grap | grap : define and match graph patterns within binaries | Reverse Engineering library

Issu here is that you also grab the row with table headers that do not contain any . But you can simply slice them:

If your app works in iOS Safari, but not in iOS Chrome browser, the issue may be how Apple has Chrome configured.

From this reference

Google Chrome is now available for the iPhone and iPad, but before you get too excited, you need to realize that it isn't Chrome at all. It's Apple's Safari with a 'chrome' interface. The actual browser, the rendering, and javascript engine is 100% Apple Safari.

The reason is simple. Apple wants control and force people to create native apps, and are thus limiting the performance of web apps in third party apps. ... This is purely anti-competitor behavior that limits choice and forces people to create native apps.

When I write Progressive Web Apps (PWA), I have to tell my users that they have to use the OS native browser. Android=Chrome, iOS=Safari. Period. No substitutes. Apple clearly doesn’t want universal browser code functionality.

Note: I know that reference above is pretty old. But as far as I know the essence of the facts still stand. Note: I'm coming at this from the perspective of developing Progressive Web Apps. The one feature I really rely on is the ability to "Save link to home page". And that is just not available in Chrome on iOS, even now. Not sure you have exactly the same use case.

It easier to look into which tests are added with a feature to have an idea how it is working. Even though I did not find any benchmarks there are some regression test that gives an idea how this optimization working.

The related tests for this feature is in:

https://github.com/postgres/postgres/blob/master/src/test/regress/sql/expressions.sql

The MIN_ARRAY_SIZE_FOR_HASHED_SAOP threshold is 9 so you can observe the effects with 9 elements. Even though the optimization is not visible in explain output you can see its effect with some experimentation.

In the original post, there's already a post on function() vs function.

In summary (and in layman terms), though, there are 2 ways on how you handle onClick.

When you want to pass some value to the function

One of the issues is that partition is an overloaded term in Spark world and you're looking at 2 different kind of partitions:

• your dataset is organized as a Hive-partitioned table, where each partition is a separate directory named with = that may contain many data files inside. This is only useful for dynamically pruning the set of input files to read and has no effect on the actual RDD processing

• when Spark loads your data and creates a DataFrame/RDD, this RDD is organized in splits that can be processed in parallel and that are also called partitions.

df.rdd.getNumPartitions() returns the number of splits in your data and that is completely unrelated to your input table partitioning. It's determined by a number of config options but is mostly driven by 3 factors:

• computing parallelism: spark.default.parallelism in particular is the reason why you have 2 partitions in your RDD even though you don't have enough data to fill the first
• input size: spark will try to not create partitions bigger than spark.sql.files.maxPartitionBytes and thus may split a single multi-gigabyte parquet file into many partitions)
• shuffling: any operation that need to reorganize data for correct behavior (for example join or groupBy) will repartition your RDD with a new strategy and you will end up with many more partitions (governed by spark.sql.shuffle.partitions and AQE settings)

On the whole, you want to preserve this behavior since it's necessary for Spark to process your data in parallel and achieve good performance. When you use df.coalesce(1) you will coalesce your data into a single RDD partition but you will do your processing on a single core in which case simply doing your work in Pandas and/or Pyarrow would be much faster.

If what you want is to preserve the property on your output to have a single parquet file per Hive-partition attribute, you can use the following construct:

You are missing a (..) in module Graph(Graph(..), addVertex, addEdge, connectedVertex) where

An smaller equally illustrative example:

Module.hs:

Use:

As you are returning primitive value from selector so it won't make a difference if you use shallowCopy or not, by default it compares through strict === reference equality check

you can use shallowEquals when you select an object that might be similar in contents but different by reference.

for e.g

Try: