DSRC | DSRC - DNA Sequence Reads Compressor

Veins 5.0 can be used to model wirelessly-networked road traffic (cars, trucks, bikes, trains, pedestrians). By default, Veins 5.0 includes models of DSRC, but it can be used together with projects like INET (for WiFi), SimuLTE (for 4G+ mobile broadband networking), or Veins VLC (for visible light communication). See http://veins.car2x.org/ for more information.

I think the question is getting a bit muddled here, so I'm going to attempt an answer. Understand that there are a lot of potential factors and my response is not intended to be an exhaustive review of Data Flow performance techniques.

First, let me sum up the project as I understand it. For each file in an ADLS folder, it appears you have the following:

1. Stored Procedure to truncate a Synapse staging table.
2. Copy activity to copy data from ADLS to Synapse staging table
3. DataFlow to read the data from the Synapse staging table, process it, and Write it back to a different Synapse table
4. Execute another pipeline to Archive the file.

From what I gather, it seems that this process is working. The question is in regards to the execution time of the Data Flow.

GENERAL performance guidelines to consider:

• Since you are running multiple DFs in succession, use a Custom Integration Runtime with ComputeOptimized type, 4 Cores, and Time To Live (TTL) greater than 0. [Not too long though, as you will pay for the environment to be active during the TTL time.] NOTE: last I was aware, DF requires the Region to be "Auto Resolve".

• Any time you WRITE to Synapse, make sure to define a Polybase Staging Storage Account:

• Be aware of cross-region operations: network Latency can be a real killer AND costs you money. For fastest performance, the Storage, Data Factory, and Synapse resources should all be in the same data center.

• Source and Sink partitioning CAN help with very large data sets and complex scenarios, but is a fairly tricky topic and (most likely) would not help in your scenario.

SPECIFIC to your posted scenario, I would consider redesigning the workflow. From a high level, you are doing the following for every (small) file:

1. Clearing a Synapse table
2. Writing from blob to Synapse
3. Reading the data you just wrote from Synapse
4. Writing the data back to Synapse [after some minimal processing].

My personal rule of thumb is to not use Data Flow to cross the Synapse boundary: if the operation is from Synapse to the same Synpase, then run the logic in Synapse. In other words, since the Source and Sink are both in Synapse, I would replace the Data Flow with a Stored Procedure.

Even better, I would consolidate the COPY activity and the Data Flow to a single Data Flow. The Data Flow can read the ADLS as a source, transform the data, and INSERT it to Synapse. This would remove the Staging table from the process. DF can even Archive the files after the operation:

Finally, I would seek to limit the number of Data Flow executions. Is there a reason you have to process this file by file? The Data Flow source could easily process the entire folder, even capturing the file name as a column if you have logic based on that value [see image above]. This would remove the need to run the Data Flow many times and could dramatically improve overall performance.

You need to change the image within the current block. In the following I have used jQuery closest to get the closest ancestor megamenu and changed the image based on that:

The original code uses 3 bytes for color, and one byte for alpha or padding, for 4 bytes per pixel. It reads 32-bit integers, and masks the colors out. You don't have that 4th byte, so you need to change things to read from unsigned chars.

Change dsrc and ddest to be an unsigned char *. The declaration for dsrc2 then becomes

I'm guessing you're just looking for entries that contain at least one space in between words. You can give this a try: "([\w%]+ [\w%]+(?: [\w%]+)*?)"

It looks like you're only trying to match letters and %, so using \w might be a bit dangerous (it also matches 0-9 and _). You can be more specific with "([A-Za-z%]+ [A-Za-z%]+(?: [A-Za-z%]+)*?)"

Also, note that \s doesn't just mean a space (). It's the set of all whitespace characters so it will also match newlines, carriage returns, and tabs. If you want to match a space, just use a literal space.

The key to the solution is to put the generated module where make expects to find it. CMake organizes the build tree in a non-trivial way.

The shortcomming in my added compilation in add_custom_command() was to believe that by default the binary will be stored in the "usual" CMake locations. Since I forge manually my compiler command, this is not the case.

I found the module in the source directory, which is a consequence of the WORKING_DIRECTORY option, with name ErrAux.o and not ErrAux.c.o.

To obtain the correct behavior, I force an output location with:

2>&1 doesn't mean "redirect stderr to console", it means "redirect stderr to the same place as stdout currently goes". If you do the redirections in the other order -- 2>&1 1>hello.i instead of 1>hello.i 2>&1 -- then you will get the effect I believe you are looking for.

You need to think of the redirections happening one at a time. If you say 1>hello.i 2>&1 then this happens:

• First, stdout is redirected to hello.i.
• Then stderr is redirected to the current destination of stdout, namely hello.i.

But if you say 2>&1 1>hello.i then this happens:

• First, stderr is redirected to the current destination of stdout, namely the console.
• Then stdout is redirected to hello.i.