Batch Download Google Docs
Catharina Dell
For example, suppose you call batchUpdate with four updates, and only the third one returns information. The response would have two empty replies, the reply to the third request, and another empty reply, in that order.
Note that while @batch doesn't allow mounting arbitrary disk volumes on the fly, you can create in-memory filesystems easily with tmpfs options. For more details, see using metaflow.S3 for in-memory processing.
For the batch read endpoint, you can also use the optional idProperty parameter to retrieve contacts by email or a custom unique identifier property. By default, the id values in the request refer to the record ID (hs_object_id), so the idProperty parameter is not required when retrieving by record ID. If you're using email or a custom unique value property to retrieve contacts, you must include the idProperty parameter.
Internally, it works as follows. Spark Streaming receives live input data streams and dividesthe data into batches, which are then processed by the Spark engine to generate the finalstream of results in batches.
The words DStream is further mapped (one-to-one transformation) to a DStream of (word,1) pairs, which is then reduced to get the frequency of words in each batch of data.Finally, wordCounts.pprint() will print a few of the counts generated every second.
First, we import the names of the Spark Streaming classes and some implicitconversions from StreamingContext into our environment in order to add useful methods toother classes we need (like DStream). StreamingContext is themain entry point for all streaming functionality. We create a local StreamingContext with two execution threads, and a batch interval of 1 second.
The words DStream is further mapped (one-to-one transformation) to a DStream of (word,1) pairs, which is then reduced to get the frequency of words in each batch of data.Finally, wordCounts.print() will print a few of the counts generated every second.
First, we create aJavaStreamingContext object,which is the main entry point for all streamingfunctionality. We create a local StreamingContext with two execution threads, and a batch interval of 1 second.
The words DStream is further mapped (one-to-one transformation) to a DStream of (word,1) pairs, using a PairFunctionobject. Then, it is reduced to get the frequency of words in each batch of data,using a Function2 object.Finally, wordCounts.print() will print a few of the counts generated every second.
For testing a Spark Streaming application with test data, one can also create a DStream based on a queue of RDDs, using streamingContext.queueStream(queueOfRDDs). Each RDD pushed into the queue will be treated as a batch of data in the DStream, and processed like a stream.
In every batch, Spark will apply the state update function for all existing keys, regardless of whether they have new data in a batch or not. If the update function returns None then the key-value pair will be eliminated.
The transform operation (along with its variations like transformWith) allowsarbitrary RDD-to-RDD functions to be applied on a DStream. It can be used to apply any RDDoperation that is not exposed in the DStream API.For example, the functionality of joining every batch in a data streamwith another dataset is not directly exposed in the DStream API. However,you can easily use transform to do this. This enables very powerful possibilities. For example,one can do real-time data cleaning by joining the input data stream with precomputedspam information (maybe generated with Spark as well) and then filtering based on it.
Note that the supplied function gets called in every batch interval. This allows you to dotime-varying RDD operations, that is, RDD operations, number of partitions, broadcast variables,etc. can be changed between batches.
Here, in each batch interval, the RDD generated by stream1 will be joined with the RDD generated by stream2. You can also do leftOuterJoin, rightOuterJoin, fullOuterJoin. Furthermore, it is often very useful to do joins over windows of the streams. That is pretty easy as well.
In fact, you can also dynamically change the dataset you want to join against. The function provided to transform is evaluated every batch interval and therefore will use the current dataset that dataset reference points to.
Finally, this can be further optimized by reusing connection objects across multiple RDDs/batches.One can maintain a static pool of connection objects than can be reused asRDDs of multiple batches are pushed to the external system, thus further reducing the overheads.
You can also run SQL queries on tables defined on streaming data from a different thread (that is, asynchronous to the running StreamingContext). Just make sure that you set the StreamingContext to remember a sufficient amount of streaming data such that the query can run. Otherwise the StreamingContext, which is unaware of any of the asynchronous SQL queries, will delete off old streaming data before the query can complete. For example, if you want to query the last batch, but your query can take 5 minutes to run, then call streamingContext.remember(Minutes(5)) (in Scala, or equivalent in other languages).
Note that checkpointing of RDDs incurs the cost of saving to reliable storage.This may cause an increase in the processing time of those batches where RDDs get checkpointed.Hence, the interval ofcheckpointing needs to be set carefully. At small batch sizes (say 1 second), checkpointing everybatch may significantly reduce operation throughput. Conversely, checkpointing too infrequentlycauses the lineage and task sizes to grow, which may have detrimental effects. For statefultransformations that require RDD checkpointing, the default interval is a multiple of thebatch interval that is at least 10 seconds. It can be set by usingdstream.checkpoint(checkpointInterval). Typically, a checkpoint interval of 5 - 10 sliding intervals of a DStream is a good setting to try.
If the batch processing time is consistently more than the batch interval and/or the queueingdelay keeps increasing, then it indicates that the system isnot able to process the batches as fast they are being generated and is falling behind.In that case, considerreducing the batch processing time.
There are a number of optimizations that can be done in Spark to minimize the processing time ofeach batch. These have been discussed in detail in the Tuning Guide. This sectionhighlights some of the most important ones.
An alternative to receiving data with multiple input streams / receivers is to explicitly repartitionthe input data stream (using inputStream.repartition()).This distributes the received batches of data across the specified number of machines in the clusterbefore further processing.
In specific cases where the amount of data that needs to be retained for the streaming application is not large, it may be feasible to persist data (both types) as deserialized objects without incurring excessive GC overheads. For example, if you are using batch intervals of a few seconds and no window operations, then you can try disabling serialization in persisted data by explicitly setting the storage level accordingly. This would reduce the CPU overheads due to serialization, potentially improving performance without too much GC overheads.
For a Spark Streaming application running on a cluster to be stable, the system should be able toprocess data as fast as it is being received. In other words, batches of data should be processedas fast as they are being generated. Whether this is true for an application can be found bymonitoring the processing times in the streaming web UI, where the batchprocessing time should be less than the batch interval.
Depending on the nature of the streamingcomputation, the batch interval used may have significant impact on the data rates that can besustained by the application on a fixed set of cluster resources. For example, let usconsider the earlier WordCountNetwork example. For a particular data rate, the system may be ableto keep up with reporting word counts every 2 seconds (i.e., batch interval of 2 seconds), but notevery 500 milliseconds. So the batch interval needs to be set such that the expected data rate inproduction can be sustained.
CMS Garbage Collector: Use of the concurrent mark-and-sweep GC is strongly recommended for keeping GC-related pauses consistently low. Even though concurrent GC is known to reduce theoverall processing throughput of the system, its use is still recommended to achieve moreconsistent batch processing times. Make sure you set the CMS GC on both the driver (using --driver-java-options in spark-submit) and the executors (using Spark configuration spark.executor.extraJavaOptions).
When data is received from a stream source, the receiver creates blocks of data. A new block of data is generated every blockInterval milliseconds. N blocks of data are created during the batchInterval where N = batchInterval/blockInterval. These blocks are distributed by the BlockManager of the current executor to the block managers of other executors. After that, the Network Input Tracker running on the driver is informed about the block locations for further processing.
An RDD is created on the driver for the blocks created during the batchInterval. The blocks generated during the batchInterval are partitions of the RDD. Each partition is a task in spark. blockInterval== batchinterval would mean that a single partition is created and probably it is processed locally.
Question:Is there any suggestions for an automatic method to to convert the documents to html with the appropriate image tags and links to the images in them, and export/package the images for ftp upload? I can already convert them to HTML automatically using a batch file and a program, but converting the images to the correct tags with href link, then exporting them for ftp is where i need some help.
An iterable-style dataset is an instance of a subclass of IterableDatasetthat implements the __iter__() protocol, and represents an iterable overdata samples. This type of datasets is particularly suitable for cases whererandom reads are expensive or even improbable, and where the batch size dependson the fetched data.
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