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PostgreSQL has a large object facility, which provides stream-style access to user data that is stored in a special large-object structure. Streaming access is useful when working with data values that are too large to manipulate conveniently as a whole.

This chapter describes the implementation and the programming and query language interfaces to PostgreSQL large object data. We use the libpq C library for the examples in this chapter, but most programming interfaces native to PostgreSQL support equivalent functionality. Other interfaces might use the large object interface internally to provide generic support for large values. This is not described here.

This is thread-safe of course and also has a side-benefit for caching: since Pile makes a new instance on every Get(), you can safely give-out a copy of object for requesting thread, even if they compete for the same object (same PilePointer). This is like an in functional languages when a worker takes the order, mutates it into something new then passes along the chain - no need to lock().

An important note to keep in mind: When you compare the test results from below against, say Redis or Memcached, ask yourself a question: am I comparing apples to apples? Does your API give you back a .NET object or a string you need to parse somehow to access ? If it gives you a string or some other form of data, please do not forget to account for serialization time to convert the raw data into a usable .NET object. In other words, a string nameFrank Drebinsalary does not allow you to access without parsing. Pile returns you a real CLR object - nothing to parse, so the ser/deser time is already accounted for in the numbers below. When Pile stores byte[] or strings, its performance needs to be multiplied at least five, if not ten, times.

Stable operation with 10 million objects in RAM before slowdowns start

Speed deteriorates (SpeeDet) after: 25 million objects added

Writes: average 0.8 million objects / sec (starts at millions+/sec then slows down significantly after SpeeDet), interrupted by GC pauses

Reads while writing: 1 million objects / sec interrupted by GC pauses

Stable operation with 1,000 million serialized objects in RAM (yes, ONE BILLION objects)

Slow down after: 600 million objects (as they stop fitting in 64 GB physical RAM)

Writes: 0.5 million objects / sec without interruptions

Reads while writing: 0.7 million objects / sec without interruptions

Garbage Collector stop-all pauses at 600M objects: none

But when you think about it, it really isnt handle tens of millions of objects if they stick around.

A Big Memory Pile Cache is abstracted into the interface that supports priority, maximum age, absolute expiration timestamps and memory limits. When you approach a limit, the object starts to get overwritten if their priorities allow.

The cache evicts old data (auto-delete) and expires objects at a certain timestamp if it was set. It also clones detailed table settings from a cache-wide setup where everything is configurable (index size, LWM, HWM, grow/shrink percentages etc.).

Here you have it. 1+ billion objects are allocated on a Pile. The write throughput is now around a paltry 300,000 inserts a second from 10 threads. This is because we have also allocated 80+GB on a 64 GB machine. Actually, I was surprised swapping did not kill it altogether, it still works, AND full-scan GC is 11 ms! This machine is 3 Ghz 6 core i7 with 64GB physical running Windows 7 64bit.

This section introduces different types of data that you encounter when developing applications and discusses which kinds of data are suitable for large objects.

This kind of data is complex in nature and is suited for the object?relational features of the Oracle database such as collections, references, and user-defined types.

Large objects are suitable for these last two kinds of data: semi-structured data and unstructured data. Large objects features allow you to store these kinds of data in the database as well as in operating system files that are accessed from the database.

Binary File objects (BFILE datatypes) can also store character data. You can use BFILEs to load read-only data from operating system files into CLOB or NCLOB instances that you then manipulate in your application.

LOBs can also be object attributes.

Persistent LOBs use copy semantics and participate in database transactions. You can recover persistent LOBs in the event of transaction or media failure, and any changes to a persistent LOB value can be committed or rolled back. In other words, all the Atomicity Consistency Isolation Durability (ACID) properties that pertain to using database objects pertain to using persistent LOBs.

External LOBs are data objects stored in operating system files, outside the database tablespaces. The database accesses external LOBs using the SQL datatype BFILE. The BFILE datatype is the only external LOB datatype.

Data that is loaded into other large object types, such as a BLOB or CLOB where the data can then be manipulated.

What if your objects are too big or awkward to teach to a robot vision system?

Why isn`t it easy to teach every object every time?

Modern robot vision systems are extremely flexible. You can use them to detect a huge array of different object types, sizes, and shapes. Whether you are detecting circuit boards in a pick-and-place application, detecting parts for a machine tending application, or detecting boxes for a palletizing application, you can probably use robot vision.

Robot vision algorithms can be taught to recognize almost any object that shows up as a clear, distinct image in the camera view…

Sometimes, you are working with objects that are too big, too awkward, and too strangely shaped to be easily detected by robot vision. You know that the robot has the capacity to manipulate the objects but the vision system just doesn`t want to play ball.

Or are you restricted to only using small objects that fit easily with the camera view and have straight, regular outlines?

However, just because they are good for demonstrations doesn`t mean that you are restricted to using small, regular objects in your applications. A good robot vision system can handle a diverse range of objects, including those that might be considered

Some cutting-edge solutions for picking awkward objects involve using complex cloud-based machine learning algorithms. But, you don`t need a complex setup to have a robust robot vision system that can handle diverse objects.

You just need to understand why some objects are harder for a robot vision system to detect than others.

The problem with big, awkward objects is that they can be challenging to teach to the vision system.

Big objects might not fit completely into the camera view or might take up too much of the view. Although you only need to detect part of the object for a usable detection, if a different part of the object shows up in the camera every time, the robot vision won`t be able to recognize that it is the same object.

Awkward objects with hard-to-teach edges can also be difficult to teach reliably to the robot vision system. Maybe the edges look different depending on the orientation of the object or lighting variations cause reflections on the material that changes how the object looks in the camera.

You can implement solutions to handle each of these factors individually — e.g. changing the lighting, adding a new background, implementing systems to avoid overlapping objects — and some of these may be necessary for your situation.

You can make one simple change to your robot programming to drastically improve the teaching of big, awkward objects. It makes the vision system able to detect the object even if only part of it is visible in the camera view and it is robust to changes in object appearance.

The real issue with big, awkward objects comes during the teaching stage of the robot vision.

So, all we have to do to overcome this problem is to change how we teach objects to the system!

This trick involves using a 2D CAD model of your object instead of the object itself to train the vision algorithm. Instead of taking a photo of the object — as is the normal teaching method — you just load the CAD file into the robot`s teach pendant.

During the detection phase, the algorithm will use this CAD model to detect instances of the object in the image.

What big or awkward objects have you had trouble with? Tell us in the comments below or join the discussion on LinkedIn, Twitter, Facebook or the DoF professional robotics community.

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