I have a mapper that reads input and writes to a database. I want to limit how many inputs are actually converted and written to that database, and all mappers must contribute to the limit and then stop once that limit is reached (approximately; one or two extra isn't a big deal.)
I implemented a limiter function on our mapper that asks the other tasks, "How many records have you imported?" Once a given limit is reached, it will stop importing those records (although it will continue processing them for other purposes.)
the map code in question looks something like this:
public void map(ImmutableBytesWritable key, Result row, Context context) {
// prepare the input
// ...
if (context.getCounter(Metrics.IMPORTED).getValue()<IMPORT_LIMIT){
importRecord();
context.getCounter(Metrics.IMPORTED).increment(1l);
}
// do other things
// ...
}
So each mapper checks to see if there is more room to import, and only if the limit hasn't been reached does it perform any importing. However, each mapper itself is importing up to the limit, so that for 16 mappers, we get 16*IMPORT_LIMIT records imported. It's definitely doing SOME limiting (the count is much much lower than the normal number of imported records.)
When are counter values pushed to other mappers, or are they even available to each mapper? Can I actually get somewhat real-time values from the counter, or do they only update when a mapper is finished? Is there a better way to share a value between mappers?
Okay: from what I've seen, MapReduce doesn't share counters between mappers until the job is finished (ie. not at all.) I'm not sure if mappers that commit partway through will allow later mappers to see their counters, but it's not reliable enough to be done real time.
Instead what I'll do is I will run a simple java application that iterates over the rows on its own and write to a column, which the existing MapReduce job will use to determine if it should import the row or not.
Related
The concept of map-reduce is very familiar. It seems like a great fit for a problem I'm trying to solve, but it's either missing something (or I lack enough understanding of the concept).
I have a stream of items, structured as follows:
{
"jobId": 777,
"numberOfParts": 5,
"data": "some data..."
}
I want to do a map-reduce on many such items.
My mapping operation is straightforward - take the jobId.
My reduce operation is irrelevant for this phase, but all we know is that it takes multiple strings (the "some data..." part) and somehow reduces them to a single object.
The only problem is - I need all five parts of this job to complete before I can reduce all the strings into a single object. Every item has a "numberOfParts" property which indicates the number of items I must have before I apply the reduce operation. The items are not ordered, therefore I don't have a "partId" field.
Long story short - I need to apply some kind of a waiting mechanism that waits for all parts of the job to complete before initiating the reduce operation, and I need this waiting mechanism to rely on a value that exists within the payload (therefore solutions like kafka wouldn't work).
Is there a way to do that, hopefully using a single tool/framework?
I only want to write the map/reduce part and the "waiting" logic, the rest I believe should come out of the box.
**** EDIT ****
I'm currently in the design phase of the project and therefore not using any framework (such as spark, hadoop, etc...)
I asked this because I wanted to find out the best way to tackle this problem.
"Waiting" is not the correct approach.
Assuming your jobId is the key, and data contains some number of parts (zero or more), then you must have multiple reducers. One that gathers all parts of the same job, then another that processes all jobs with a collection of parts greater than or equal to numberOfParts while ignoring others
If I scan or query in DynamoDB it is possible to set the Limit property. The DynamoDB documentation says the following:
The maximum number of items to evaluate (not necessarily the number of
matching items).
So the problem with this is if you set filters and such it won't return all the items.
My goal that I'm trying to figure out how to achieve is to have a filter in a scan or query, but have it return x number of items. No matter what. I'm ok with having to use LastEvaluatedKey and make multiple requests, but I would like to try to make it as seamless and easy as possible (so not doing that would be best.
The only way I have thought to do this is to set the Limit property to say 1 or something. Then just keep scanning or querying using the LastEvaluatedKey until I reach that x number of items I'm looking for. Problem is, this seems VERY wasteful and inefficient. I mean if you have a table of millions of records you might have to make thousands and thousands of requests. It doesn't seem like it scales very well. Of course I'm sure it's no different than what DynamoDB would be doing behind the scenes.
But is there a way to do this more efficiently where I can reduce the number of requests I have to make? Or is that the only way to achieve this?
How would you achieve this goal?
A single Query operation will read up to the maximum number of items set (if using the Limit parameter) or a maximum of 1 MB of data and then apply any filtering to the results using FilterExpression.
You're 100% right that Limit is applied before FilterExpression. Meaning Dynamo might return some number or documents less than the Limit while other documents that satisfy the FilterExpression still exist in the table but aren't returned.
Its sounds like it would be unacceptable for your api to behave in the same manner. That is going to mean that in some cases, a single request to your service will result in multiple requests to Dynamo. Also, keep in mind that there is no way to predict what the LastEvaluatedKey will be which would be required to parallelize these requests. So in the case that your service makes multiple requests to Dynamo, they will be serial. To me, this is a rather heavy tradeoff but, if it is a requirement that you satisfy the Limit whenever possible, you have options.
First, Dynamo will automatically page at 1 MB. That means you could simply send your query to Dynamo without a Limit and implement the Limit on your end. You may still need to make multiple requests to ensure that your've satisfied the Limit but this approach will result in the fewest number of requests to Dynamo. The trade off here is the total data being read and transferred. Chances are your Limit will not happen to line up perfectly with the 1 MB limit which means the excess data being read, filtered, and transferred is wasted.
You already mentioned the other extreme of sending a Limit of 1 and pointed out that will result in the maximum number of requests to Dynamo
Another approach along these lines is to create some sort of probabilistic function that takes the Limit given to your service by the client and computes a new Limit for Dynamo. For example, your FilterExpression filters out about half of the documents in the table. That means you can multiply the client Limit by 2 and that would be a reasonable Limit to send to Dynamo. Of the approaches we've talked about so far, this one has the highest potential for efficiency however, it also has the highest potential for complexity. For example, you might find that using a simple linear function is not good enough and instead you need to use machine learning to find a multi-variate non-linear function to calculate the new Limit. This approach also heavily depends on the uniformity of your data in Dynamo as well as your access patterns. Again, you might need machine learning to optimize for those variables.
In any of the cases where you are implementing the Limit on your end, if you plan on sending back the LastEvaluatedKey to the client for subsequent calls to your service, you will also need to take care to keep track of the LastEvaluatedKey that you evaluated. You will no longer be able to rely on the LastEvaluatedKey returned from Dynamo.
The final approach would be to reorganize/regroup your data either with a GSI, a separate table that you keep in sync using Dynamo Streams or a different schema altogether with the goal of not requiring a FilterExpression.
Given a Spark application
What factors decide the number of executors in a stand alone mode? In the Mesos and YARN according to this documents, we can specify the number of executers/cores and memory.
Once a number of executors are started. Does Spark start the tasks in a round robin fashion or is it smart enough to see if some of the executors are idle/busy and then schedule the tasks accordingly.
Also, how does Spark decide on the number of tasks? I did write a simple max temperature program with small dataset and Spark spawned two tasks in a single executor. This is in the Spark stand alone mode.
Answering your questions:
The standalone mode uses the same configuration variable as Mesos and Yarn modes to set the number of executors. The variable spark.cores.max defines the maximun number of cores used in the spark Context. The default value is infinity so Spark will use all the cores in the cluster. The spark.task.cpus variable defines how many CPUs Spark will allocate for a single task, the default value is 1. With these two variables you can define the maximun number of parallel tasks in your cluster.
When you create an RDD subClass you can define in which machines to run your task. This is defined in the getPreferredLocations method. But as the method signatures suggest this is only a preference so if Spark detects that one machine is not busy, it will launch the task in this idle machine. However I don't know the mechanism used by Spark to know what machines are idle. To achieve locality, we (Stratio) decided to make each Partions smaller so the task takes less time and achieve locality.
The number of tasks of each Spark's operation is defined according to the length of the RDD's partitions. This vector is the result of the getPartitions method that you have to override if you want to develop a new RDD subClass. This method returns how a RDD is split, where the information is and the partitions. When you join two or more RDDs using, for example, union or join operations, the number of tasks of the resulting RDD is the maximum number of tasks of the RDDs involved in the operation. For example: if you join RDD1 that has 100 tasks and RDD2 that has 1000 tasks, the next operation of the resulting RDD will have 1000 tasks. Note that a high number of partitions is not necessarily synonym of more data.
I hope this will help.
I agree with #jlopezmat about how Spark chooses its configuration. With respect to your test code, your are seeing two task due to the way textFile is implemented. From SparkContext.scala:
/**
* Read a text file from HDFS, a local file system (available on all nodes), or any
* Hadoop-supported file system URI, and return it as an RDD of Strings.
*/
def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String] = {
hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text],
minPartitions).map(pair => pair._2.toString)
}
and if we check what is the value of defaultMinPartitions:
/** Default min number of partitions for Hadoop RDDs when not given by user */
def defaultMinPartitions: Int = math.min(defaultParallelism, 2)
Spark chooses the number of tasks based on the number of partitions in the original data set. If you are using HDFS as your data source, then the number of partitions with be equal to the number of HDFS blocks, by default. You can change the number of partitions in a number of different ways. The top two: as an extra argument to the SparkContext.textFile method; by calling the RDD.repartion method.
Answering some points that were not addressed in previous answers:
in Standalone mode, you need to play with --executor-cores and --max-executor-cores to set the number of executors that will be launched (granted that you have enough memory to fit that number if you specify --executor-memory)
Spark does not allocate task in a round-robin manner, it uses a mechanism called "Delay Scheduling", which is a pull-based technique allowing each executor to offer it's availability to the master, which will decide whether or not to send a task on it.
I have a function which should have two modes of behaviour according to the place where it's called from.
The core functionality is to do an insert into a table in my database, but it has to be done in two different ways.
Normal mode: whenever it's called only one time (outside of a loop)
For example:
//...
myfunc(param1, record); // it should insert a single record into the database
//...
Batch mode: whenever it's called from inside of a loop
For example:
while(...){
myfunc(param1, record);
}
Inside the "while" loop, each time it's called, it only should store the record in a list and when it reaches the end of the loop, it should fetch all records from the list and prepare a "batch" query that inserts all in one go.
I am wondering how to make it to detect from where it's called in order to switch to the corresponding mode and also how to detect that it has reached the end of a loop and from now on, it should start getting records from the list, prepare the query and execute it.
Any tips or suggestions will be highly appreciated!
Thanks heaps!
It is not, in general, possible to tell whether you are being called in a loop, even with full source code access.
You might be able to do something with caching and delaying the actual database insert for a limited time in all cases. Go on caching until you go for x microseconds without a new call, and then insert the cached data.
However, that could give strange effects if you are not in control of all accesses to the database. In particular, you should do your cached inserts any time there is query that might be affected by them, even in a loop.
Sometimes it is useful to cache queries like this in order to minimize the number of database queries. You can have one function that builds a cache and a second function that sends the request and flushes the cache. If you are going to do that, I recommend using the same function for both single-entry and multiple-entries. The pseudocode will look something like this:
Single-entry usage:
myfunc(param1, record); # caches requests
sendRequests(); # sends all cached requests, flushes cache
Multiple-entry usage:
while(...){
myfunc(param1, record);
}
sendRequests();
sendRequest() will send as many queries as it finds: 1 or many. For efficiency, it can format the requests differently based on their size.
Long story short, I'm rewriting a piece of a system and am looking for a way to store some hit counters in AWS SimpleDB.
For those of you not familiar with SimpleDB, the (main) problem with storing counters is that the cloud propagation delay is often over a second. Our application currently gets ~1,500 hits per second. Not all those hits will map to the same key, but a ballpark figure might be around 5-10 updates to a key every second. This means that if we were to use a traditional update mechanism (read, increment, store), we would end up inadvertently dropping a significant number of hits.
One potential solution is to keep the counters in memcache, and using a cron task to push the data. The big problem with this is that it isn't the "right" way to do it. Memcache shouldn't really be used for persistent storage... after all, it's a caching layer. In addition, then we'll end up with issues when we do the push, making sure we delete the correct elements, and hoping that there is no contention for them as we're deleting them (which is very likely).
Another potential solution is to keep a local SQL database and write the counters there, updating our SimpleDB out-of-band every so many requests or running a cron task to push the data. This solves the syncing problem, as we can include timestamps to easily set boundaries for the SimpleDB pushes. Of course, there are still other issues, and though this might work with a decent amount of hacking, it doesn't seem like the most elegant solution.
Has anyone encountered a similar issue in their experience, or have any novel approaches? Any advice or ideas would be appreciated, even if they're not completely flushed out. I've been thinking about this one for a while, and could use some new perspectives.
The existing SimpleDB API does not lend itself naturally to being a distributed counter. But it certainly can be done.
Working strictly within SimpleDB there are 2 ways to make it work. An easy method that requires something like a cron job to clean up. Or a much more complex technique that cleans as it goes.
The Easy Way
The easy way is to make a different item for each "hit". With a single attribute which is the key. Pump the domain(s) with counts quickly and easily. When you need to fetch the count (presumable much less often) you have to issue a query
SELECT count(*) FROM domain WHERE key='myKey'
Of course this will cause your domain(s) to grow unbounded and the queries will take longer and longer to execute over time. The solution is a summary record where you roll up all the counts collected so far for each key. It's just an item with attributes for the key {summary='myKey'} and a "Last-Updated" timestamp with granularity down to the millisecond. This also requires that you add the "timestamp" attribute to your "hit" items. The summary records don't need to be in the same domain. In fact, depending on your setup, they might best be kept in a separate domain. Either way you can use the key as the itemName and use GetAttributes instead of doing a SELECT.
Now getting the count is a two step process. You have to pull the summary record and also query for 'Timestamp' strictly greater than whatever the 'Last-Updated' time is in your summary record and add the two counts together.
SELECT count(*) FROM domain WHERE key='myKey' AND timestamp > '...'
You will also need a way to update your summary record periodically. You can do this on a schedule (every hour) or dynamically based on some other criteria (for example do it during regular processing whenever the query returns more than one page). Just make sure that when you update your summary record you base it on a time that is far enough in the past that you are past the eventual consistency window. 1 minute is more than safe.
This solution works in the face of concurrent updates because even if many summary records are written at the same time, they are all correct and whichever one wins will still be correct because the count and the 'Last-Updated' attribute will be consistent with each other.
This also works well across multiple domains even if you keep your summary records with the hit records, you can pull the summary records from all your domains simultaneously and then issue your queries to all domains in parallel. The reason to do this is if you need higher throughput for a key than what you can get from one domain.
This works well with caching. If your cache fails you have an authoritative backup.
The time will come where someone wants to go back and edit / remove / add a record that has an old 'Timestamp' value. You will have to update your summary record (for that domain) at that time or your counts will be off until you recompute that summary.
This will give you a count that is in sync with the data currently viewable within the consistency window. This won't give you a count that is accurate up to the millisecond.
The Hard Way
The other way way is to do the normal read - increment - store mechanism but also write a composite value that includes a version number along with your value. Where the version number you use is 1 greater than the version number of the value you are updating.
get(key) returns the attribute value="Ver015 Count089"
Here you retrieve a count of 89 that was stored as version 15. When you do an update you write a value like this:
put(key, value="Ver016 Count090")
The previous value is not removed and you end up with an audit trail of updates that are reminiscent of lamport clocks.
This requires you to do a few extra things.
the ability to identify and resolve conflicts whenever you do a GET
a simple version number isn't going to work you'll want to include a timestamp with resolution down to at least the millisecond and maybe a process ID as well.
in practice you'll want your value to include the current version number and the version number of the value your update is based on to more easily resolve conflicts.
you can't keep an infinite audit trail in one item so you'll need to issue delete's for older values as you go.
What you get with this technique is like a tree of divergent updates. you'll have one value and then all of a sudden multiple updates will occur and you will have a bunch of updates based off the same old value none of which know about each other.
When I say resolve conflicts at GET time I mean that if you read an item and the value looks like this:
11 --- 12
/
10 --- 11
\
11
You have to to be able to figure that the real value is 14. Which you can do if you include for each new value the version of the value(s) you are updating.
It shouldn't be rocket science
If all you want is a simple counter: this is way over-kill. It shouldn't be rocket science to make a simple counter. Which is why SimpleDB may not be the best choice for making simple counters.
That isn't the only way but most of those things will need to be done if you implement an SimpleDB solution in lieu of actually having a lock.
Don't get me wrong, I actually like this method precisely because there is no lock and the bound on the number of processes that can use this counter simultaneously is around 100. (because of the limit on the number of attributes in an item) And you can get beyond 100 with some changes.
Note
But if all these implementation details were hidden from you and you just had to call increment(key), it wouldn't be complex at all. With SimpleDB the client library is the key to making the complex things simple. But currently there are no publicly available libraries that implement this functionality (to my knowledge).
To anyone revisiting this issue, Amazon just added support for Conditional Puts, which makes implementing a counter much easier.
Now, to implement a counter - simply call GetAttributes, increment the count, and then call PutAttributes, with the Expected Value set correctly. If Amazon responds with an error ConditionalCheckFailed, then retry the whole operation.
Note that you can only have one expected value per PutAttributes call. So, if you want to have multiple counters in a single row, then use a version attribute.
pseudo-code:
begin
attributes = SimpleDB.GetAttributes
initial_version = attributes[:version]
attributes[:counter1] += 3
attributes[:counter2] += 7
attributes[:version] += 1
SimpleDB.PutAttributes(attributes, :expected => {:version => initial_version})
rescue ConditionalCheckFailed
retry
end
I see you've accepted an answer already, but this might count as a novel approach.
If you're building a web app then you can use Google's Analytics product to track page impressions (if the page to domain-item mapping fits) and then to use the Analytics API to periodically push that data up into the items themselves.
I haven't thought this through in detail so there may be holes. I'd actually be quite interested in your feedback on this approach given your experience in the area.
Thanks
Scott
For anyone interested in how I ended up dealing with this... (slightly Java-specific)
I ended up using an EhCache on each servlet instance. I used the UUID as a key, and a Java AtomicInteger as the value. Periodically a thread iterates through the cache and pushes rows to a simpledb temp stats domain, as well as writing a row with the key to an invalidation domain (which fails silently if the key already exists). The thread also decrements the counter with the previous value, ensuring that we don't miss any hits while it was updating. A separate thread pings the simpledb invalidation domain, and rolls up the stats in the temporary domains (there are multiple rows to each key, since we're using ec2 instances), pushing it to the actual stats domain.
I've done a little load testing, and it seems to scale well. Locally I was able to handle about 500 hits/second before the load tester broke (not the servlets - hah), so if anything I think running on ec2 should only improve performance.
Answer to feynmansbastard:
If you want to store huge amount of events i suggest you to use distributed commit log systems such as kafka or aws kinesis. They allow to consume stream of events cheap and simple (kinesis's pricing is 25$ per month for 1K events per seconds) – you just need to implement consumer (using any language), which bulk reads all events from previous checkpoint, aggregates counters in memory then flushes data into permanent storage (dynamodb or mysql) and commit checkpoint.
Events can be logged simply using nginx log and transfered to kafka/kinesis using fluentd. This is very cheap, performant and simple solution.
Also had similiar needs/challenges.
I looked at using google analytics and count.ly. the latter seemed too expensive to be worth it (plus they have a somewhat confusion definition of sessions). GA i would have loved to use, but I spent two days using their libraries and some 3rd party ones (gadotnet and one other from maybe codeproject). unfortunately I could only ever see counters post in GA realtime section, never in the normal dashboards even when the api reported success. we were probably doing something wrong but we exceeded our time budget for ga.
We already had an existing simpledb counter that updated using conditional updates as mentioned by previous commentor. This works well, but suffers when there is contention and conccurency where counts are missed (for example, our most updated counter lost several million counts over a period of 3 months, versus a backup system).
We implemented a newer solution which is somewhat similiar to the answer for this question, except much simpler.
We just sharded/partitioned the counters. When you create a counter you specify the # of shards which is a function of how many simulatenous updates you expect. this creates a number of sub counters, each which has the shard count started with it as an attribute :
COUNTER (w/5shards) creates :
shard0 { numshards = 5 } (informational only)
shard1 { count = 0, numshards = 5, timestamp = 0 }
shard2 { count = 0, numshards = 5, timestamp = 0 }
shard3 { count = 0, numshards = 5, timestamp = 0 }
shard4 { count = 0, numshards = 5, timestamp = 0 }
shard5 { count = 0, numshards = 5, timestamp = 0 }
Sharded Writes
Knowing the shard count, just randomly pick a shard and try to write to it conditionally. If it fails because of contention, choose another shard and retry.
If you don't know the shard count, get it from the root shard which is present regardless of how many shards exist. Because it supports multiple writes per counter, it lessens the contention issue to whatever your needs are.
Sharded Reads
if you know the shard count, read every shard and sum them.
If you don't know the shard count, get it from the root shard and then read all and sum.
Because of slow update propogation, you can still miss counts in reading but they should get picked up later. This is sufficient for our needs, although if you wanted more control over this you could ensure that- when reading- the last timestamp was as you expect and retry.