Java Code to Zip all folders in a particular folder.

A small utility code to create multiple zip files for all folders in the a particular folder.

for example

- c:/path/to/folder
    -> folder 1
    -> folder 2
    -> folder 3
    -> folder 4


- c:/path/to/folder
    -> folder 1
    -> folder 2
    -> folder 3
    -> folder 4
    -> folder
    -> folder
    -> folder
    -> folder

original source:

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LDAP Connector

Below is a sample code to perform LDAP Queries. Just modify the configuration information and then provide any valid query to get the search results.

You can also modify the code to get custom business logic as required.


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Sort a list of tuples by Nth item in Python

Suppose you have a list of tuples that looks something like this:

[('abc', 121),('abc', 231),('abc', 148), ('abc',221)]

And you want to sort this list in ascending order by the integer value inside the tuples.

We can achieve this using the key keyword with sorted().

sorted([('abc', 121),('abc', 231),('abc', 148), ('abc',221)], key=lambda x: x[1])

key should be a function that identifies how to retrieve the comparable element from your data structure. For example, the second element of the tuple, so we access [1].


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Configuring maximum number of simultaneous open WebSockets (for IE)

Given I’ve got this JS application. All client side JS communicating using web sockets. One page may have multiple web sockets open as many as 10-15.

Firefox and Chrome handle this many open web sockets at once just fine. IE11 seemed to have a limitation of 6 open web sockets at once.

Once I open that 7th web socket, regardless of socket call to the third-party server, I got an error thrown by IE, which closes the socket and gives the general error “SecurityError” and expanding the __proto__ section it gives me . The error seems to be pretty generic.

At first it looks as if there may be a trusted zone type issue with IE, but even after adding the client site to trusted zone as well as the server providing the data, the error still persisted.


This site shows the max connections settings for IE. It’s a registry setting in Windows that controls the amount of web socket connections.

Interestingly enough, By default, windows doesn’t have that registry key, but there is still a limit. Therefore you have to add the Registry in order for this to work.

The MSDN Section mentions:

WebSocket Maximum Server Connections

Internet Explorer 10. When enabled, the FEATURE_WEBSOCKET_MAXCONNECTIONSPERSERVER feature sets the maximum number of concurrent WebSocket connections allowed to a single host. The minimum number that can be specified is 2 and the maximum value allowed is 128.

The default value for this setting is 6 in Internet Explorer and applications hosting the WebBrowser Control. To modify this feature by using the registry, add the name of your executable file to the following setting.

         Internet Explorer
                     contoso.exe = (DWORD) 0x00000006 (6)

This should fix the problem.



Posted in AJAX, Browsers, Client Side Programming, HTML, IE, IE problems, JavaScript, jQuery, Windows | Tagged , , , , , , , , , | Leave a comment

Best way to select random rows PostgreSQL

Given, you have a very large table with 500 Million rows, and you have to select some random 1000 rows out of the table and you want it to be fast.

Given the specifications:

  • You assumed to have a numeric ID column (integer numbers) with only few (or moderately few) gaps.
  • Ideally no or few write operations.
  • Your ID column should have been indexed! A primary key serves nicely.

The query below does not need a sequential scan of the big table, only an index scan.

First, get estimates for the main query:

SELECT count(*) AS ct              -- optional
     , min(id)  AS min_id
            , max(id)  AS max_id
            , max(id) - min(id) AS id_span
FROM   big;

The only possibly expensive part is the count(*) (for huge tables). You will get an estimate, available at almost no cost (detailed explanation here):

SELECT reltuples AS ct FROM pg_class WHERE oid = 'schema_name.big'::regclass;

As long as ct isn’t much smaller than id_span, the query will outperform most other approaches.

WITH params AS (
    SELECT 1       AS min_id           -- minimum id <= current min id
         , 5100000 AS id_span          -- rounded up. (max_id - min_id + buffer)
    SELECT p.min_id + trunc(random() * p.id_span)::integer AS id
    FROM   params p
          ,generate_series(1, 1100) g  -- 1000 + buffer
    GROUP  BY 1                        -- trim duplicates
    ) r
JOIN   big USING (id)
LIMIT  1000;                           -- trim surplus
  • Generate random numbers in the id space. You have “few gaps”, so add 10 % (enough to easily cover the blanks) to the number of rows to retrieve.
  • Each id can be picked multiple times by chance (though very unlikely with a big id space), so group the generated numbers (or use DISTINCT).
  • Join the ids to the big table. This should be very fast with the index in place.
  • Finally trim surplus ids that have not been eaten by dupes and gaps. Every row has a completely equal chance to be picked.

Short version

You can simplify this query. The CTE in the query above is just for educational purposes:

    SELECT DISTINCT 1 + trunc(random() * 5100000)::integer AS id
    FROM   generate_series(1, 1100) g
    ) r
JOIN   big USING (id)
LIMIT  1000;

Refine with rCTE

Especially if you are not so sure about gaps and estimates.

WITH RECURSIVE random_pick AS (
   FROM  (
      SELECT 1 + trunc(random() * 5100000)::int AS id
      FROM   generate_series(1, 1030)  -- 1000 + few percent - adapt to your needs
      LIMIT  1030                      -- hint for query planner
      ) r
   JOIN   big b USING (id)             -- eliminate miss

   UNION                               -- eliminate dupe
   SELECT b.*
   FROM  (
      SELECT 1 + trunc(random() * 5100000)::int AS id
      FROM   random_pick r             -- plus 3 percent - adapt to your needs
      LIMIT  999                       -- less than 1000, hint for query planner
      ) r
   JOIN   big b USING (id)             -- eliminate miss
FROM   random_pick
LIMIT  1000;  -- actual limit

We can work with a smaller surplus in the base query. If there are too many gaps so we don’t find enough rows in the first iteration, the rCTE continues to iterate with the recursive term. We still need relatively few gaps in the ID space or the recursion may run dry before the limit is reached – or we have to start with a large enough buffer which defies the purpose of optimizing performance.

Duplicates are eliminated by the UNION in the rCTE.

The outer LIMIT makes the CTE stop as soon as we have enough rows.

This query is carefully drafted to use the available index, generate actually random rows and not stop until we fulfill the limit (unless the recursion runs dry). There are a number of pitfalls here if you are going to rewrite it.

Wrap into function

For repeated use with varying parameters:

CREATE OR REPLACE FUNCTION f_random_sample(_limit int = 1000, _gaps real = 1.03)
   _surplus  int := _limit * _gaps;
   _estimate int := (           -- get current estimate from system
      SELECT c.reltuples * _gaps
      FROM   pg_class c
      WHERE  c.oid = 'big'::regclass);

   WITH RECURSIVE random_pick AS (
      SELECT *
      FROM  (
         SELECT 1 + trunc(random() * _estimate)::int
         FROM   generate_series(1, _surplus) g
         LIMIT  _surplus           -- hint for query planner
         ) r (id)
      JOIN   big USING (id)        -- eliminate misses

      UNION                        -- eliminate dupes
      SELECT *
      FROM  (
         SELECT 1 + trunc(random() * _estimate)::int
         FROM   random_pick        -- just to make it recursive
         LIMIT  _limit             -- hint for query planner
         ) r (id)
      JOIN   big USING (id)        -- eliminate misses
   FROM   random_pick
   LIMIT  _limit;
$func$  LANGUAGE plpgsql VOLATILE ROWS 1000;


SELECT * FROM f_random_sample();
SELECT * FROM f_random_sample(500, 1.05);

You could even make this generic to work for any table: Take the name of the PK column and the table as polymorphic type and use EXECUTE … But that’s beyond the scope of this post. See:

Possible alternative

IF your requirements allow identical sets for repeated calls (and we are talking about repeated calls) I would consider a materialized view. Execute above query once and write the result to a table. Users get a quasi random selection at lightening speed. Refresh your random pick at intervals or events of your choosing.

Postgres 9.5 introduces TABLESAMPLE SYSTEM (n)

It’s very fast, but the result is not exactly random. The manual:

The SYSTEM method is significantly faster than the BERNOULLI method when small sampling percentages are specified, but it may return a less-random sample of the table as a result of clustering effects.

And the number of rows returned can vary wildly. For our example, to get roughly 1000 rows, try:

SELECT * FROM big TABLESAMPLE SYSTEM ((1000 * 100) / 5100000.0);

Where n is a percentage. The manual:

The BERNOULLI and SYSTEM sampling methods each accept a single argument which is the fraction of the table to sample, expressed as a percentage between 0 and 100. This argument can be any real-valued expression.

Bold emphasis mine.



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K random combinations of N elements in List in Java

Given a List of N Strings, generate and print all possible combinations of R elements in array and return X random combinations from the result. Following is the code for implementing it:

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Convert Comma separated String to Rows in Oracle SQL

Many times we need to convert a comma separated list of terms in a single string and convert it rows in SQL query.

for example

 India, USA, Russia, Malaysia, Mexico

Needs to be converted to:


The following SQL script can help in this:

just replace the required values with your string and your delimiter.

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Apache Ignite: What is Ignite?

Apache Ignite(TM) In-Memory Data Fabric is a high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash-based technologies.



You can view Ignite as a collection of independent, well-integrated, in-memory components geared to improve performance and scalability of your application. Some of these components include:

Apache Ignite APIs

Apache Ignite has a reach set of APIs that are covered throughout the documentation. The APIs are implemented in a form of native libraries for such major languages and technologies as Java, .NET and C++ and by supporting a variety of protocols like REST, Memcached or Redis.

The documentation that is located under this domain is mostly related to Java. Refer to the following documentation sections and domains to learn more about alternative technologies and protocols you can use to connect to and work with an Apache Ignite cluster:

Fork It on GIT

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Fetch GET parameters in JS/jQuery

If you have a URL with some GET parameters as follows: 

and need to get the values of each parameters then below is a nifty piece of code solving your requirement.

JavaScript has nothing built in for handling query string parameters.

You could access, which would give you from the ? character on to the end of the URL or the start of the fragment identifier (#foo), whichever comes first.

You can then access QueryString.c

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HackerRank: Repeated String


Lilah has a string, s, of lowercase English letters that she repeated infinitely many times.

Given an integer, n, find and print the number of letter a‘s in the first letters of Lilah’s infinite string.

Input Format

The first line contains a single string, s.
The second line contains an integer, n.


  • 1<=|s|<=100
  • 1<=|n|<=10^12
  • For 25% of the test cases, n <= 10^6

Output Format

Print a single integer denoting the number of letter a’s in the first letters of the infinite string created by repeating infinitely many times.

Sample Input 0


Sample Output 0


Explanation 0

The first n = 10 letters of the infinite string are abaabaabaa. Because there are 7 a‘s, we print on a new line.

Sample Input 1


Sample Output 1


Explanation 1

Because all of the first n=1000000000000 letters of the infinite string are a, we print 1000000000000 on a new line.


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