_time] TIME, [closing_time] TIME) selecting information from CUBRID system tables: csql> SELECT d.enumeration FROM _db_domain d, _db_attribute a WHERE a.attr_name = 'week_day' AND a.class_of.class_name = 'opening_hours' AND d IN a.domains; === === enumeration ====================== {'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'} Both might require complex coding and selecting from system tables requires administrator privileges. ENUM type is not reusable If you have several tables which require the names of week days, you will have to create an ENUM type for each of them. If you create a table to hold week days names, you can join this table with whichever other table that requires this information. Portability is a concern The ENUM type is only supported by a few RDBMSs and each one has its own idea as to how ENUM type is supposed to work. Below is a list of a few notable differences between CUBRID, MySQL and PostgreSQL: CUBRID PostgreSQL MySQL Inserting out of range value Throws error Throws error Inserts special value index 0 Comparing to char literals Compare as strings Compare as ENUM elements Compare as strings Comparing to values outside of the ENUM domain Compare as strings Throws error Compare as strings These subtle differences will most probably break your application in interesting and hard to understand ways. If you’re migrating from PostgreSQL to CUBRID for example, and you expect comparisons with char literals to be performed as ENUM comparisons, you’ll have a hard time understanding why your query returns weird results.

This is the second article in the series of "Become a Java GC Expert". In the first issue Understanding Java Garbage Collection we have learned about the processes for different GC algorithms, about how GC works, what Young and Old Generation is, what you should know about the 5 types of GC in the new JDK 7, and what the performance implications are for each of these GC types. In this article, I will explain how JVM is actually running Garbage Collection in the real time. What is GC Monitoring? Garbage Collection Monitoring refers to the process of figuring out how JVM is running GC. For example, we can find out: when an object in young has moved to old and by how much, or when stop-the-world has occurred and for how long. GC monitoring is carried out to see if JVM is running GC efficiently, and to check if additional GC tuning is necessary. Based on this information, the application can be edited or GC method can be changed (GC tuning). How to Monitor GC? There are different ways to monitor GC, but the only difference is how the GC operation information is shown. GC is done by JVM, and since the GC monitoring tools disclose the GC information provided by JVM, you will get the same results no matter how you monitor GC. Therefore, you do not need to learn all methods to monitor GC, but since it only requires a little amount of time to learn each GC monitoring method, knowing a few of them can help you use the right one for different situations and environments. The tools or JVM options listed below cannot be used universally regardless of the HVM vendor. This is because there is no need for a "standard" for disclosing GC information. In this example we will use HotSpot JVM (Oracle JVM). Since NHN is using Oracle (Sun) JVM, there should be no difficulties in applying the tools or JVM options that we are explaining here. First, the GC monitoring methods can be separated into CUI and GUI depending on the access interface. The typical CUI GC monitoring method involves using a separate CUI application called "jstat", or selecting a JVM option called "verbosegc" when running JVM. GUI GC monitoring is done by using a separate GUI application, and three most commonly used applications would be "jconsole", "jvisualvm" and "Visual GC". Let's learn more about each method. jstat jstat is a monitoring tool in HotSpot JVM. Other monitoring tools for HotSpot JVM are jps and jstatd. Sometimes, you need all three tools to monitor a Java application. jstat does not provide only the GC operation information display. It also provides class loader operation information or Just-in-Time compiler operation information. Among all the information jstat can provide, in this article we will only cover its functionality to monitor GC operating information. jstat is located in $JDK_HOME/bin, so if java or javac can run without setting a separate directory from the command line, so can jstat. You can try running the following in the command line. $> jstat –gc $ 1000 S0C S1C S0U S1U EC EU OC OU PC PU YGC YGCT FGC FGCT GCT 3008.0 3072.0 0.0 1511.1 343360.0 46383.0 699072.0 283690.2 75392.0 41064.3 2540 18.454 4 1.133 19.588 3008.0 3072.0 0.0 1511.1 343360.0 47530.9 699072.0 283690.2 75392.0 41064.3 2540 18.454 4 1.133 19.588 3008.0 3072.0 0.0 1511.1 343360.0 47793.0 699072.0 283690.2 75392.0 41064.3 2540 18.454 4 1.133 19.588 $> Just like in the example, the real type data will be output along with the following columns: S0C S1C S0U S1U EC EU OC OU PC. vmid (Virtual Machine ID), as its name implies, is the ID for the VM. Java applications running either on a local machine or on a remote machine can be specified using vmid. The vmid for Java application running on a local machine is called lvmid (Local vmid), and usually is PID. To find out the lvmid, you can write the PID value using a ps command or Windows task manager, but we suggest jps because PID and lvmid does not always match. jps stands for Java PS. jps shows vmids and main method information. Just like ps shows PIDs and process names. Find out the vmid of the Java application that you want to monitor by using jps, then use it as a parameter in jstat. If you use jps alone, only bootstrap information will show when several WAS instances are running in one equipment. We suggest that you use ps -ef | grep java command along with jps. GC performance data needs constant observation, therefore when running jstat, try to output the GC monitoring information on a regular basis. For example, running "jstat –gc 1000" (or 1s) will display the GC monitoring data on the console every 1 second. "jstat –gc 1000 10" will display the GC monitoring information once every 1 second for 10 times in total. There are many options other than -gc, among which GC related ones are listed below. Option Name Description gc It shows the current size for each heap area and its current usage (Ede, survivor, old, etc.), total number of GC performed, and the accumulated time for GC operations. gccapactiy It shows the minimum size (ms) and maximum size (mx) of each heap area, current size, and the number of GC performed for each area. (Does not show current usage and accumulated time for GC operations.) gccause It shows the "information provided by -gcutil" + reason for the last GC and the reason for the current GC. gcnew Shows the GC performance data for the new area. gcnewcapacity Shows statistics for the size of new area. gcold Shows the GC performance data for the old area. gcoldcapacity Shows statistics for the size of old area. gcpermcapacity Shows statistics for the permanent area. gcutil Shows the usage for each heap area in percentage. Also shows the total number of GC performed and the accumulated time for GC operations. Only looking at frequency, you will probably use -gcutil (or -gccause), -gc and -gccapacity the most in that order. -gcutil is used to check the usage of heap areas, the number of GC performed, and the total accumulated time for GC operations, while -gccapacity option and others can be used to check the actual size allocated. You can see the following output by using the -gc option: S0C S1C … GCT 1248.0 896.0 … 1.246 1248.0 896.0 … 1.246 … … … … Different jstat options show different types of columns, which are listed below. Each column information will be displayed when you use the "jstat option" listed on the right. Column Description Jstat Option S0C Displays the current size of Survivor0 area in KB -gc -gccapacity -gcnew -gcnewcapacity S1C Displays the current size of Survivor1 area in KB -gc -gccapacity -gcnew -gcnewcapacity S0U Displays the current usage of Survivor0 area in KB -gc -gcnew S1U Displays the current usage of Survivor1 area in KB -gc -gcnew EC Displays the current size of Eden area in KB -gc -gccapacity -gcnew -gcnewcapacity EU Displays the current usage of Eden area in KB -gc -gcnew OC Displays the current size of old area in KB -gc -gccapacity -gcold -gcoldcapacity OU Displays the current usage of old area in KB -gc -gcold PC Displays the current size of permanent area in KB -gc -gccapacity -gcold -gcoldcapacity -gcpermcapacity PU Displays the current usage of permanent area in KB -gc -gcold YGC The number of GC event occurred in young area -gc -gccapacity -gcnew -gcnewcapacity -gcold -gcoldcapacity -gcpermcapacity -gcutil -gccause YGCT The accumulated time for GC operations for Yong area -gc -gcnew -gcutil -gccause FGC The number of full GC event occurred -gc -gccapacity -gcnew -gcnewcapacity -gcold -gcoldcapacity -gcpermcapacity -gcutil -gccause FGCT The accumulated time for full GC operations -gc -gcold -gcoldcapacity -gcpermcapacity -gcutil -gccause GCT The total accumulated time for GC operations -gc -gcold -gcoldcapacity -gcpermcapacity -gcutil -gccause NGCMN The minimum size of new area in KB -gccapacity -gcnewcapacity NGCMX The maximum size of max area in KB -gccapacity -gcnewcapacity NGC The current size of new area in KB -gccapacity -gcnewcapacity OGCMN The minimum size of old area in KB -gccapacity -gcoldcapacity OGCMX The maximum size of old area in KB -gccapacity -gcoldcapacity OGC The current size of old area in KB -gccapacity -gcoldcapacity PGCMN The minimum size of permanent area in KB -gccapacity -gcpermcapacity PGCMX The maximum size of permanent area in KB -gccapacity -gcpermcapacity PGC The current size of permanent generation area in KB -gccapacity -gcpermcapacity PC The current size of permanent area in KB -gccapacity -gcpermcapacity PU The current usage of permanent area in KB -gc -gcold LGCC The cause for the last GC occurrence -gccause GCC The cause for the current GC occurrence -gccause TT Tenuring threshold. If copied this amount of times in young area (S0 ->S1, S1->S0), they are then moved to old area. -gcnew MTT Maximum Tenuring threshold. If copied this amount of times inside young arae, then they are moved to old area. -gcnew DSS Adequate size of survivor in KB -gcnew The advantage of jstat is that it can always monitor the GC operation data of Java applications running on local/remote machine, as long as a console can be used. From these items, the following result is output when –gcutil is used. At the time of GC tuning, pay careful attention to YGC, YGCT, FGC, FGCT and GCT. S0 S1 E O P YGC YGCT FGC FGCT GCT 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 These items are important because they show how much time was spent in running GC. In this example, YGC is 217 and YGCT is 0.928. So, after calculating the arithmetical average, you can see that it required about 4 ms (0.004 seconds) for each young GC. Likewise, the average full GC time us 33ms. But the arithmetical average often does not help analyzing the actual GC problem. This is due to the severe deviations in GC operation time. (In other words, if the average time is 0.067 seconds for a full GC, one GC may have lasted 1 ms while the other one lasted 57 ms.) In order to check the individual GC time instead of the arithmetical average time, it is better to use -verbosegc. -verbosegc -verbosegc is one of the JVM options specified when running a Java application. While jstat can monitor any JVM application that has not specified any options, -verbosegc needs to be specified in the beginning, so it could be seen as an unnecessary option (since jstat can be used instead). However, as -verbosegc displays easy to understand output results whenever a GC occurs, it is very helpful for monitoring rough GC information. jstat -verbosegc Monitoring Target Java application running on a machine that can log in to a terminal, or a remote Java application that can connect to the network by using jstatd Only when -verbogc was specified as a JVM starting option Output information Heap status (usage, maximum size, number of times for GC/time, etc.) Size of ew and old area before/after GC, and GC operation time Output Time Every designated time Whenever GC occurs Whenever useful When trying to observe the changes of the size of heap area When trying to see the effect of a single GC The followings are other options that can be used with -verbosegc. -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps (from JDK 6 update 4) If only -verbosegc is used, then -XX:+PrintGCDetails is applied by default. Additional options for –verbosgc are not exclusive and can be mixed and used together. When using -verbosegc, you can see the results in the following format whenever a minor GC occurs. [GC [: -> , secs] -> , secs] ] Collector Name of Collector Used for minor gc starting occupancy1 The size of young area before GC ending occupancy1 The size of young area after GC pause time1 The time when the Java application stopped running for minor GC starting occupancy3 The total size of heap area before GC ending occupancy3 The total size of heap area after GC pause time3 The time when the Java application stopped running for overall heap GC, including major GC This is an example of -verbosegc output for minor GC: S0 S1 E O P YGC YGCT FGC FGCT GCT 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 This is the example of output results after an Full GC occurred. [Full GC [Tenured: 3485K->4095K(4096K), 0.1745373 secs] 61244K->7418K(63104K), [Perm : 10756K->10756K(12288K)], 0.1762129 secs] [Times: user=0.19 sys=0.00, real=0.19 secs] If a CMS collector is used, then the following CMS information can be provided as well. As -verbosegc option outputs a log every time a GC event occurs, it is easy to see the changes of the heap usage rates caused by GC operation. (Java) VisualVM + Visual GC Java Visual VM is a GUI profiling/monitoring tool provided by Oracle JDK. Figure 1: VisualVM Screenshot. Instead of the version that is included with JDK, you can download Visual VM directly from its website. For the sake of convenience, the version included with JDK will be referred to as Java VisualVM (jvisualvm), and the version available from the website will be referred to as Visual VM (visualvm). The features of the two are not exactly identical, as there are slight differences, such as when installing plug-ins. Personally, I prefer the Visual VM version, which can be downloaded from the website. After running Visual VM, if you select the application that you wish to monitor from the window on the left side, you can find the "Monitoring" tab there. You can get the basic information about GC and Heap from this Monitoring tab. Though the basic GC status is also available through the basic features of VisualVM, you cannot access detailed information that is available from either jstat or -verbosegc option. If you want the detailed information provided by jstat, then it is recommended to install the Visual GC plug-in. Visual GC can be accessed in real time from the Tools menu. Figure 2: Viusal GC Installation Screenshot. By using Visual GC, you can see the information provided by running jstatd in a more intuitive way. Figure 3: Visual GC execution screenshot. HPJMeter HPJMeter is convenient for analyzing -verbosegc output results. If Visual GC can be considered as the GUI equivalent of jstat, then HPJMeter would be the GUI equivalent of -verbosgc. Of course, GC analysis is just one of the many features provided by HPJMeter. HPJMeter is a performance monitoring tool developed by HP. It can be used in HP-UX, as well as Linux and MS Windows. Originally, a tool called HPTune used to provide the GUI analysis feature for -verbosegc. However, since the HPTune feature has been integrated into HPJMeter since version 3.0, there is no need to download HPTune separately. When executing an application, the -verbosegc output results will be redirected to a separate file. You can open the redirected file with HPJMeter, which allows faster and easier GC performance data analysis through the intuitive GUI. Figure 4: HPJMeter.

Learn about the structure of JVM, how it works, executes Java bytecode, the order of execution, examples of common mistakes and their solutions, new Java SE 7 features.

Explore SQL query optimization and normalization.

I would consider this post as best practice for using Spring in enterprise application development.

Today we're going to build a simple TCP proxy server. The scenario: we've got one host (the client) that establishes a TCP connection to another one (the remote). client —> remote We want to set up a proxy server in the middle, so the client will establish the connection with the proxy and the proxy will forward it to the remote, keeping in mind the remote response also. With node.js is really simple to perform those kind of network operations. client —> proxy -> remote var net = require('net'); var LOCAL_PORT = 6512; var REMOTE_PORT = 6512; var REMOTE_ADDR = "192.168.1.25"; var server = net.createServer(function (socket) { socket.on('data', function (msg) { console.log(' ** START **'); console.log('<< From client to proxy ', msg.toString()); var serviceSocket = new net.Socket(); serviceSocket.connect(parseInt(REMOTE_PORT), REMOTE_ADDR, function () { console.log('>> From proxy to remote', msg.toString()); serviceSocket.write(msg); }); serviceSocket.on("data", function (data) { console.log('<< From remote to proxy', data.toString()); socket.write(data); console.log('>> From proxy to client', data.toString()); }); }); }); server.listen(LOCAL_PORT); console.log("TCP server accepting connection on port: " + LOCAL_PORT); Simple, isn’t it? Source code in github

As you may have seen from my past performance related articles and HashMap case studies, Java thread safety problems can bring down your Java EE application and the Java EE container fairly easily. One of most common problems I have observed when troubleshooting Java EE performance problems is infinite looping triggered from the non-thread safe HashMap get() and put() operations. This problem is known since several years but recent production problems have forced me to revisit this issue one more time. This article will revisit this classic thread safety problem and demonstrate, using a simple Java program, the risk associated with a wrong usage of the plain old java.util.HashMap data structure involved in a concurrent threads context. This proof of concept exercise will attempt to achieve the following 3 goals: Revisit and compare the Java program performance level between the non-thread safe and thread safe Map data structure implementations (HashMap, Hashtable, synchronized HashMap, ConcurrentHashMap) Replicate and demonstrate the HashMap infinite looping problem using a simple Java program that everybody can compile, run and understand Review the usage of the above Map data structures in a real-life and modern Java EE container implementation such as JBoss AS7 For more detail on the ConcurrentHashMap implementation strategy, I highly recommend the great article from Brian Goetz on this subject. Tools and server specifications As a starting point, find below the different tools and software’s used for the exercise: Sun/Oracle JDK & JRE 1.7 64-bit Eclipse Java EE IDE Windows Process Explorer (CPU per Java Thread correlation) JVM Thread Dump (stuck thread analysis and CPU per Thread correlation) The following local computer was used for the problem replication process and performance measurements: Intel(R) Core(TM) i5-2520M CPU @ 2.50Ghz (2 CPU cores, 4 logical cores) 8 GB RAM Windows 7 64-bit * Results and performance of the Java program may vary depending of your workstation or server specifications. Java program In order to help us achieve the above goals, a simple Java program was created as per below: The main Java program is HashMapInfiniteLoopSimulator.java A worker Thread class WorkerThread.java was also created The program is performing the following: Initialize different static Map data structures with initial size of 2 Assign the chosen Map to the worker threads (you can chose between 4 Map implementations) Create a certain number of worker threads (as per the header configuration). 3 worker threads were created for this proof of concept NB_THREADS = 3; Each of these worker threads has the same task: lookup and insert a new element in the assigned Map data structure using a random Integer element between 1 – 1 000 000. Each worker thread perform this task for a total of 500K iterations The overall program performs 50 iterations in order to allow enough ramp up time for the HotSpot JVM The concurrent threads context is achieved using the JDK ExecutorService As you can see, the Java program task is fairly simple but complex enough to generate the following critical criteria’s: Generate concurrency against a shared / static Map data structure Use a mix of get() and put() operations in order to attempt to trigger internal locks and / or internal corruption (for the non-thread safe implementation) Use a small Map initial size of 2, forcing the internal HashMap to trigger an internal rehash/resize Finally, the following parameters can be modified at your convenience: ## Number of worker threads private static final int NB_THREADS = 3; ## Number of Java program iterations private static final int NB_TEST_ITERATIONS = 50; ## Map data structure assignment. You can choose between 4 structures // Plain old HashMap (since JDK 1.2) nonThreadSafeMap = new HashMap(2); // Plain old Hashtable (since JDK 1.0) threadSafeMap1 = new Hashtable(2); // Fully synchronized HashMap threadSafeMap2 = new HashMap(2); threadSafeMap2 = Collections.synchronizedMap(threadSafeMap2); // ConcurrentHashMap (since JDK 1.5) threadSafeMap3 = new ConcurrentHashMap(2); /*** Assign map at your convenience ****/ assignedMapForTest = threadSafeMap3; Now find below the source code of our sample program. #### HashMapInfiniteLoopSimulator.java package org.ph.javaee.training4; import java.util.Collections; import java.util.Map; import java.util.HashMap; import java.util.Hashtable; import java.util.concurrent.ConcurrentHashMap; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; /** * HashMapInfiniteLoopSimulator * @author Pierre-Hugues Charbonneau * */ public class HashMapInfiniteLoopSimulator { private static final int NB_THREADS = 3; private static final int NB_TEST_ITERATIONS = 50; private static Map assignedMapForTest = null; private static Map nonThreadSafeMap = null; private static Map threadSafeMap1 = null; private static Map threadSafeMap2 = null; private static Map threadSafeMap3 = null; /** * Main program * @param args */ public static void main(String[] args) { System.out.println("Infinite Looping HashMap Simulator"); System.out.println("Author: Pierre-Hugues Charbonneau"); System.out.println("http://javaeesupportpatterns.blogspot.com"); for (int i=0; i(2); // Plain old Hashtable (since JDK 1.0) threadSafeMap1 = new Hashtable(2); // Fully synchronized HashMap threadSafeMap2 = new HashMap(2); threadSafeMap2 = Collections.synchronizedMap(threadSafeMap2); // ConcurrentHashMap (since JDK 1.5) threadSafeMap3 = new ConcurrentHashMap(2); // ConcurrentHashMap /*** Assign map at your convenience ****/ assignedMapForTest = threadSafeMap3; long timeBefore = System.currentTimeMillis(); long timeAfter = 0; Float totalProcessingTime = null; ExecutorService executor = Executors.newFixedThreadPool(NB_THREADS); for (int j = 0; j < NB_THREADS; j++) { /** Assign the Map at your convenience **/ Runnable worker = new WorkerThread(assignedMapForTest); executor.execute(worker); } // This will make the executor accept no new threads // and finish all existing threads in the queue executor.shutdown(); // Wait until all threads are finish while (!executor.isTerminated()) { } timeAfter = System.currentTimeMillis(); totalProcessingTime = new Float( (float) (timeAfter - timeBefore) / (float) 1000); System.out.println("All threads completed in "+totalProcessingTime+" seconds"); } } } #### WorkerThread.java package org.ph.javaee.training4; import java.util.Map; /** * WorkerThread * * @author Pierre-Hugues Charbonneau * */ public class WorkerThread implements Runnable { private Map map = null; public WorkerThread(Map assignedMap) { this.map = assignedMap; } @Override public void run() { for (int i=0; i<500000; i++) { // Return 2 integers between 1-1000000 inclusive Integer newInteger1 = (int) Math.ceil(Math.random() * 1000000); Integer newInteger2 = (int) Math.ceil(Math.random() * 1000000); // 1. Attempt to retrieve a random Integer element Integer retrievedInteger = map.get(String.valueOf(newInteger1)); // 2. Attempt to insert a random Integer element map.put(String.valueOf(newInteger2), newInteger2); } } } Performance comparison between thread safe Map implementations The first goal is to compare the performance level of our program when using different thread safe Map implementations: Plain old Hashtable (since JDK 1.0) Fully synchronized HashMap (via Collections.synchronizedMap()) ConcurrentHashMap (since JDK 1.5) Find below the graphical results of the execution of the Java program for each iteration along with a sample of the program console output. # Output when using ConcurrentHashMap Infinite Looping HashMap Simulator Author: Pierre-Hugues Charbonneau http://javaeesupportpatterns.blogspot.com All threads completed in 0.984 seconds All threads completed in 0.908 seconds All threads completed in 0.706 seconds All threads completed in 1.068 seconds All threads completed in 0.621 seconds All threads completed in 0.594 seconds All threads completed in 0.569 seconds All threads completed in 0.599 seconds ……………… As you can see, the ConcurrentHashMap is the clear winner here, taking in average only half a second (after an initial ramp-up) for all 3 worker threads to concurrently read and insert data within a 500K looping statement against the assigned shared Map. Please note that no problem was found with the program execution e.g. no hang situation. The performance boost is definitely due to the improved ConcurrentHashMap performance such as the non-blocking get() operation. The 2 other Map implementations performance level was fairly similar with a small advantage for the synchronized HashMap. HashMap infinite looping problem replication The next objective is to replicate the HashMap infinite looping problem observed so often from Java EE production environments. In order to do that, you simply need to assign the non-thread safe HashMap implementation as per code snippet below: /*** Assign map at your convenience ****/ assignedMapForTest = nonThreadSafeMap; Running the program as is using the non-thread safe HashMap should lead to: No output other than the program header Significant CPU increase observed from the system At some point the Java program will hang and you will be forced to kill the Java process What happened? In order to understand this situation and confirm the problem, we will perform a CPU per Thread analysis from the Windows OS using Process Explorer and JVM Thread Dump. 1 - Run the program again then quickly capture the thread per CPU data from Process Explorer as per below. Under explore.exe you will need to right click over the javaw.exe and select properties. The threads tab will be displayed. We can see overall 4 threads using almost all the CPU of our system. 2 – Now you have to quickly capture a JVM Thread Dump using the JDK 1.7 jstack utility. For our example, we can see our 3 worker threads which seems busy/stuck performing get() and put() operations. ..\jdk1.7.0\bin>jstack 272 2012-08-29 14:07:26 Full thread dump Java HotSpot(TM) 64-Bit Server VM (21.0-b17 mixed mode): "pool-1-thread-3" prio=6 tid=0x0000000006a3c000 nid=0x18a0 runnable [0x0000000007ebe000] java.lang.Thread.State: RUNNABLE at java.util.HashMap.put(Unknown Source) at org.ph.javaee.training4.WorkerThread.run(WorkerThread.java:32) at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source) at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source) at java.lang.Thread.run(Unknown Source) "pool-1-thread-2" prio=6 tid=0x0000000006a3b800 nid=0x6d4 runnable [0x000000000805f000] java.lang.Thread.State: RUNNABLE at java.util.HashMap.get(Unknown Source) at org.ph.javaee.training4.WorkerThread.run(WorkerThread.java:29) at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source) at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source) at java.lang.Thread.run(Unknown Source) "pool-1-thread-1" prio=6 tid=0x0000000006a3a800 nid=0x2bc runnable [0x0000000007d9e000] java.lang.Thread.State: RUNNABLE at java.util.HashMap.put(Unknown Source) at org.ph.javaee.training4.WorkerThread.run(WorkerThread.java:32) at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source) at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source) at java.lang.Thread.run(Unknown Source) .............. 3 – CPU per thread correlation It is now time to convert the Process Explorer thread ID DECIMAL format to HEXA format as per below. The HEXA value allows us to map and identify each thread as per below: ## TID: 1748 (nid=0X6D4) Thread name: pool-1-thread-2 CPU @25.71% Task: Worker thread executing a HashMap.get() operation at java.util.HashMap.get(Unknown Source) at org.ph.javaee.training4.WorkerThread.run(WorkerThread.java:29) at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source) at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source) at java.lang.Thread.run(Unknown Source) ## TID: 700 (nid=0X2BC) Thread name: pool-1-thread-1 CPU @23.55% Task: Worker thread executing a HashMap.put() operation at java.util.HashMap.put(Unknown Source) at org.ph.javaee.training4.WorkerThread.run(WorkerThread.java:32) at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source) at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source) at java.lang.Thread.run(Unknown Source) ## TID: 6304 (nid=0X18A0) Thread name: pool-1-thread-3 CPU @12.02% Task: Worker thread executing a HashMap.put() operation at java.util.HashMap.put(Unknown Source) at org.ph.javaee.training4.WorkerThread.run(WorkerThread.java:32) at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source) at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source) at java.lang.Thread.run(Unknown Source) ## TID: 5944 (nid=0X1738) Thread name: pool-1-thread-1 CPU @20.88% Task: Main Java program execution "main" prio=6 tid=0x0000000001e2b000 nid=0x1738 runnable [0x00000000029df000] java.lang.Thread.State: RUNNABLE at org.ph.javaee.training4.HashMapInfiniteLoopSimulator.main(HashMapInfiniteLoopSimulator.java:75) As you can see, the above correlation and analysis is quite revealing. Our main Java program is in a hang state because our 3 worker threads are using lot of CPU and not going anywhere. They may appear "stuck" performing HashMap get() & put() but in fact they are all involved in an infinite loop condition. This is exactly what we wanted to replicate. HashMap infinite looping deep dive Now let’s push the analysis one step further to better understand this looping condition. For this purpose, we added tracing code within the JDK 1.7 HashMap Java class itself in order to understand what is happening. Similar logging was added for the put() operation and also a trace indicating that the internal & automatic rehash/resize got triggered. The tracing added in get() and put() operations allows us to determine if the for() loop is dealing with circular dependency which would explain the infinite looping condition. #### HashMap.java get() operation public V get(Object key) { if (key == null) return getForNullKey(); int hash = hash(key.hashCode()); /*** P-H add-on- iteration counter ***/ int iterations = 1; for (Entry e = table[indexFor(hash, table.length)]; e != null; e = e.next) { /*** Circular dependency check ***/ Entry currentEntry = e; Entry nextEntry = e.next; Entry nextNextEntry = e.next != null?e.next.next:null; K currentKey = currentEntry.key; K nextNextKey = nextNextEntry != null?(nextNextEntry.key != null?nextNextEntry.key:null):null; System.out.println("HashMap.get() #Iterations : "+iterations++); if (currentKey != null && nextNextKey != null ) { if (currentKey == nextNextKey || currentKey.equals(nextNextKey)) System.out.println(" ** Circular Dependency detected! ["+currentEntry+"]["+nextEntry+"]"+"]["+nextNextEntry+"]"); } /***** END ***/ Object k; if (e.hash == hash && ((k = e.key) == key || key.equals(k))) return e.value; } return null; } HashMap.get() #Iterations : 1 HashMap.put() #Iterations : 1 HashMap.put() #Iterations : 1 HashMap.put() #Iterations : 1 HashMap.put() #Iterations : 1 HashMap.resize() in progress... HashMap.put() #Iterations : 1 HashMap.put() #Iterations : 2 HashMap.resize() in progress... HashMap.resize() in progress... HashMap.put() #Iterations : 1 HashMap.put() #Iterations : 2 HashMap.put() #Iterations : 1 HashMap.get() #Iterations : 1 HashMap.get() #Iterations : 1 HashMap.put() #Iterations : 1 HashMap.get() #Iterations : 1 HashMap.get() #Iterations : 1 HashMap.put() #Iterations : 1 HashMap.get() #Iterations : 1 HashMap.put() #Iterations : 1 ** Circular Dependency detected! [362565=362565][333326=333326]][362565=362565] HashMap.put() #Iterations : 2 ** Circular Dependency detected! [333326=333326][362565=362565]][333326=333326] HashMap.put() #Iterations : 1 HashMap.put() #Iterations : 1 HashMap.get() #Iterations : 1 HashMap.put() #Iterations : 1 ............................. HashMap.put() #Iterations : 56823 Again, the added logging was quite revealing. We can see that following a few internal HashMap.resize() the internal structure became affected, creating circular dependency conditions and triggering this infinite looping condition (#iterations increasing and increasing...) with no exit condition. It is also showing that the resize() / rehash operation is the most at risk of internal corruption, especially when using the default HashMap size of 16. This means that the initial size of the HashMap appears to be a big factor in the risk & problem replication. Finally, it is interesting to note that we were able to successfully run the test case with the non-thread safe HashMap by assigning an initial size setting at 1000000, preventing any resize at all. Find below the merged graph results: The HashMap was our top performer but only when preventing an internal resize. Again, this is definitely not a solution to the thread safe risk but just a way to demonstrate that the resize operation is the most at risk given the entire manipulation of the HashMap performed at that time. The ConcurrentHashMap, by far, is our overall winner by providing both fast performance and thread safety against that test case. JBoss AS7 Map data structures usage We will now conclude this article by looking at the different Map implementations within a modern Java EE container implementation such as JBoss AS 7.1.2. You can obtain the latest source code from the github master branch. Find below the report: Total JBoss AS7.1.2 Java files (August 28, 2012 snapshot): 7302 Total Java classes using java.util.Hashtable: 72 Total Java classes using java.util.HashMap: 512 Total Java classes using synchronized HashMap: 18 Total Java classes using ConcurrentHashMap: 46 Hashtable references were found mainly within the test suite components and from naming and JNDI related implementations. This low usage is not a surprise here. References to the java.util.HashMap were found from 512 Java classes. Again not a surprise given how common this implementation is since the last several years. However, it is important to mention that a good ratio was found either from local variables (not shared across threads), synchronized HashMap or manual synchronization safeguard so “technically” thread safe and not exposed to the above infinite looping condition (pending/hidden bugs is still a reality given the complexity with Java concurrency programming…this case study involving Oracle Service Bus 11g is a perfect example). A low usage of synchronized HashMap was found with only 18 Java classes from packages such as JMS, EJB3, RMI and clustering. Finally, find below a breakdown of the ConcurrentHashMap usage which was our main interest here. As you will see below, this Map implementation is used by critical JBoss components layers such as the Web container, EJB3 implementation etc. ## JBoss Single Sign On Used to manage internal SSO ID's involving concurrent Thread access Total: 1 ## JBoss Java EE & Web Container Not surprising here since lot of internal Map data structures are used to manage the http sessions objects, deployment registry, clustering & replication, statistics etc. with heavy concurrent Thread access. Total: 11 ## JBoss JNDI & Security Layer Used by highly concurrent structures such as internal JNDI security management. Total: 4 ## JBoss domain & managed server management, rollout plans... Total: 7 ## JBoss EJB3 Used by data structures such as File Timer persistence store, application Exception, Entity Bean cache, serialization, passivation... Total: 8 ## JBoss kernel, Thread Pools & protocol management Used by high concurrent Threads Map data structures involved in handling and dispatching/processing incoming requests such as HTTP. Total: 3 ## JBoss connectors such as JDBC/XA DataSources... Total: 2 ## Weld (reference implementation of JSR-299: Contexts and Dependency Injection for the JavaTM EE platform) Used in the context of ClassLoader and concurrent static Map data structures involving concurrent Threads access. Total: 3 ## JBoss Test Suite Used in some integration testing test cases such as an internal Data Store, ClassLoader testing etc. Total: 3 Final words I hope this article has helped you revisit this classic problem and understand one of the common problems and risks associated with a wrong usage of the non-thread safe HashMap implementation. My main recommendation to you is to be careful when using an HashMap in a concurrent threads context. Unless you are a Java concurrency expert, I recommend that you use ConcurrentHashMap instead which offers a very good balance between performance and thread safety. As usual, extra due diligence is always recommended such as performing cycles of load & performance testing. This will allow you to detect thread safety and / or performance problems before you promote the solution to your client production environment. Please provide any comments and share your experience with ConcurrentHashMap or HashMap implementations and troubleshooting.

In my post about JaCoCo and MavenI wrote about the problems of using the JaCoCo Maven plugin in multimodule Maven projects because of having one report for each module separately instead of one report for all modules, and how it can be fixed using JaCoCo Ant Task. In this post we are going to see how to use the JaCoCo Jenkins plugin to achieve the same goal of Ant Tasks and have overall code coverage statistics for all modules. The first step is installing the JaCoCo Jenkins plugin. Go to Jenkins -> Manage Jenkins -> Plugin Manager -> Available and find JaCoCo Plugin The next step, if it is not done already, is configuring your JaCoCo Maven plugin into parent pom: org.jacoco jacoco-maven-plugin ${jacoco.version} prepare-agent report prepare-package report And finally a post-action must be configured to the job responsible for packaging the application. Note that in previous pom file reports are generated just before the package goal is executed. Go to Configure -> Post-build Actions -> Add post-build action -> Record JaCoCo coverage report. Then we have to set folders or files containing JaCoCoXML reports, which are using the previous pom to **/target/site/jacoco/jacoco*.xml, and also set when we consider that a build is healthy in terms of coverage. Then we can save the job configuration and run the build project. After the project is build, a new report will appear just under the test result trend graph, called code coverage trend, where we can see the code coverage of all project modules. From the left menu, you can enter into Coverage Report and see code coverage of each module separately. Furthermore, visiting the Jenkins main page will give you a nice quick overview of a job when you mouse over the weather icon as shown: Keep in mind that this approach for merging code coverage files will only work if you are using Jenkins as a CI system. Ant Task is a more generic solution and can also be used with the JaCoCo Jenkins plugin. We Keep Learning, Alex.

It has been a while since I wanted to have CDI running with JavaFX2. Some people already blogged on how to proceed by getting Guice injection [1] to work with JavaFX & FXML. Well, now it's my turn to provide a way to empower JavaFX with CDI, using Weld as the implementation. My goal was just to have CDI working, no matter how I was using JavaFX, by directly coding in plain Java or using FXML. Ready? Let's go!!! Bootstrap JavaFX & Weld/CDI The launcher class will be the only place where we will have Weld-specific code—all the rest will be totally CDI compliant. The only trick here is to make the application parameters available as a CDI-compliant object so we can reuse them afterwards. Notice also that we use the CDI event mechanism to start up our real application code. public class WeldJavaFXLauncher extends Application { /** * Nothing special, we just use the JavaFX Application methods to boostrap * JavaFX */ public static void main(String[] args) { Application.launch(WeldJavaFXLauncher.class, args); } @SuppressWarnings("serial") @Override public void start(final Stage primaryStage) throws Exception { // Let's initialize CDI/Weld. WeldContainer weldContainer = new Weld().initialize(); // Make the application parameters injectable with a standard CDI // annotation weldContainer.instance().select(ApplicationParametersProvider.class).get().setParameters(getParameters()); // Now that JavaFX thread is ready // let's inform whoever cares using standard CDI notification mechanism: // CDI events weldContainer.event().select(Stage.class, new AnnotationLiteral() {}).fire(primaryStage); } } Start our real JavaFX application Here we start our real application code. We're just listening to the previously fired event (containing the Scene object to render into) so we can start showing our application. In the following example, we load an FXML GUI, but it might have been any node created in any way. public class LoginApplicationStarter { // Let's have a FXMLLoader injected automatically @Inject FXMLLoader fxmlLoader; // Our CDI entry point, we just listen to an event providing the startup scene public void launchJavaFXApplication(@Observes @StartupScene Stage s) { InputStream is = null; try { is = getClass().getResourceAsStream("login.fxml"); // we just load our FXML form (including controler and so on) Parent root = (Parent) fxmlLoader.load(is); s.setScene(new Scene(root, 300, 275)); s.show(); // let's show the scene } catch (IOException e) { throw new IllegalStateException("cannot load FXML login screen", e); } finally { // omitted is cleanup } } } But what about the FXML controller? First let's have a look at the controller we want to use inside our application. It is a pure POJO class annotated with both JavaFX & CDI annotations. // Simple application controller that uses injected fields // to delegate login process and to get default values from the command line using: --user=SomeUser public class LoginController implements Initializable { // Standard FXML injected fields @FXML TextField loginField; @FXML PasswordField passwordField; @FXML Text feedback; // CDI Injected service @Inject LoginService loginService; // Default application parameters retrieved using CDI @Inject Parameters applicationParameters; @FXML protected void handleSubmitButtonAction(ActionEvent event) { feedback.setText(loginService.login(loginField.getText(), passwordField.getText())); } @Override public void initialize(URL location, ResourceBundle resources) { loginField.setText(applicationParameters.getNamed().get("user")); } } In order to have injection working inside the FXML controller, we need to set up JavaFX so that controller objects are created by CDI. As we are in a CDI environment we can also have the FXMLLoader classes injected (that's exactly what we did in the previous LoginApplicationStarter class). How can we achieve this? We just have to provide a Producer class whose responsibility will be to create FXMLLoader instances that are able to load FXML GUIs and instantiate controllers using CDI. The only part that's a little tricky there is that the controller instantiation depends on the required class or interface (using fx:controller in your fxml file). In order to have such a runtime injection/resolution available we use a CDI Instance Object. public class FXMLLoaderProducer { @Inject Instance, Object>() { @Override public Object call(Class param) { return instance.select(param).get(); } }); return loader; } } I hope you found the article interesting and you do not hesitate to comment if you see some errors or possible enhancements. Finally, if you are interested you can find the full source code here. [1] http://andrewtill.blogspot.be/2012/07/creating-javafx-controllers-using-guice.htm

Here is a quick example of how to programmatically access Camel MBeans to monitor and manipulate routes... first, get a connection to a JMX server (assumes localhost, port 1099, no auth) note, always cache the connection for subsequent requests (can cause memory utilization issues otherwise) JMXServiceURL url = new JMXServiceURL("service:jmx:rmi:///jndi/rmi://localhost:1099/jmxrmi"); JMXConnector jmxc = JMXConnectorFactory.connect(url); MBeanServerConnection server = jmxc.getMBeanServerConnection(); use the following to iterate over all routes and retrieve statistics (state, exchanges, etc)... ObjectName objName = new ObjectName("org.apache.camel:type=routes,*"); List cacheList = new LinkedList(server.queryNames(objName, null)); for (Iterator iter = cacheList.iterator(); iter.hasNext();) { objName = iter.next(); String keyProps = objName.getCanonicalKeyPropertyListString(); ObjectName objectInfoName = new ObjectName("org.apache.camel:" + keyProps); String routeId = (String) server.getAttribute(objectInfoName, "RouteId"); String description = (String) server.getAttribute(objectInfoName, "Description"); String state = (String) server.getAttribute(objectInfoName, "State"); ... } use the following to execute operations against a Camel route (stop,start, etc) ObjectName objName = new ObjectName("org.apache.camel:type=routes,*"); List cacheList = new LinkedList(server.queryNames(objName, null)); for (Iterator iter = cacheList.iterator(); iter.hasNext();) { objName = iter.next(); String keyProps = objName.getCanonicalKeyPropertyListString(); if(keyProps.contains(routeID)) { ObjectName objectRouteName = new ObjectName("org.apache.camel:" + keyProps); Object[] params = {}; String[] sig = {}; server.invoke(objectRouteName, operationName, params, sig); return; } } summary These APIs can easily be used to build a web or command line based tool to support remote Camel management features. All of these features are available via the JMX console and Camel does provide a web console to support some management/monitoring tasks. See these pages for more information... http://camel.apache.org/camel-jmx.html http://camel.apache.org/web-console.html

JBoss AS 7 is neat but the documentation is still quite lacking (and error messages not as useful as they could be). This post summarizes how you can create your own JavaEE-compliant login module for authenticating users of your webapp deployed on JBoss AS. A working elementary username-password module provided. Why use Java EE standard authentication? Java EE security primer A part of the Java EE specification is security for web and EE applications, which makes it possible both to specify declarative constraints in your web.xml (such as “role X is required to access resources at URLs “/protected/*”) and to control it programatically, i.e. verifying that the user has a particular role (see HttpServletRequest.isUserInRole). It works as follows: You declare in your web.xml: Login configuration – primarily whether to use browser prompt (basic) or a custom login form and a name for the login realm The custom form uses “magic” values for the post action and the fields, starting with j_, which are intercepted and processed by the server The roles used in your application (typically you’d something like “user” and perhaps “admin”) What roles are required for accessing particular URL patterns (default: none) Whether HTTPS is required for some parts of the application You tell your application server how to authenticate users for that login realm, usually by associating its name with one of the available login modules in the configuration (the modules ranging from simple file-based user list to LDAP and Kerberos support). Only rarely do you need to create your own login module, the topic of this post. If this is new for you than I strongly recommend reading The Java EE 5 Tutorial – Examples: Securing Web Applications (Form-Based Authentication with a JSP Page incl. security constraint specification, Basic Authentication with JAX-WS, Securing an Enterprise Bean, Using the isCallerInRole and getCallerPrincipal Methods). Why to bother? Declarative security is nicely decoupled from the business code It’s easy to propagate security information between a webapp and for example EJBs (where you can protect a complete bean or a particular method declaratively via xml or via annotations such as @RolesAllowed) It’s easy to switch to a different authentication mechanism such as LDAP and it’s more likely that SSO will be supported Custom login module implementation options If one of the login modules (part of a security domain) provided out of the box with JBoss, such as UsersRoles, Ldap, Database, Certificate, isn’t sufficient for you then you can adjust one of them or implement your own. You can: Extend one of the concrete modules, overriding one or some of its methods to ajdust to your needs – see f.ex. how to override the DatabaseServerLoginModule to specify your own encryption of the stored passwords. This should be your primary choice, of possible. Subclass UsernamePasswordLoginModule Implement javax.security.auth.spi.LoginModule if you need maximal flexibility and portability (this is a part of Java EE, namely JAAS, and is quite complex) JBoss EAP 5 Security Guide Ch. 12.2. Custom Modules has an excellent description of the basic modules (AbstractServerLoginModule, UsernamePasswordLoginModule) and how to proceed when subclassing them or any other standard module, including description of the key methods to implement/override. You must read it. (The guide is still perfectly applicable to JBoss AS 7 in this regard.) The custom JndiUserAndPass module example, extending UsernamePasswordLoginModule, is also worth reading – it uses module options and JNDI lookup. Example: Custom UsernamePasswordLoginModule subclass See the source code of MySimpleUsernamePasswordLoginModule that extends JBoss’ UsernamePasswordLoginModule. The abstract UsernamePasswordLoginModule (source code) works by comparing the password provided by the user for equality with the password returned from the method getUsersPassword, implemented by a subclass. You can use the method getUsername to obtain the user name of the user attempting login. Implement abstract methods getUsersPassword() Implement getUsersPassword() to lookup the user’s password wherever you have it. If you do not store passwords in plain text then read how to customize the behavior via other methods below getRoleSets() Implement getRoleSets() (from AbstractServerLoginModule) to return at least one group named “Roles” and containing 0+ roles assigned to the user, see the implementation in the source code for this post. Usually you’d lookup the roles for the user somewhere (instead of returning hardcoded “user_role” role). Optionally extend initialize(..) to get access to module options etc. Usually you will also want to extend initialize(Subject subject, CallbackHandler callbackHandler, Map sharedState, Map options) (called for each authentication attempt), To get values of properties declared via the element in the security-domain configuration – see JBoss 5 custom module example To do other initialization, such as looking up a data source via JNDI – see the DatabaseServerLoginModule Optionally override other methods to customize the behavior If you do not store passwords in plain text (a wise choice!) and your hashing method isn’t supported out of the box then you can override createPasswordHash(String username, String password, String digestOption) to hash/encrypt the user-supplied password before comparison with the stored password. Alternatively you could override validatePassword(String inputPassword, String expectedPassword) to do whatever conversion on the password before comparison or even do a different type of comparison than equality. Custom login module deployment options In JBoss AS you can Deploy your login module class in a JAR as a standalone module, independently of the webapp, under /modules/, together with a module.xml – described at JBossAS7SecurityCustomLoginModules Deploy your login module class as a part of your webapp (no module.xml required) In a JAR inside WEB-INF/lib/ Directly under WEB-INF/classes In each case you have to declare a corresponding security-domain it inside JBoss configuration (standalone/configuration/standalone.xml or domain/configuration/domain.xml): The code attribute should contain the fully qualified name of your login module class and the security-domain’s name must match the declaration in jboss-web.xml: form-auth true The code Download the webapp jboss-custom-login containing the custom login module MySimpleUsernamePasswordLoginModule, follow the deployment instructions in the README.

I've seen a lot of discussion about how to monitor Camel based applications. Most people are looking for the following features: ability to view services (contexts, endpoints, routes), to view performance statistics (route throughput, etc) and to perform basic operations (start/stop routes, send messages, etc). This post will breakdown the options (that I know of) that are available today (as of Camel 2.8). If you have used other approaches or know of other ongoing development in this area, please let me know. JMX APIs Camel uses JMX to provide a standardized way to access metadata about contexts/routes/endpoints defined in a given application. Also, you can use JMX to interact with these components (start/stop routes, etc) in some interesting ways. I recently had some very specific Camel/ActiveMQ monitoring requests from a client. After looking at the options, we ended up building a standalone Tomcat web app that used JSPs, jQuery, Ajax and JMX APIs to view route/endpoint statistics, manage Camel routes (stop, start, etc) and monitor/manipulate ActiveMQ queues. It provided some much needed visibility and management features for our Camel/ActiveMQ based message processing application... CamelContext If you have a handle to the CamelContext, there are various APIs that can help describe and manage routes and endpoints. These are used by the existing Camel Web Console and can be used to build custom interface to retrieve and use this information in various ways... here are some of the notable APIs... getRouteDefinitions() getEndpoints() getEndpointsMap() getRouteStatus(routeId) startRoute(routeId) stopRoute(routeId) removeRoute(routeId) addRoutes(routeBuilder) suspendRoute(routeId) resumeRoute(routeId) With a little creativity, you can use these APIs to manage/monitor and re-wire a Camel application dynamically. Camel Web Console This console provides web and REST interfaces to Camel contexts/routes/endpoints and allows you to view/manage endpoints/routes, send messages to endpoints, viewing route statistics, etc. That being said, using this web console with an existing Camel application is tricky at the moment. It's currently deployed as a war file that only has access to the CamelContext defined in its embedded spring XML file. Though the entire camel-web project can be embedded and customized in your application if you desire (and know Scalate). Given my recent client requirements, I opted to build my own basic app using JSPs/JMX as described above. There has been some recent support for deploying this console in OSGI, where it should be able to view any CamelContexts deployed in the container, etc. However, I'm yet to see this work...more on this later. Using Camel APIs There are also a number of Camel technologies/patterns that can be used to add monitoring to existing routes. wire tap - can add message logging (to a file or JMS queue/topic, etc) or other inline processing advicewith - can be used to modify existing routes to apply before/after operations or add/remove operations in a route intercept - can be used to intercept Exchanges while they are in route, can apply to all endpoints, certain endpoints or just starting endpoints BrowsableEndpoint - is an interface which Endpoints may implement to support the browsing of the exchanges which are pending or have been sent on it. That being said, it takes some creativity to use these effectively and caution to not adversely affect the routes you are trying to monitor. Hyperic HQ You can use this tool to monitor Servicemix (or any process), but it more geared towards system monitoring and JVM stats. I didn't find it useful for any Camel specific monitoring. jConsole/VisualVM these are standard JMX based consoles. They aren't web based and can't be customized (easily anyways) to provide anything more than a tree-like view of JMX MBeans. If you know where to look though, you can do a lot with it. Summary These are just some quick notes at this point. As I learn about other ways of monitoring Camel, I'll update this list and give some more detailed comparison. Any comments are welcome...

Here is a quick example of how to programmatically access ActiveMQ MBeans to monitor and manipulate message queues... First, get a connection to a JMX server (assumes localhost, port 1099, no auth) Note, always cache the connection for subsequent requests (can cause memory utilization issues otherwise) JMXServiceURL url = new JMXServiceURL("service:jmx:rmi:///jndi/rmi://localhost:1099/jmxrmi"); JMXConnector jmxc = JMXConnectorFactory.connect(url); MBeanServerConnection conn = jmxc.getMBeanServerConnection(); Then, you can execute various operations such as addQueue, removeQueue, etc... String operationName="addQueue"; String parameter="MyNewQueue"; ObjectName activeMQ = new ObjectName("org.apache.activemq:BrokerName=localhost,Type=Broker"); if(parameter != null) { Object[] params = {parameter}; String[] sig = {"java.lang.String"}; conn.invoke(activeMQ, operationName, params, sig); } else { conn.invoke(activeMQ, operationName,null,null); } Also, you can get an ActiveMQ QueueViewMBean instance for a specified queue name... ObjectName activeMQ = new ObjectName("org.apache.activemq:BrokerName=localhost,Type=Broker"); BrokerViewMBean mbean = (BrokerViewMBean) MBeanServerInvocationHandler.newProxyInstance(conn, activeMQ,BrokerViewMBean.class, true); for (ObjectName name : mbean.getQueues()) { QueueViewMBean queueMbean = (QueueViewMBean) MBeanServerInvocationHandler.newProxyInstance(mbsc, name, QueueViewMBean.class, true); if (queueMbean.getName().equals(queueName)) { queueViewBeanCache.put(cacheKey, queueMbean); return queueMbean; } } Then, execute one of several APIs against the QueueViewMBean instance... Queue monitoring - getEnqueueCount(), getDequeueCount(), getConsumerCount(), etc... Queue manipulation - purge(), getMessage(String messageId), removeMessage(String messageId), moveMessageTo(String messageId, String destinationName), copyMessageTo(String messageId, String destinationName), etc... Summary The APIs can easily be used to build a web or command line based tool to support remote ActiveMQ management features. That being said, all of these features are available via the JMX console itself and ActiveMQ does provide a web console to support some management/monitoring tasks. See these pages for more information... http://activemq.apache.org/jmx-support.html http://activemq.apache.org/web-console.html

This is interesting stuff; Jonas Bonér organized some general some latency data by Peter Norvig as a Gist, and others expanded on it. What's interesting is how, scaling time up by a billion, converts a CPU instruction cycle into approximately one heartbeat, and yields a disk seek time of "a semester in university". ### Latency numbers every programmer should know L1 cache reference ......................... 0.5 ns Branch mispredict ............................ 5 ns L2 cache reference ........................... 7 ns Mutex lock/unlock ........................... 25 ns Main memory reference ...................... 100 ns Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs SSD random read ........................ 150,000 ns = 150 µs Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs Round trip within same datacenter ...... 500,000 ns = 0.5 ms Read 1 MB sequentially from SSD* ..... 1,000,000 ns = 1 ms Disk seek ........................... 10,000,000 ns = 10 ms Read 1 MB sequentially from disk .... 20,000,000 ns = 20 ms Send packet CA->Netherlands->CA .... 150,000,000 ns = 150 ms Assuming ~1GB/sec SSD  Visual chart provided by [ayshen](https://gist.github.com/ayshen) Data by [Jeff Dean](http://research.google.com/people/jeff/) Originally by [Peter Norvig](http://norvig.com/21-days.html#answers) Lets multiply all these durations by a billion: Magnitudes: ### Minute: L1 cache reference 0.5 s One heart beat (0.5 s) Branch mispredict 5 s Yawn L2 cache reference 7 s Long yawn Mutex lock/unlock 25 s Making a coffee ### Hour: Main memory reference 100 s Brushing your teeth Compress 1K bytes with Zippy 50 min One episode of a TV show (including ad breaks) ### Day: Send 2K bytes over 1 Gbps network 5.5 hr From lunch to end of work day ### Week SSD random read 1.7 days A normal weekend Read 1 MB sequentially from memory 2.9 days A long weekend Round trip within same datacenter 5.8 days A medium vacation Read 1 MB sequentially from SSD 11.6 days Waiting for almost 2 weeks for a delivery ### Year Disk seek 16.5 weeks A semester in university Read 1 MB sequentially from disk 7.8 months Almost producing a new human being The above 2 together 1 year ### Decade Send packet CA->Netherlands->CA 4.8 years Average time it takes to complete a bachelor's degree

Some time ago I came across What's the purpose of AsyncContext.start(...) in Servlet 3.0? question. Quoting the Javadoc of aforementioned method: Causes the container to dispatch a thread, possibly from a managed thread pool, to run the specified Runnable. To remind all of you, AsyncContext is a standard way defined in Servlet 3.0 specification to handle HTTP requests asynchronously. Basically HTTP request is no longer tied to an HTTP thread, allowing us to handle it later, possibly using fewer threads. It turned out that the specification provides an API to handle asynchronous threads in a different thread pool out of the box. First we will see how this feature is completely broken and useless in Tomcat and Jetty - and then we will discuss why the usefulness of it is questionable in general. Our test servlet will simply sleep for given amount of time. This is a scalability killer in normal circumstances because even though sleeping servlet is not consuming CPU, but sleeping HTTP thread tied to that particular request consumes memory - and no other incoming request can use that thread. In our test setup I limited the number of HTTP worker threads to 10 which means only 10 concurrent requests are completely blocking the application (it is unresponsive from the outside) even though the application itself is almost completely idle. So clearly sleeping is an enemy of scalability. @WebServlet(urlPatterns = Array("/*")) class SlowServlet extends HttpServlet with Logging { protected override def doGet(req: HttpServletRequest, resp: HttpServletResponse) { logger.info("Request received") val sleepParam = Option(req.getParameter("sleep")) map {_.toLong} TimeUnit.MILLISECONDS.sleep(sleepParam getOrElse 10) logger.info("Request done") } } Benchmarking this code reveals that the average response times are close to sleep parameter as long as the number of concurrent connections is below the number of HTTP threads. Unsurprisingly the response times begin to grow the moment we exceed the HTTP threads count. Eleventh connection has to wait for any other request to finish and release worker thread. When the concurrency level exceeds 100, Tomcat begins to drop connections - too many clients are already queued. So what about the the fancy AsyncContext.start() method (do not confuse with ServletRequest.startAsync())? According to the JavaDoc I can submit any Runnable and the container will use some managed thread pool to handle it. This will help partially as I no longer block HTTP worker threads (but still another thread somewhere in the servlet container is used). Quickly switching to asynchronous servlet: @WebServlet(urlPatterns = Array("/*"), asyncSupported = true) class SlowServlet extends HttpServlet with Logging { protected override def doGet(req: HttpServletRequest, resp: HttpServletResponse) { logger.info("Request received") val asyncContext = req.startAsync() asyncContext.setTimeout(TimeUnit.MINUTES.toMillis(10)) asyncContext.start(new Runnable() { def run() { logger.info("Handling request") val sleepParam = Option(req.getParameter("sleep")) map {_.toLong} TimeUnit.MILLISECONDS.sleep(sleepParam getOrElse 10) logger.info("Request done") asyncContext.complete() } }) } } We are first enabling the asynchronous processing and then simply moving sleep() into a Runnable and hopefully a different thread pool, releasing the HTTP thread pool. Quick stress test reveals slightly unexpected results (here: response times vs. number of concurrent connections): Guess what, the response times are exactly the same as with no asynchronous support at all (!) After closer examination I discovered that when AsyncContext.start() is called Tomcat submits given task back to... HTTP worker thread pool, the same one that is used for all HTTP requests! This basically means that we have released one HTTP thread just to utilize another one milliseconds later (maybe even the same one). There is absolutely no benefit of calling AsyncContext.start() in Tomcat. I have no idea whether this is a bug or a feature. On one hand this is clearly not what the API designers intended. The servlet container was suppose to manage separate, independent thread pool so that HTTP worker thread pool is still usable. I mean, the whole point of asynchronous processing is to escape the HTTP pool. Tomcat pretends to delegate our work to another thread, while it still uses the original worker thread pool. So why I consider this to be a feature? Because Jetty is "broken" in exactly same way... No matter whether this works as designed or is only a poor API implementation, using AsyncContext.start() in Tomcat and Jetty is pointless and only unnecessarily complicates the code. It won't give you anything, the application works exactly the same under high load as if there was no asynchronous logic at all. But what about using this API feature on correct implementations like IBM WAS? It is better, but still the API as is doesn't give us much in terms of scalability. To explain again: the whole point of asynchronous processing is the ability to decouple HTTP request from an underlying thread, preferably by handling several connections using the same thread. AsyncContext.start() will run the provided Runnable in a separate thread pool. Your application is still responsive and can handle ordinary requests while long-running request that you decided to handle asynchronously are processed in a separate thread pool. It is better, unfortunately the thread pool and thread per connection idiom is still a bottle-neck. For the JVM it doesn't matter what type of threads are started - they still occupy memory. So we are no longer blocking HTTP worker threads, but our application is not more scalable in terms of concurrent long-running tasks we can support. In this simple and unrealistic example with sleeping servlet we can actually support thousand of concurrent (waiting) connections using Servlet 3.0 asynchronous support with only one extra thread - and without AsyncContext.start(). Do you know how? Hint: ScheduledExecutorService. Postscriptum: Scala goodness I almost forgot. Even though examples were written in Scala, I haven't used any cool language features yet. Here is one: implicit conversions. Make this available in your scope: implicit def blockToRunnable[T](block: => T) = new Runnable { def run() { block } } And suddenly you can use code block instead of instantiating Runnable manually and explicitly: asyncContext start { logger.info("Handling request") val sleepParam = Option(req.getParameter("sleep")) map { _.toLong} TimeUnit.MILLISECONDS.sleep(sleepParam getOrElse 10) logger.info("Request done") asyncContext.complete() } Sweet!

In my last two blog posts, I showed examples of using Selenium WebDriver to capture screenshots, and running in a headless (no X-server) mode. This example combines the two solutions to capture screenshots inside a virtual display. To achieve this, I use a combination of Selenium WebDriver and pyvirtualdisplay (which uses xvfb) to run a browser in a virtual display and capture screenshots. the setup you need is: Selenium 2 Python bindings: PyPI pyvirtualdisplay Python package (depends on xvfb): PyPI On Debian/Ubuntu Linux systems, you can install everything with: $ sudo apt-get install python-pip xvfb xserver-xephyr $ sudo pip install selenium once you have it setup, the following code example should work: #!/usr/bin/env python from pyvirtualdisplay import Display from selenium import webdriver display = Display(visible=0, size=(800, 600)) display.start() browser = webdriver.Firefox() browser.get('http://www.google.com') browser.save_screenshot('screenie.png') browser.quit() display.stop() this will: launch a virtual display launch Firefox browser inside the virtual display navigate to google.com capture and save a screenshot close the browser stop the virtual display

First blogged about this on my previous blog site here: http://consultingblogs.emc.com/merrickchaffer/archive/2011/02/14/virtualization-in-wpf-with-virtualizingstackpanel.aspx However, having come across this again today on a project, I thought it was important enough to re-blog! Finally managed to figure out how to get virtualization to actually behave itself in a listbox wpf control. Turns out that in order for Virtualization to work, you need three things satisfied. Use a control that supports virtualization (e.g. list box or list view). (see Controls That Implement Performance Features section at bottom of this page for more info http://msdn.microsoft.com/en-us/library/cc716879.aspx#Controls ) Ensure that the ScrollViewer.CanContentScroll attached property is set to True on the containing list box / list view control. Ensure that either the list box has a height set, or that it is contained within a parent Grid row, where that row definition has a height set (Height="*" will do if you want it to occupy the Client window height). Note: Do not use height=”Auto” as this will not work, as this instructs WPF to simply size the row to the height needed to fit all the items of the list box in, hence you do not get the vertical scroll bar appearing. Ensure that there is no wrapping ScrollViewer control around the list box, as this will prevent virtualization from occuring. Ensure that you use a VirtualizingStackPanel in the ItemsPanelTemplate for the ListBox.ItemsPanel Example

Execute the slow integration tests separately from unit tests and show as much information about them as possible in Sonar.