final class in java

A final class is simply a class that can’t be extended.

It’s useful if you for instance write a class which you intend to be immutable. (String does this for instance.) You do it mainly for efficiency and security reasons.

Executing SQL Statements from a Text File

Executing SQL Statements from a Text File

The mysql client typically is used interactively, like this:

shell> mysql db_name

However, it is also possible to put your SQL statements in a file and then tell mysql to read its input from that file. To do so, create a text file text_file that contains the statements you wish to execute. Then invoke mysql as shown here:

shell> mysql db_name < text_file

If you place a USE db_name statement as the first statement in the file, it is unnecessary to specify the database name on the command line:

shell> mysql < text_file

If you are already running mysql, you can execute an SQL script file using the source command or \. command:

mysql> source file_name
mysql> \. file_name

Sometimes you may want your script to display progress information to the user. For this you can insert statements like this:

SELECT '<info_to_display>' AS ' ';

The statement shown outputs <info_to_display>.

You can also invoke mysql with the --verbose option, which causes each statement to be displayed before the result that it produces.


A point of interest, or POI, is a specific point location that someone may find useful or interesting. An example is a point on the Earth representing the location of the Space Needle, or a point on Mars representing the location of the mountain, Olympus Mons. Most consumers use the term when referring to hotels, campsites, fuel stations or any other categories used in modern (automotive) navigation systems. The term is widely used in cartography, especially in electronic variants including GIS, and GPS navigation software. In this context the synonym waypoint is common. A GPS point of interest specifies, at minimum, the latitude and longitude of the POI, assuming a certain map datum. A name or description for the POI is usually included, and other information such as altitude or a telephone number may also be attached. GPS applications typically use icons to represent different categories of POI on a map graphically.[1]

POI collections[edit] Custom speed camera POI overlayed on a BMW navigation map Digital maps for modern GPS devices typically include a basic selection of POI for the map area.[2] However websites exist that specialize in the collection, verification, management and distribution of POI which end-users can load onto their devices to replace or supplement the existing POI. While some of these websites are generic, and will collect and categorize POI for any interest, others are more specialized in a particular category (such as speed cameras) or GPS device (e.g. TomTom/Garmin). End-users also have the ability to create their own custom collections. Commercial POI collections, especially those that ship with digital maps, or that are sold on a subscription basis are usually protected by copyright. However there are also many websites from which royalty-free POI collections can be obtained.

Applications[edit] The applications for POI are extensive. As GPS-enabled devices as well as software applications that use digital maps become more available, so too the applications for POI are also expanding. Newer digital cameras for example can automatically tag a photograph using Exif with the GPS location where a picture was taken; these pictures can then be overlaid as POI on a digital map or satellite image such as Google Earth. Geocaching applications are built around POI collections. In Vehicle tracking systems POIs are used to mark destination points and/or offices to that users of GPS tracking software would easily monitor position of vehicles according to POIs.


In communication networks, such as Ethernet or packet radio, throughput or network throughput is the average rate of successful message delivery over a communication channel. This data may be delivered over a physical or logical link, or pass through a certain network node. The throughput is usually measured in bits per second (bit/s or bps), and sometimes in data packets per second or data packets per time slot.

The system throughput or aggregate throughput is the sum of the data rates that are delivered to all terminals in a network.


DynamoDB differs from other Amazon services by allowing developers to purchase a service based on throughput, rather than storage

bloom filter

An empty Bloom filter is a bit array of m bits, all set to 0. There must also be k different hash functions defined, each of which maps or hashes some set element to one of the m array positions with a uniform random distribution.

To add an element, feed it to each of the k hash functions to get k array positions. Set the bits at all these positions to 1.

To query for an element (test whether it is in the set), feed it to each of the k hash functions to get k array positions. If any of the bits at these positions are 0, the element is definitely not in the set – if it were, then all the bits would have been set to 1 when it was inserted. If all are 1, then either the element is in the set, or the bits have by chance been set to 1 during the insertion of other elements, resulting in a false positive. In a simple bloom filter, there is no way to distinguish between the two cases, but more advanced techniques can address this problem.

While risking false positives, Bloom filters have a strong space advantage over other data structures for representing sets, such as self-balancing binary search trees, tries, hash tables, or simple arrays or linked lists of the entries. Most of these require storing at least the data items themselves, which can require anywhere from a small number of bits, for small integers, to an arbitrary number of bits, such as for strings (tries are an exception, since they can share storage between elements with equal prefixes). Linked structures incur an additional linear space overhead for pointers. A Bloom filter with 1% error and an optimal value of k, in contrast, requires only about 9.6 bits per element — regardless of the size of the elements. This advantage comes partly from its compactness, inherited from arrays, and partly from its probabilistic nature. The 1% false-positive rate can be reduced by a factor of ten by adding only about 4.8 bits per element.

However, if the number of potential values is small and many of them can be in the set, the Bloom filter is easily surpassed by the deterministic bit array, which requires only one bit for each potential element. Note also that hash tables gain a space and time advantage if they begin ignoring collisions and store only whether each bucket contains an entry; in this case, they have effectively become Bloom filters with k = 1.[2]

Bloom filters also have the unusual property that the time needed either to add items or to check whether an item is in the set is a fixed constant, O(k), completely independent of the number of items already in the set. No other constant-space set data structure has this property, but the average access time of sparse hash tables can make them faster in practice than some Bloom filters. In a hardware implementation, however, the Bloom filter shines because its k lookups are independent and can be parallelized.

To understand its space efficiency, it is instructive to compare the general Bloom filter with its special case when k = 1. If k = 1, then in order to keep the false positive rate sufficiently low, a small fraction of bits should be set, which means the array must be very large and contain long runs of zeros. The information content of the array relative to its size is low. The generalized Bloom filter (k greater than 1) allows many more bits to be set while still maintaining a low false positive rate; if the parameters (k and m) are chosen well, about half of the bits will be set, and these will be apparently random, minimizing redundancy and maximizing information content.