Last Key Checkpoint
I'm trying to use visited checkpoints to load the correct level and place the player at the last visited checkpoint. For this I'm saving the key (level name) and value (checkpoint name) in a dictionary on config file.
Last Key Checkpoint
Thank you for your suggestion. If I understand correctly, you're suggesting a better way of save/load (json), but my main problem still stands: how to return the last key and value from a dictionary?
Godot's JSON serialization / parsing seems to keep them too. But I would advise you to use an Array instead of storing your checkpoint as a Dictionary, as you may lose key order at some point. It could be an Array of Dictionary of course.
The introduction of immune-checkpoint blockade in the cancer therapy led to a paradigm change of the management of late stage cancers. There are already multiple FDA approved checkpoint inhibitors and many other agents are undergoing phase 2 and early phase 3 clinical trials. The therapeutic indication of immune checkpoint inhibitors expanded in the last years, but still remains unclear who can benefit. MicroRNAs are small RNAs with no coding potential. By complementary pairing to the 3' untranslated region of messenger RNA, microRNAs exert posttranscriptional control of protein expression. A network of microRNAs directly and indirectly controls the expression of checkpoint receptors and several microRNAs can target multiple checkpoint molecules, mimicking the therapeutic effect of a combined immune checkpoint blockade. In this review, we will describe the microRNAs that control the expression of immune checkpoints and we will present four specific issues of the immune checkpoint therapy in cancer: (1) imprecise therapeutic indication, (2) difficult response evaluation, (3) numerous immunologic adverse-events, and (4) the absence of response to immune therapy. Finally, we propose microRNAs as possible solutions for these pitfalls. We consider that in the near future microRNAs could become important therapeutic partners of the immune checkpoint therapy.
If a reader disconnects from a partition, when it reconnects it begins reading at the checkpoint that was previously submitted by the last reader of that partition in that consumer group. When the reader connects, it passes the offset to the event hub to specify the location at which to start reading. In this way, you can use checkpointing to both mark events as "complete" by downstream applications, and to provide resiliency if a failover between readers running on different machines occurs. It's possible to return to older data by specifying a lower offset from this checkpointing process. Through this mechanism, checkpointing enables both failover resiliency and event stream replay.
If you are using Azure Blob Storage as the checkpoint store in an environment that supports a different version of Storage Blob SDK than those typically available on Azure, you'll need to use code to change the Storage service API version to the specific version supported by that environment. For example, if you are running Event Hubs on an Azure Stack Hub version 2002, the highest available version for the Storage service is version 2017-11-09. In this case, you need to use code to target the Storage service API version to 2017-11-09. For an example on how to target a specific Storage API version, see these samples on GitHub:
After an AMQP 1.0 session and link is opened for a specific partition, events are delivered to the AMQP 1.0 client by the Event Hubs service. This delivery mechanism enables higher throughput and lower latency than pull-based mechanisms such as HTTP GET. As events are sent to the client, each event data instance contains important metadata such as the offset and sequence number that are used to facilitate checkpointing on the event sequence.
An Ultra Shortcut is a special kind of shortcut that allows you to skip significant portions of the course by skipping key the 1st checkpoint to go to the last key checkpoint of the course. These aren't seen in certain speed run categories as they are considered glitches. Most of them require the use of a mushroom to do it.
As shown in the figure, every time the window slides over a source DStream,the source RDDs that fall within the window are combined and operated upon to produce theRDDs of the windowed DStream. In this specific case, the operation is applied over the last 3 timeunits of data, and slides by 2 time units. This shows that any window operation needs tospecify two parameters.
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 the any 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).
A streaming application must operate 24/7 and hence must be resilient to failures unrelatedto the application logic (e.g., system failures, JVM crashes, etc.). For this to be possible,Spark Streaming needs to checkpoint enough information to a fault-tolerant storage system such that it can recover from failures. There are two types of datathat are checkpointed.
To summarize, metadata checkpointing is primarily needed for recovery from driver failures,whereas data or RDD checkpointing is necessary even for basic functioning if statefultransformations are used.
Note that simple streaming applications without the aforementioned stateful transformations can berun without enabling checkpointing. The recovery from driver failures will also be partial inthat case (some received but unprocessed data may be lost). This is often acceptable and many runSpark Streaming applications in this way. Support for non-Hadoop environments is expectedto improve in the future.
Checkpointing can be enabled by setting a directory in a fault-tolerant,reliable file system (e.g., HDFS, S3, etc.) to which the checkpoint information will be saved.This is done by using streamingContext.checkpoint(checkpointDirectory). This will allow you touse the aforementioned stateful transformations. Additionally,if you want to make the application recover from driver failures, you should rewrite yourstreaming application to have the following behavior.
If the checkpointDirectory exists, then the context will be recreated from the checkpoint data.If the directory does not exist (i.e., running for the first time),then the function functionToCreateContext will be called to create a newcontext and set up the DStreams. See the Scala exampleRecoverableNetworkWordCount.This example appends the word counts of network data into a file.
If the checkpointDirectory exists, then the context will be recreated from the checkpoint data.If the directory does not exist (i.e., running for the first time),then the function contextFactory will be called to create a newcontext and set up the DStreams. See the Java exampleJavaRecoverableNetworkWordCount.This example appends the word counts of network data into a file.
If the checkpointDirectory exists, then the context will be recreated from the checkpoint data.If the directory does not exist (i.e., running for the first time),then the function functionToCreateContext will be called to create a newcontext and set up the DStreams. See the Python examplerecoverable_network_wordcount.py.This example appends the word counts of network data into a file.
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.
Configuring sufficient memory for the executors - Since the received data must be stored inmemory, the executors must be configured with sufficient memory to hold the received data. Notethat if you are doing 10 minute window operations, the system has to keep at least last 10 minutesof data in memory. So the memory requirements for the application depends on the operationsused in it.
Configuring checkpointing - If the stream application requires it, then a directory in theHadoop API compatible fault-tolerant storage (e.g. HDFS, S3, etc.) must be configured as thecheckpoint directory and the streaming application written in a way that checkpointinformation can be used for failure recovery. See the checkpointing sectionfor more details.
Configuring write-ahead logs - Since Spark 1.2,we have introduced write-ahead logs for achieving strongfault-tolerance guarantees. If enabled, all the data received from a receiver gets written intoa write-ahead log in the configuration checkpoint directory. This prevents data loss on driverrecovery, thus ensuring zero data loss (discussed in detail in theFault-tolerance Semantics section). This can be enabled by settingthe configuration parameterspark.streaming.receiver.writeAheadLog.enable to true. However, these stronger semantics maycome at the cost of the receiving throughput of individual receivers. This can be corrected byrunning more receivers in parallelto increase aggregate throughput. Additionally, it is recommended that the replication of thereceived data within Spark be disabled when the write-ahead log is enabled as the log is alreadystored in a replicated storage system. This can be done by setting the storage level for theinput stream to StorageLevel.MEMORY_AND_DISK_SER. While using S3 (or any file system thatdoes not support flushing) for write-ahead logs, please remember to enablespark.streaming.driver.writeAheadLog.closeFileAfterWrite andspark.streaming.receiver.writeAheadLog.closeFileAfterWrite. SeeSpark Streaming Configuration for more details.Note that Spark will not encrypt data written to the write-ahead log when I/O encryption isenabled. If encryption of the write-ahead log data is desired, it should be stored in a filesystem that supports encryption natively. 041b061a72