try {
threshold : 0, // You can set threshold on how close to the edge ad should come before it is loaded. Default is 0 (when it is visible).
forceLoad : false, // Ad is loaded even if not visible. Default is false.
onLoad : false, // Callback function on call ad loading
onComplete : false, // Callback function when load is loaded
timeout : 1500, // Timeout ad load
debug : false, // For debug use : draw colors border depends on load status
xray : false // For debug use : display a complete page view with ad placements
}) ;
catch (exception){
console.log(“error loading lazyload_ad ” + exception);

Databricks, the main commercial backer for , has released , an open source storage layer for Spark that provides ACID transactions and other data-management functions for machine learning and other big data work.

Many kinds of data work need features like ACID transactions or schema enforcement for consistency, metadata management for security, and the ability to work with discrete versions of data. Features like those don’t come standard with every data source out there, so Delta Lake provides those features for any Spark DataFrame data source.

Delta Lake can be used as a drop-in replacement to access storage systems like HDFS. Data ingested into Spark through Delta Lake is stored in Parquet format in a cloud storage service of your choice. Devlopers can use their choice of Java, Python, or Scala to access Delta Lake’s API set.

Delta Lake supports most of the existing Spark SQL DataFrame functions for reading and writing data. It also supports Spark Structured Streaming as a source or destination, although not the DStream API. Every read and write through Delta Lake has an ACID transaction guarantee, so that multiple writers will have their writes serialized and multiple readers will see consistent snapshots.

Reading a specific version of a data set—what the Delta Lake documentation calls “time travel”—works by simply reading a DataFrame with an associated time stamp or version ID. Delta Lake also ensures the schema of the DataFrame being written matches the table it’s being written to; if there’s a mismatch, it throws an exception rather than change the schema. (Spark’s file APIs will replace the table in such a case.)

Future releases of Delta Lake may support more of Spark’s public API set, although DataFrameReader/Writer are the main focus for now.