I have a simple Kafka streams scenario where I am doing a groupyByKey then reduce and then an action. There could be duplicate events in the source topic hence the groupyByKey and reduce
The action could error and in that case, I need the streams app to reprocess that event. In the example below I'm always throwing an error to demonstrate the point.
It is very important that the action only ever happens once and at least once.
The problem I'm finding is that when the streams app reprocesses the event, the reduce function is being called and as it returns null the action doesn't get recalled.
As only one event is produced to the source topic TOPIC_NAME I would expect the reduce to not have any values and skip down to the mapValues.
val topologyBuilder = StreamsBuilder()
topologyBuilder.stream(
TOPIC_NAME,
Consumed.with(Serdes.String(), EventSerde())
)
.groupByKey(Grouped.with(Serdes.String(), EventSerde()))
.reduce { current, _ ->
println("reduce hit")
null
}
.mapValues { _, v ->
println(Id: "${v.correlationId}")
throw Exception("simulate error")
}
To cause the issue I run the streams app twice. This is the output:
First run
Id: 90e6aefb-8763-4861-8d82-1304a6b5654e
11:10:52.320 [test-app-dcea4eb1-a58f-4a30-905f-46dad446b31e-StreamThread-1] ERROR org.apache.kafka.streams.KafkaStreams - stream-client [test-app-dcea4eb1-a58f-4a30-905f-46dad446b31e] All stream threads have died. The instance will be in error state and should be closed.
Second run
reduce hit
As you can see the .mapValues doesn't get called on the second run even though it errored on the first run causing the streams app to reprocess the same event again.
Is it possible to be able to have a streams app re-process an event with a reduced step where it's treating the event like it's never seen before? - Or is there a better approach to how I'm doing this?
I was missing a property setting for the streams app.
props["processing.guarantee"]= "exactly_once"
By setting this, it will guarantee that any state created from the point of picking up the event will rollback in case of a exception being thrown and the streams app crashing.
The problem was that the streams app would pick up the event again to re-process but the reducer step had state which has persisted. By enabling the exactly_once setting it ensures that the reducer state is also rolled back.
It now successfully re-processes the event as if it had never seen it before
We are running Ignite cluster with 12 nodes running on Ignite 2.7.0 on openjdk
1.8 at RHEL platform.
Seeing heavy cputime spent with https://ignite.apache.org/releases/latest/javadoc/org/apache/ignite/IgniteCache.html#replace-K-V-V-
We are witnessing slowness with one of our process and when we tried to drill it
further by profiling the JVM, the main culprit (taking ~78% of total time)
seems to be coming from Ignite cache.repalce(K,V,V) api call.
Out of 77.9 by replace, 39% is taken by GridCacheAdapater.equalVal and 38.5%
by GridCacheAdapter.put
Cache is Partitioned and ATOMIC with readThrough,writeThrough,writeBehindEnabled set to True.
Attaching the profiling snapshot of one node(similar is the profiling result on other nodes), Can someone please check and suggest what
could be the cause OR some known performance issue with this Ignite version related to cache.replace(k,v,v) api ?
JVM Prolfiling Snapshot of one node
I guess that it can be related to next issue:
https://issues.apache.org/jira/browse/IGNITE-5003
The problem there related to the operations for the same key before the previous batch of updates (that contains this key) will be stored in the database.
As I see it should be added to Ignite 2.8.
Update:
I tested putAll operation. From the next two pictures you can see that putAll waiting for GridCacheWriteBehindStore.write (two different threads) that contains updateCache:
public void write(Entry<? extends K, ? extends V> entry) {
try {
if (log.isDebugEnabled())
log.debug(S.toString("Store put",
"key", entry.getKey(), true,
"val", entry.getValue(), true));
updateCache(entry.getKey(), entry, StoreOperation.PUT);
}
And provided issue can affect your put operations (or replace as well).
Situation is the following.
We have setup SSL + ACLs in Kafka Broker.
We are setting up stream, which reads messages from two topics:
KStream<String, String> stringInput
= kBuilder.stream( STRING_SERDE, STRING_SERDE, inTopicName );
stringInput
.filter( streamFilter::passOrFilterMessages )
.map( processor )
.to( outTopicName );
It is done like two times (in the loop).
Then we are setting general error handler:
streams.setUncaughtExceptionHandler( ( Thread t, Throwable e ) -> {
synchronized ( this ) {
LOG.fatal( ... );
this.stop();
}
}
);
Problem is the following. If for example in one topic certificate is no more valid. The stream is throwing exception Not authorized to access topics ...
So far so good.
But the exception is handled by general error handler, so the complete application stops even if the second topic has no problems.
The question is, how to handle this exception per topic?
How to avoid situation that at some moment complete application stops due to the problem that one single topic has problems with authorization?
I understand that if Broker is not available, then complete app may stop. But if only one topic is not available, then single stream shall stop, and not complete application, or?
By design, Kafka Streams treats the topology a one and cannot distinguish between both parts. For your specific case, as you loop and build to independent pipelines, you could run two KafkaStreams instances in parallel (within the same application/JVM) to isolate both from each other. Thus, if one fails, the other one is not affected. You would need to use two different application.id for both instances.
In MT4, there exists a stage/state: when we switch from AccountA to AccountB, when Connection is established and init() and start() are triggered by MT4; but before the "blinnnggg" (sound) when all the historical/outstanding trades are loaded from Server.
Switch Account>Establish Connection>Trigger Init()/Start() events>Start Downloading of Outstanding/Historical trades>Completed Downloading (issue "bliinng" sound).
I need to know (in MQL4) that all the trades are completed downloaded from the tradeServer --to know that the account is truly empty -vs- still downloading history from tradeServer.
Any pointer will be appreciated. I've explored IsTradeAllowed() IsContextBusy() and IsConnected(). All these are in "normal" state and the init() and start() events are all fired ok. But I cannot figure out if the history/outstanding trade lists has completed downloading.
UPDATE: The final workaround I finally implemented was to use the OrdersHistoryTotal(). Apparently this number will be ZERO (0) during downloading of order history. And it will NEVER be zero (due to initial deposit). So, I ended-up using this as a "flag".
Observation
As the problem was posted, there seems no such "integrated" method for MT4-Terminal.
IsTradeAllowed() reflects an administrative state of the account/access to the execution of the Trading Services { IsTradeAllowed | !IsTradeAllowed }
IsConnected() reflects a technical state of the visibility / login credentials / connection used upon an attempt to setup/maintain an online connection between a localhost <-> Server { IsConnected() | !IsConnected() }
init() {...} is a one-stop setup facility, that is/was being called once an MT4-programme { ExpertAdvisor | Script | TechnicalIndicator } was launched on a localhost machine. This facility is strongly advised to be non-blocking and non-re-entrant. A change from the user account_A to another user account_B is typically ( via an MT4-configuration options ) a reason to stop an execution of a previously loaded MQL4-code ( be it an EA / a Script / a Technical Indicator ) )
start() {...} is an event-handler facility, that endlessly waits, for a next occurrence of an FX-Market Event appearance ( being propagated down the line by the Broker MT4-Server automation ) that is being announced via an established connection downwards, to the MT4-Terminal process, being run on a localhost machine.
A Workaround Solution
As understood, the problem may be detected and handled indirectly.
While the MT4 platform seems to have no direct method to distinguish between the complete / in-complete refresh of the list of { current | historical } trades, let me propose a method of an indirect detection thereof.
Try to launch a "signal"-trade ( a pending order, placed geometrically well far away, in the PriceDOMAIN, from the current Ask/Bid-levels ).
Once this trade would be end-to-end registered ( Server-side acknowledged ), the local-side would have confirmed the valid state of the db.POOL
Making this a request/response pattern between localhost/MT4-Server processes, the localhost int init(){...} / int start(){...} functionality may thus reflect a moment, when the both sides have synchronised state of the records in db.POOL
I need to design a Redis-driven scalable task scheduling system.
Requirements:
Multiple worker processes.
Many tasks, but long periods of idleness are possible.
Reasonable timing precision.
Minimal resource waste when idle.
Should use synchronous Redis API.
Should work for Redis 2.4 (i.e. no features from upcoming 2.6).
Should not use other means of RPC than Redis.
Pseudo-API: schedule_task(timestamp, task_data). Timestamp is in integer seconds.
Basic idea:
Listen for upcoming tasks on list.
Put tasks to buckets per timestamp.
Sleep until the closest timestamp.
If a new task appears with timestamp less than closest one, wake up.
Process all upcoming tasks with timestamp ≤ now, in batches (assuming
that task execution is fast).
Make sure that concurrent worker wouldn't process same tasks. At the same time, make sure that no tasks are lost if we crash while processing them.
So far I can't figure out how to fit this in Redis primitives...
Any clues?
Note that there is a similar old question: Delayed execution / scheduling with Redis? In this new question I introduce more details (most importantly, many workers). So far I was not able to figure out how to apply old answers here — thus, a new question.
Here's another solution that builds on a couple of others [1]. It uses the redis WATCH command to remove the race condition without using lua in redis 2.6.
The basic scheme is:
Use a redis zset for scheduled tasks and redis queues for ready to run tasks.
Have a dispatcher poll the zset and move tasks that are ready to run into the redis queues. You may want more than 1 dispatcher for redundancy but you probably don't need or want many.
Have as many workers as you want which do blocking pops on the redis queues.
I haven't tested it :-)
The foo job creator would do:
def schedule_task(queue, data, delay_secs):
# This calculation for run_at isn't great- it won't deal well with daylight
# savings changes, leap seconds, and other time anomalies. Improvements
# welcome :-)
run_at = time.time() + delay_secs
# If you're using redis-py's Redis class and not StrictRedis, swap run_at &
# the dict.
redis.zadd(SCHEDULED_ZSET_KEY, run_at, {'queue': queue, 'data': data})
schedule_task('foo_queue', foo_data, 60)
The dispatcher(s) would look like:
while working:
redis.watch(SCHEDULED_ZSET_KEY)
min_score = 0
max_score = time.time()
results = redis.zrangebyscore(
SCHEDULED_ZSET_KEY, min_score, max_score, start=0, num=1, withscores=False)
if results is None or len(results) == 0:
redis.unwatch()
sleep(1)
else: # len(results) == 1
redis.multi()
redis.rpush(results[0]['queue'], results[0]['data'])
redis.zrem(SCHEDULED_ZSET_KEY, results[0])
redis.exec()
The foo worker would look like:
while working:
task_data = redis.blpop('foo_queue', POP_TIMEOUT)
if task_data:
foo(task_data)
[1] This solution is based on not_a_golfer's, one at http://www.saltycrane.com/blog/2011/11/unique-python-redis-based-queue-delay/, and the redis docs for transactions.
You didn't specify the language you're using. You have at least 3 alternatives of doing this without writing a single line of code in Python at least.
Celery has an optional redis broker.
http://celeryproject.org/
resque is an extremely popular redis task queue using redis.
https://github.com/defunkt/resque
RQ is a simple and small redis based queue that aims to "take the good stuff from celery and resque" and be much simpler to work with.
http://python-rq.org/
You can at least look at their design if you can't use them.
But to answer your question - what you want can be done with redis. I've actually written more or less that in the past.
EDIT:
As for modeling what you want on redis, this is what I would do:
queuing a task with a timestamp will be done directly by the client - you put the task in a sorted set with the timestamp as the score and the task as the value (see ZADD).
A central dispatcher wakes every N seconds, checks out the first timestamps on this set, and if there are tasks ready for execution, it pushes the task to a "to be executed NOW" list. This can be done with ZREVRANGEBYSCORE on the "waiting" sorted set, getting all items with timestamp<=now, so you get all the ready items at once. pushing is done by RPUSH.
workers use BLPOP on the "to be executed NOW" list, wake when there is something to work on, and do their thing. This is safe since redis is single threaded, and no 2 workers will ever take the same task.
once finished, the workers put the result back in a response queue, which is checked by the dispatcher or another thread. You can add a "pending" bucket to avoid failures or something like that.
so the code will look something like this (this is just pseudo code):
client:
ZADD "new_tasks" <TIMESTAMP> <TASK_INFO>
dispatcher:
while working:
tasks = ZREVRANGEBYSCORE "new_tasks" <NOW> 0 #this will only take tasks with timestamp lower/equal than now
for task in tasks:
#do the delete and queue as a transaction
MULTI
RPUSH "to_be_executed" task
ZREM "new_tasks" task
EXEC
sleep(1)
I didn't add the response queue handling, but it's more or less like the worker:
worker:
while working:
task = BLPOP "to_be_executed" <TIMEOUT>
if task:
response = work_on_task(task)
RPUSH "results" response
EDit: stateless atomic dispatcher :
while working:
MULTI
ZREVRANGE "new_tasks" 0 1
ZREMRANGEBYRANK "new_tasks" 0 1
task = EXEC
#this is the only risky place - you can solve it by using Lua internall in 2.6
SADD "tmp" task
if task.timestamp <= now:
MULTI
RPUSH "to_be_executed" task
SREM "tmp" task
EXEC
else:
MULTI
ZADD "new_tasks" task.timestamp task
SREM "tmp" task
EXEC
sleep(RESOLUTION)
If you're looking for ready solution on Java. Redisson is right for you. It allows to schedule and execute tasks (with cron-expression support) in distributed way on Redisson nodes using familiar ScheduledExecutorService api and based on Redis queue.
Here is an example. First define a task using java.lang.Runnable interface. Each task can access to Redis instance via injected RedissonClient object.
public class RunnableTask implements Runnable {
#RInject
private RedissonClient redissonClient;
#Override
public void run() throws Exception {
RMap<String, Integer> map = redissonClient.getMap("myMap");
Long result = 0;
for (Integer value : map.values()) {
result += value;
}
redissonClient.getTopic("myMapTopic").publish(result);
}
}
Now it's ready to sumbit it into ScheduledExecutorService:
RScheduledExecutorService executorService = redisson.getExecutorService("myExecutor");
ScheduledFuture<?> future = executorService.schedule(new CallableTask(), 10, 20, TimeUnit.MINUTES);
future.get();
// or cancel it
future.cancel(true);
Examples with cron expressions:
executorService.schedule(new RunnableTask(), CronSchedule.of("10 0/5 * * * ?"));
executorService.schedule(new RunnableTask(), CronSchedule.dailyAtHourAndMinute(10, 5));
executorService.schedule(new RunnableTask(), CronSchedule.weeklyOnDayAndHourAndMinute(12, 4, Calendar.MONDAY, Calendar.FRIDAY));
All tasks are executed on Redisson node.
A combined approach seems plausible:
No new task timestamp may be less than current time (clamp if less). Assuming reliable NTP synch.
All tasks go to bucket-lists at keys, suffixed with task timestamp.
Additionally, all task timestamps go to a dedicated zset (key and score — timestamp itself).
New tasks are accepted from clients via separate Redis list.
Loop: Fetch oldest N expired timestamps via zrangebyscore ... limit.
BLPOP with timeout on new tasks list and lists for fetched timestamps.
If got an old task, process it. If new — add to bucket and zset.
Check if processed buckets are empty. If so — delete list and entrt from zset. Probably do not check very recently expired buckets, to safeguard against time synchronization issues. End loop.
Critique? Comments? Alternatives?
Lua
I made something similar to what's been suggested here, but optimized the sleep duration to be more precise. This solution is good if you have few inserts into the delayed task queue. Here's how I did it with a Lua script:
local laterChannel = KEYS[1]
local nowChannel = KEYS[2]
local currentTime = tonumber(KEYS[3])
local first = redis.call("zrange", laterChannel, 0, 0, "WITHSCORES")
if (#first ~= 2)
then
return "2147483647"
end
local execTime = tonumber(first[2])
local event = first[1]
if (currentTime >= execTime)
then
redis.call("zrem", laterChannel, event)
redis.call("rpush", nowChannel, event)
return "0"
else
return tostring(execTime - currentTime)
end
It uses two "channels". laterChannel is a ZSET and nowChannel is a LIST. Whenever it's time to execute a task, the event is moved from the the ZSET to the LIST. The Lua script with respond with how many MS the dispatcher should sleep until the next poll. If the ZSET is empty, sleep forever. If it's time to execute something, do not sleep(i e poll again immediately). Otherwise, sleep until it's time to execute the next task.
So what if something is added while the dispatcher is sleeping?
This solution works in conjunction with key space events. You basically need to subscribe to the key of laterChannel and whenever there is an add event, you wake up all the dispatcher so they can poll again.
Then you have another dispatcher that uses the blocking left pop on nowChannel. This means:
You can have the dispatcher across multiple instances(i e it's scaling)
The polling is atomic so you won't have any race conditions or double events
The task is executed by any of the instances that are free
There are ways to optimize this even more. For example, instead of returning "0", you fetch the next item from the zset and return the correct amount of time to sleep directly.
Expiration
If you can not use Lua scripts, you can use key space events on expired documents.
Subscribe to the channel and receive the event when Redis evicts it. Then, grab a lock. The first instance to do so will move it to a list(the "execute now" channel). Then you don't have to worry about sleeps and polling. Redis will tell you when it's time to execute something.
execute_later(timestamp, eventId, event) {
SET eventId event EXP timestamp
SET "lock:" + eventId, ""
}
subscribeToEvictions(eventId) {
var deletedCount = DEL eventId
if (deletedCount == 1) {
// move to list
}
}
This however has it own downsides. For example, if you have many nodes, all of them will receive the event and try to get the lock. But I still think it's overall less requests any anything suggested here.