I Have a requirement where I need to ingest continuous/steam data(Json format) from eventHub to Azure data lake.
I want to follow the layered approach(raw, clean, prepared) to finally store data into delta table.
My doubt is around the raw layer.
out of below two approach which one do you recommend is best.
Event hub -> RawLayer(Raw Json Format) -> cleanLayer (delta table) -> preparedLayer(delta table)
Event hub -> RawLayer(delta table) -> cleanLayer (delta table) -> preparedLayer(delta table)
so shall I store the raw Json format in raw layer or its suggested to create delta table in Raw layer is well.
Regards,
I will let others debate the theoretical approaches.
From a practical standpoint, here are the most common ways to write to disk from Event Hub:
Event Hub Capture, dumps files to a storage account directly from an event hub, but the format is AVRO. This is not practical, but it is the "rawest" form your records can take. If I remember correctly your payload is encoded in base64 and embedded in a common schema. They have guidance on how to extract your data in Spark.
Azure Stream Analytics can output to JSON or parquet. In both cases, events are actually going through a deserialization / serialization process that can't be bypassed. This means the output will look raw (at least in the JSON case) but won't really be. In this scenario ASA should be seen as a streaming ETL/ETL. Don't use it (and pay for it) if you're not actively using its features (transformation, cleaning, enrichment...). Note that ASA doesn't support delta lake as an output yet - so you will still need some post processing to ingest the generated files.
Azure Functions using the proper bindings, but as ASA it will require deserialization that don't really qualify as "raw", unless you take a similar approach to what's done in EH Capture to which point you should just use Capture.
Related
My Azure Stream Analytics Job does not detect any input events if I use reference data in the query. When I'm using only streaming data it works well.
Here is my query:
SELECT v.localization as Station, v.lonn as Station_Longitude, v.latt as Station_Latitude, d.lat as My_Latitude, d.lon as My_Longitude
INTO [closest-station]
FROM eventhub d
CROSS JOIN [stations] v
WHERE ST_DISTANCE(CreatePoint(d.lat, d.lon), CreatePoint(v.latt, v.lonn) ) < 300
I used eventhub and blob as the input and the result was the same - works only without reference data
Inb4
When I'm testing the query with sample reference data (I'm uploading the exact same file as stored in the reference data location) it returns expected values
I've tested both inputs and tests were conducted successfully
The data comes from the logic app which copies it from dropbox to the eventhub or storage account (I've tested both scenarios) that are used in Azure Stream Analytics as inputs. Even if see this ran successfully, still no input events in ASA appear.
The idea is to get coordinates of the stations closer than 300 m to my localization.
Solved - you have to specify explicitly the reference file in the reference data input path pattern. Specifying container only doesn't work even if there is only one file inside.
Stream Analytics job will wait indefinitely for the blob to become available
As described here: Use referenece data for lookups in Stream Analytics
As you know, Kappa architecture is some kind of simplification of Lambda architecture. Kappa doesn't need batch layer, instead speed layer have to guarantee computation precision and enough throughput (more parallelism/resources) on historical data re-computation.
Still Kappa architecture requires two serving layers in case when you need to do analytic based on historical data. For example, data that have age < 2 weeks are stored at Redis (streaming serving layer), while all older data are stored somewhere at HBase (batch serving layer).
When (due to Kappa architecture) I have to insert data to batch serving layer?
If streaming layer inserts data immidiately to both batch & stream serving layers - than how about late data arrival? Or streaming layer should backup speed serving layer to batch serving layer on regular basis?
Example: let say source of data is Kafka, data are processed by Spark Structured Streaming or Flink, sinks are Redis and HBase. When write to Redis & HBase should happen?
If we perform stream processing, we want to make sure that output data is firstly made available as a data stream. In your example that means we write to Kafka as a primary sink.
Now you have two options:
have secondary jobs that reads from that Kafka topic and writes to Redis and HBase. That is the Kafka way, in that Kafka Streams does not support writing directly to any of these systems and you set up a Kafka connect job. These secondary jobs can then be tailored to the specific sinks, but they add additional operations overhead. (That's a bit of the backup option that you mentioned).
with Spark and Flink you also have the option to have secondary sinks directly in your job. You may add additional processing steps to transform the Kafka output into a more suitable form for the sink, but you are more limited when configuring the job. For example in Flink, you need to use the same checkpointing settings for the Kafka sink and the Redis/HBase sink. Nevertheless, if the settings work out, you just need to run one streaming job instead of 2 or 3.
Late events
Now the question is what to do with late data. The best solution is to let the framework handle that through watermarks. That is, data is only committed at all sinks, when the framework is sure that no late data arrives. If that doesn't work out because you really need to process late events even if they arrive much, much later and still want to have temporary results, you have to use update events.
Update events
(as requested by the OP, I will add more details to the update events)
In Kafka Streams, elements are emitted through a continuous refinement mechanism by default. That means, windowed aggregations emit results as soon as they have any valid data point and update that result while receiving new data. Thus, any late event is processed and yield an updated result. While this approach nicely lowers the burden to users, as they do not need to understand watermarks, it has some severe short-comings that led the Kafka Streams developers to add Suppression in 2.1 and onward.
The main issue is that it poses quite big challenges to downward users to process intermediate results as also explained in the article about Suppression. If it's not obvious if a result is temporary or "final" (in the sense that all expected events have been processed) then many applications are much harder to implement. In particular, windowing operations need to be replicated on consumer side to get the "final" value.
Another issue is that the data volume is blown up. If you'd have a strong aggregation factor, using watermark-based emission will reduce your data volume heavily after the first operation. However, continuous refinement will add a constant volume factor as each record triggers a new (intermediate) record for all intermediate steps.
Lastly, and particularly interesting for you is how to offload data to external systems if you have update events. Ideally, you would offload the data with some time lag continuously or periodically. That approach simulates the watermark-based emission again on consumer side.
Mixing the options
It's possible to use watermarks for the initial emission and then use update events for late events. The volume is then reduced for all "on-time" events. For example, Flink offers allowed lateness to make windows trigger again for late events.
This setup makes offloading data much easier as data only needs to be re-emitted to the external systems if a late event actually happened. The system should be tweaked that a late event is a rare case though.
I'm still quite new to the world of stream and batch processing and trying to understnad concepts and speach. It is admitedly very possible that the answer to my question well known, easy to find or even answered a hundred times here at SO, but I was not able to find it.
The background:
I am working in a big scientific project (nuclear fusion research), and we are producing tons of measurement data during experiment runs. Those data are mostly streams of samples tagged with a nanosecond timestamp, where samples can be anything from a single by ADC value, via an array of such, via deeply structured data (with up to hundreds of entries from 1 bit booleans to 64bit double precision floats) to raw HD video frames or even string text messages. If I understand the common terminologies right, I would regard our data as "tabular data", for the most part.
We are working with mostly selfmade software solutions from data acquisition over simple online (streaming) analysis (like scaling, subsampling and such) to our own data sotrage, management and access facilities.
In view of the scale of the operation and the effort for maintaining all those implementations, we are investigating the possibilities to use standard frameworks and tools for more of our tasks.
My question:
In particular at this stage, we are facing the need for more and more sofisticated (automated and manual) data analytics on live/online/realtime data as well as "after the fact" offline/batch analytics of "historic" data. In this endavor, I am trying to understand if and how existing analytics frameworks like Spark, Flink, Storm etc. (possibly supported by message queues like Kafka, Pulsar,...) can support a scenario, where
data is flowing/streamed into the platform/framework, attached an identifier like a URL or an ID or such
the platform interacts with integrated or external storage to persist the streaming data (for years), associated with the identifier
analytics processes can now transparently query/analyse data addressed by an identifier and an arbitrary (open or closed) time window, and the framework suplies data batches/samples for the analysis either from backend storage or coming in live from data acquisition
Simply streaming the online data into storage and querying from there seems no option as we need both raw and analysed data for live monitoring and realtime feedback control of the experiment.
Also, letting the user query either a live input signal or a historic batch from storage differently would not be ideal, as our physicists mostly are no data scientists and we would like to keep such "technicalities" away from them and idealy the exact same algorithms should be used for analysing new real time data and old stored data from previous experiments.
Sitenotes:
we are talking about peek data loads in the range of 10th of gigabits per second coming in bursts of increasing length of seconds up to minutes - could this be handled by the candidates?
we are using timestamps in nanosecond resolution, even thinking about pico - this poses some limitations on the list of possible candidates if I unserstand correctly?
I would be very greatfull if anyone would be able to understand my question and to shed some light on the topic for me :-)
Many Thanks and kind regards,
Beppo
I don't think anyone can say "yes, framework X can definitely handle your workload", because it depends a lot on what you need out of your message processing, e.g. regarding messaging reliability, and how your data streams can be partitioned.
You may be interested in BenchmarkingDistributedStreamProcessingEngines. The paper is using versions of Storm/Flink/Spark that are a few years old (looks like they were released in 2016), but maybe the authors would be willing to let you use their benchmark to evaluate newer versions of the three frameworks?
A very common setup for streaming analytics is to go data source -> Kafka/Pulsar -> analytics framework -> long term data store. This decouples processing from data ingest, and lets you do stuff like reprocessing historical data as if it were new.
I think the first step for you should be to see if you can get the data volume you need through Kafka/Pulsar. Either generate a test set manually, or grab some data you think could be representative from your production environment, and see if you can put it through Kafka/Pulsar at the throughput/latency you need.
Remember to consider partitioning of your data. If some of your data streams could be processed independently (i.e. ordering doesn't matter), you should not be putting them in the same partitions. For example, there is probably no reason to mix sensor measurements and the video feed streams. If you can separate your data into independent streams, you are less likely to run into bottlenecks both in Kafka/Pulsar and the analytics framework. Separate data streams would also allow you to parallelize processing in the analytics framework much better, as you could run e.g. video feed and sensor processing on different machines.
Once you know whether you can get enough throughput through Kafka/Pulsar, you should write a small example for each of the 3 frameworks. To start, I would just receive and drop the data from Kafka/Pulsar, which should let you know early whether there's a bottleneck in the Kafka/Pulsar -> analytics path. After that, you can extend the example to do something interesting with the example data, e.g. do a bit of processing like what you might want to do in production.
You also need to consider which kinds of processing guarantees you need for your data streams. Generally you will pay a performance penalty for guaranteeing at-least-once or exactly-once processing. For some types of data (e.g. the video feed), it might be okay to occasionally lose messages. Once you decide on a needed guarantee, you can configure the analytics frameworks appropriately (e.g. disable acking in Storm), and try benchmarking on your test data.
Just to answer some of your questions more explicitly:
The live data analysis/monitoring use case sounds like it fits the Storm/Flink systems fairly well. Hooking it up to Kafka/Pulsar directly, and then doing whatever analytics you need sounds like it could work for you.
Reprocessing of historical data is going to depend on what kind of queries you need to do. If you simply need a time interval + id, you can likely do that with Kafka plus a filter or appropriate partitioning. Kafka lets you start processing at a specific timestamp, and if you data is partitioned by id or you filter it as the first step in your analytics, you could start at the provided timestamp and stop processing when you hit a message outside the time window. This only applies if the timestamp you're interested in is when the message was added to Kafka though. I also don't believe Kafka supports below-millisecond resolution on the timestamps it generates.
If you need to do more advanced queries (e.g. you need to look at timestamps generated by your sensors), you could look at using Cassandra or Elasticsearch or Solr as your permanent data store. You will also want to investigate how to get the data from those systems back into your analytics system. For example, I believe Spark ships with a connector for reading from Elasticsearch, while Elasticsearch provides a connector for Storm. You should check whether such a connector exists for your data store/analytics system combination, or be willing to write your own.
Edit: Elaborating to answer your comment.
I was not aware that Kafka or Pulsar supported timestamps specified by the user, but sure enough, they both do. I don't see that Pulsar supports sub-millisecond timestamps though?
The idea you describe can definitely be supported by Kafka.
What you need is the ability to start a Kafka/Pulsar client at a specific timestamp, and read forward. Pulsar doesn't seem to support this yet, but Kafka does.
You need to guarantee that when you write data into a partition, they arrive in order of timestamp. This means that you are not allowed to e.g. write first message 1 with timestamp 10, and then message 2 with timestamp 5.
If you can make sure you write messages in order to Kafka, the example you describe will work. Then you can say "Start at timestamp 'last night at midnight'", and Kafka will start there. As live data comes in, it will receive it and add it to the end of its log. When the consumer/analytics framework has read all the data from last midnight to current time, it will start waiting for new (live) data to arrive, and process it as it comes in. You can then write custom code in your analytics framework to make sure it stops processing when it reaches the first message with timestamp 'tomorrow night'.
With regard to support of sub-millisecond timestamps, I don't think Kafka or Pulsar will support it out of the box, but you can work around it reasonably easily. Just put the sub-millisecond timestamp in the message as a custom field. When you want to start at e.g. timestamp 9ms 10ns, you ask Kafka to start at 9ms, and use a filter in the analytics framework to drop all messages between 9ms and 9ms 10ns.
Allow me to add the following suggestions on how Apache Pulsar might help address some of your requirements. Food for thought as it were.
"data is flowing/streamed into the platform/framework, attached an identifier like a URL or an ID or such"
You might want to look at Pulsar Functions, which allows you to write simple functions (In Java or Python) that gets executed on each individual message that is published to a topic. They are ideal for this type of data augmentation use case.
the platform interacts with integrated or external storage to persist the streaming data (for years), associated with the identifier
Pulsar has recently added tiered-storage, that allows you to retain event streams in S3, Azure Blob Store, or Google Cloud storage. This would allow you to keep the data for years in a cheap and reliable data store
analytics processes can now transparently query/analyse data addressed by an identifier and an arbitrary (open or closed) time window, and the framework suplies data batches/samples for the analysis either from backend storage or coming in live from data acquisition
Apache Pulsar has also added integration with the Presto query engine, which would allow you to query the data over a given time period (including data from tiered-storage) and place it into a topic for processing.
I have 1 eventhub with 2 partitions, I want to aggregate my data for a minute and save that data to database, I am using IEventProcessor to read events from the eventhub.
I am able to save data to database as it is, but when I aggregate data, I get 2 entries per minute instead of 1. I think the reason is the IEventProcessor runs twice, i.e each time for a partition in eventhub.
Are there any ways I can achieve aggregation of streaming data for a minute while reading from eventhub and then save to the database? (I can't use stream analytics, since I have data in protobuf format.)
You can use Azure IoTHub React Java and Scala API, it provides a merged reactive stream with events from all EventHub partitions.
From your perspective you'll see only one stream of data, regardless of the number of partitions in EventHub, and you can select a subset of partitions too if you need.
These samples show how the API works, it should make your task very simple. You need to define your "Sink" which is going to be a method writing events to a database, and link the provided "Source", something like:
val eventHubRecords = IoTHub().source(java.time.Instant.now())
val myDatabase = Sink.foreach[MessageFromDevice] {
m ⇒ MyDB.writeRecord(m)
}
eventHubRecords.to(myDatabase).run()
Here are the configuration settings, checkpointing supports Cassandra and AzureBlob.
Note: the project is named after Azure IoT, however you can use it for EventHub, let me know if you have any question.
You can use Stream Analytics and it's Group By clause. As long as all the rows are unique it won't summarize them. You can then push that output onto another Event Hub for your IEventProcessor to handle, or write it directly to storage.
I am using Stream Analytics to insert data into table storage. This works when all I want to do is add new rows. However, I now want to insert or update existing rows. Is this possible with Stream Analytics/Table storage?
The current implementation of Stream Analytics output to Azure Table uses InsertOrReplace API. So as long as your new data is cumulative (not just the deltas) it should simply work.
On the other hand, if you would like only upsert (insert or update), you could consider DocumentDB output.
If you like something more customized, You could also consider a trigger in your SQL table output.
cheers
Chetan
In short, no. Stream Analytics isn't an ETL tool.
However, you might be able to pass the output to a downstream SQLDB table. Then have a second stream job and query that joins the first to the table using left/right and inner joins. Just an idea, not tested, and not recommended.
OR
Maybe output the streamed data to a SQL DB landing table or Data Lake Store. Then perform a merge there before producing the output dataset. This would be a more natural approach.