Maybe you also edit the tile cache size. See here:

 Slower snappy processing python No, I think the will not improve the performance anymore. But maybe if you increase the size of the tile cache. Change the property 'snap.jai.tileCacheSize' in snap.properties in the etc folder of the SNAP installation directory. A good value might be ~70% of the java_max_mem value

I work with osnaps turned on, and hold alt to temporarily disable. However I work with smooth gumball, since I usually use it to nudge things around. Occasionally I turn on snappy dragging, but holding alt would be simpler and expected behaviour.

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I prefer working with smooth dragging but sometimes I think it would be useful to quickly change to snappy dragging using a shortcut. I imagine this to be a button one could press while dragging already. Similar to pressing alt to temporarily turn off the object snaps or pressing shift to temporarily turn Ortho off/on.

Before everyone rushes to mark this as a duplicate, I have indeed read the other threads here and here, googled this, and visited the snappy homepage here searching to clear my confusion, and I couldn't. I ask here because I still remain confused. I understand that it is some sort of a minimal working ubuntu, and the spirit of this question is not what is "in it", rather what it is and how it works.

Will regular ubuntu applications work if I install snappy (the same thing that works on my normal ubuntu install using apt-get or by downloading a deb package), or does someone need to do something special to make an app work in snappy?

apt-get and other utilities for working with traditional .deb packages do not work to install and upgrade applications on a Snappy Ubuntu Core system. Instead you use the snappy utility. See the Snappy Tour for details.

And to install updates, you run sudo snappy update-versions (which corresponds to sudo apt-get update on a normal Ubuntu system), snappy versions to see what's newly available, and sudo snappy update ... to specify packages for updating (put their names in place of ...).

Like in just about any OS, you could manually install programs on Snappy Ubuntu Core provided you have or can obtain all the libraries (and any other dependencies) they need. However, just as the best way to install most software on regular Ubuntu systems is with a .deb package, the best way to install software on Snappy Ubuntu Core is with specially built snappy packages. Only a handful of these exist so far.

Snappy is cloud-oriented ("Snappy Ubuntu Core is the perfect system for large-scale cloud container deployments..."), and explicitly supports many x86 (i.e., not ARM) platforms--see the list of options under "Try the new, snappy Ubuntu yourself!" on the Snappy home page.

Therefore, you have a newer version of the snappy package installed, which is causing this issue.Hadoop requires the snappy-devel package that is a lower version that what is on the machine already. Run the following on the host and retry.

Note: The following examples assume that you work with SNAP 10 and the new esa_snappy interface. Differences to the previous snappy interface are outlined in the text or in comment lines in the Python code snippets.

For a guideline how to access, install and configure Python for SNAP please see Configure Python to use the new SNAP-Python (esa_snappy) interface (SNAP version 10+) for the most recent SNAP 10, or Configure Python to use the SNAP-Python (snappy) interface (SNAP versions

It makes sense to start exploring the capabilities of esa_snappy by reading a product and try out the methods and fields it contains. Calling get on an object in esa_snappy (e.g. p.getRasterHeight()) returns either an Integer or String value or an object. Upon the latter, you can again normally call get again. This might again return an object with fields to return.

In the other direction, you may also set fields. For instance, if you called get and received a String value, you may set the same field of this object with a String.

SNAP Operators are available in snappy via GPF.createProduct(). Its first parameter is a String denoting the name of the Operator as denoted in the Engine and available via GPT. If you have added GPT to your environment variables, you may call GPT from cmd in order to check out the available Operators, their description and parameters. In snappy, we provide the parameters through the second parameter of GPF.createProduct(). This parameter is a Java Hashmap, an object that is equivalent to a Python dictionary. The parameters must be named exactly with the String parameter name provided in GPT.

I have an requirement to load hadoop snappy compressed avro file to Big query.

Saw from Google docs that big query detects snappy compression. But when I tried

bq load --source-format=AVRO project:dataset.table gs://mybucket/inputsnappy.snappy

I got "Apache Avro library failed to parse the header with the following error: Invalid data file. Magic does not match" error.

Any input on this will really help.

Also Google doc says only compression on data blocks can be detected by bigquery. Can some one help me understand that point on data blocks.

I also tried converting snappy to avro using python snappy. But iam getting error when doing "decompressed_data= snappy.decompress(input_data)"

Error :Uncompress:invalid input file.

Not sure how to proceed now.

The reason why your installation via the QGIS python interpreter fails could be that there is another package called snappy ( ) which is used when you use pip for example. Sometimes it is already installed with QGIS which causes additional problems.It works when you use the full path to the QGIS python.exe in the command line as described in the ESA documentation you posted.

If you search for an alternative to snappy, you can use snapista. -contrib.github.io/snapista/It is easy to use and directly calls the SNAP graph processing tool (gpt), so it's also computationally more efficient.

Snappy's main target is very high-speed compression/decompression with reasonable compression size. So the compression ratio of snappy-java is modest and about the same as LZF (ranging 20%-100% according to the dataset).

See the installation instruction. Building from the source code is an option when your OS platform and CPU architecture is not supported. To build snappy-java, you need Git, JDK (1.6 or higher), Maven (3.x or higher is required), g++ compiler (mingw in Windows) etc.

The Makefile contains rules for cross-compiling the native library for other platforms so that the snappy-java JAR can support multiple platforms. For example, to build the native libraries for x86 Linux, x86 and x86-64 Windows, and soft- and hard-float ARM:

Simply put the snappy-java's jar to WEB-INF/lib folder of your web application. Usual JNI-library specific problem no longer exists since snappy-java version 1.0.3 or higher can be loaded by multiple class loaders in the same JVM by using native code injection to the parent class loader.

In order to see if Snappy has been found by Picard, pass -Dsnappy.loader.verbosity=true on the Java command line. When SortingCollection first tries to sort something, a message will be printed to stderr indicating whether or not Snappy-java and Snappy DLL have been found. be457b7860

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