Re: Which committers care about Kafka?
to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto: langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Cody, I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight for that one. For the Kafka DStream skipping the WAL implementation - this is something I discussed with TD a few weeks ago. Though it is a good idea to implement this to avoid unnecessary HDFS writes, it is an optimization. For that reason, we must be careful in implementation. There are a couple of issues that we need to ensure works properly - specifically ordering. To ensure we pull messages from different topics and partitions in the same order after failure, we’d still have to persist the metadata to HDFS (or some other system) - this metadata must contain the order of messages consumed, so we know how to re-read the messages. I am planning to explore this once I have some time (probably in Jan). In addition, we must also ensure bucketing functions work fine as well. I will file a placeholder jira for this one. I also wrote an API to write data back to Kafka a while back - https://github.com/apache/spark/pull/2994 . I am hoping that this will get pulled in soon, as this is something I know people want. I am open to feedback on that - anything that I can do to make it better. Thanks, Hari On Thu, Dec 18, 2014 at 11:14 AM, Patrick Wendell pwend...@gmail.com wrote: Hey Cody, Thanks for reaching out with this. The lead on streaming is TD - he is traveling this week though so I can respond a bit. To the high level point of whether Kafka is important - it definitely is. Something like 80% of Spark Streaming deployments (anecdotally) ingest data from Kafka. Also, good support for Kafka is something we generally want in Spark and not a library. In some cases IIRC there were user libraries that used unstable Kafka API's and we were somewhat waiting on Kafka to stabilize them to merge things upstream. Otherwise users wouldn't be able
Re: Which committers care about Kafka?
. As this Low Level consumer has complete control over the Kafka Offsets , we can implement Trident like feature on top of it like having implement a transaction-id for a given block , and re-emit the same block with same set of message during Driver failure. Regards, Dibyendu On Fri, Dec 19, 2014 at 7:33 AM, Shao, Saisai saisai.s...@intel.com wrote: Hi all, I agree with Hari that Strong exact-once semantics is very hard to guarantee, especially in the failure situation. From my understanding even current implementation of ReliableKafkaReceiver cannot fully guarantee the exact once semantics once failed, first is the ordering of data replaying from last checkpoint, this is hard to guarantee when multiple partitions are injected in; second is the design complexity of achieving this, you can refer to the Kafka Spout in Trident, we have to dig into the very details of Kafka metadata management system to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto: langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Cody, I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight for that one. For the Kafka DStream skipping the WAL implementation - this is something I discussed with TD a few weeks ago. Though it is a good idea to implement this to avoid unnecessary HDFS writes, it is an optimization. For that reason, we must be careful in implementation. There are a couple of issues that we need to ensure works properly - specifically ordering. To ensure we pull messages from different topics and partitions in the same order after failure, we’d still have to persist the metadata to HDFS (or some other system) - this metadata must contain the order of messages consumed, so we know how to re-read the messages. I am planning to explore
RE: Which committers care about Kafka?
Hi Cody, From my understanding rate control is an optional configuration in Spark Streaming and is disabled by default, so user can reach maximum throughput without any configuration. The reason why rate control is so important in streaming processing is that Spark Streaming and other streaming frameworks are easily prone to unexpected behavior and failure situation due to network boost and other un-controllable inject rate. Especially for Spark Streaming, the large amount of processed data will delay the processing time, which will further delay the ongoing job, and finally lead to failure. Thanks Jerry From: Cody Koeninger [mailto:c...@koeninger.org] Sent: Tuesday, December 30, 2014 6:50 AM To: Tathagata Das Cc: Hari Shreedharan; Shao, Saisai; Sean McNamara; Patrick Wendell; Luis Ángel Vicente Sánchez; Dibyendu Bhattacharya; dev@spark.apache.org; Koert Kuipers Subject: Re: Which committers care about Kafka? Can you give a little more clarification on exactly what is meant by 1. Data rate control If someone wants to clamp the maximum number of messages per RDD partition in my solution, it would be very straightforward to do so. Regarding the holy grail, I'm pretty certain you can't have end-to-end transactional semantics without the client code being in charge of offset state. That means the client code is going to also need to be in charge of setting up an initial state for updateStateByKey that makes sense; as long as they can do that, the job should be safe to restart from arbitrary failures. On Mon, Dec 29, 2014 at 4:33 PM, Tathagata Das tathagata.das1...@gmail.commailto:tathagata.das1...@gmail.com wrote: Hey all, Some wrap up thoughts on this thread. Let me first reiterate what Patrick said, that Kafka is super super important as it forms the largest fraction of Spark Streaming user base. So we really want to improve the Kafka + Spark Streaming integration. To this end, some of the things that needs to be considered can be broadly classified into the following to sort facilitate the discussion. 1. Data rate control 2. Receiver failure semantics - partially achieving this gives at-least once, completely achieving this gives exactly-once 3. Driver failure semantics - partially achieving this gives at-least once, completely achieving this gives exactly-once Here is a run down of what is achieved by different implementations (based on what I think). 1. Prior to WAL in Spark 1.2, the KafkaReceiver could handle 3, could handle 1 partially (some duplicate data), and could NOT handle 2 (all previously received data lost). 2. In Spark 1.2 with WAL enabled, the Saisai's ReliableKafkaReceiver can handle 3, can almost completely handle 1 and 2 (except few corner cases which prevents it from completely guaranteeing exactly-once). 3. I believe Dibyendu's solution (correct me if i am wrong) can handle 1 and 2 perfectly. And 3 can be partially solved with WAL, or possibly completely solved by extending the solution further. 4. Cody's solution (again, correct me if I am wrong) does not use receivers at all (so eliminates 2). It can handle 3 completely for simple operations like map and filter, but not sure if it works completely for stateful ops like windows and updateStateByKey. Also it does not handle 1. The real challenge for Kafka is in achieving 3 completely for stateful operations while also handling 1. (i.e., use receivers, but still get driver failure guarantees). Solving this will give us our holy grail solution, and this is what I want to achieve. On that note, Cody submitted a PR on his style of achieving exactly-once semantics - https://github.com/apache/spark/pull/3798 . I am reviewing it. Please follow the PR if you are interested. TD On Wed, Dec 24, 2014 at 11:59 PM, Cody Koeninger c...@koeninger.orgmailto:c...@koeninger.org wrote: The conversation was mostly getting TD up to speed on this thread since he had just gotten back from his trip and hadn't seen it. The jira has a summary of the requirements we discussed, I'm sure TD or Patrick can add to the ticket if I missed something. On Dec 25, 2014 1:54 AM, Hari Shreedharan hshreedha...@cloudera.commailto:hshreedha...@cloudera.com wrote: In general such discussions happen or is posted on the dev lists. Could you please post a summary? Thanks. Thanks, Hari On Wed, Dec 24, 2014 at 11:46 PM, Cody Koeninger c...@koeninger.orgmailto:c...@koeninger.org wrote: After a long talk with Patrick and TD (thanks guys), I opened the following jira https://issues.apache.org/jira/browse/SPARK-4964 Sample PR has an impementation for the batch and the dstream case, and a link to a project with example usage. On Fri, Dec 19, 2014 at 4:36 PM, Koert Kuipers ko...@tresata.commailto:ko...@tresata.com wrote: yup, we at tresata do the idempotent store the same way. very simple approach. On Fri, Dec 19, 2014 at 5:32 PM, Cody Koeninger c...@koeninger.orgmailto:c...@koeninger.org wrote
Re: Which committers care about Kafka?
Assuming you're talking about spark.streaming.receiver.maxRate, I just updated my PR to configure rate limiting based on that setting. So hopefully that's issue 1 sorted. Regarding issue 3, as far as I can tell regarding the odd semantics of stateful or windowed operations in the face of failure, my solution is no worse than existing classes such as FileStream that use inputdstream directly rather than a receiver. Can we get some specific cases that are a concern? Regarding the WAL solutions TD mentioned, one of the disadvantages of them is that they rely on checkpointing, unlike my approach. As I noted in this thread and in the jira ticket, I need something that can recover even when a checkpoint is lost, and I've already seen multiple situations in production where a checkpoint cannot be recovered (e.g. because code needs to be updated). On Mon, Dec 29, 2014 at 7:50 PM, Shao, Saisai saisai.s...@intel.com wrote: Hi Cody, From my understanding rate control is an optional configuration in Spark Streaming and is disabled by default, so user can reach maximum throughput without any configuration. The reason why rate control is so important in streaming processing is that Spark Streaming and other streaming frameworks are easily prone to unexpected behavior and failure situation due to network boost and other un-controllable inject rate. Especially for Spark Streaming, the large amount of processed data will delay the processing time, which will further delay the ongoing job, and finally lead to failure. Thanks Jerry *From:* Cody Koeninger [mailto:c...@koeninger.org] *Sent:* Tuesday, December 30, 2014 6:50 AM *To:* Tathagata Das *Cc:* Hari Shreedharan; Shao, Saisai; Sean McNamara; Patrick Wendell; Luis Ángel Vicente Sánchez; Dibyendu Bhattacharya; dev@spark.apache.org; Koert Kuipers *Subject:* Re: Which committers care about Kafka? Can you give a little more clarification on exactly what is meant by 1. Data rate control If someone wants to clamp the maximum number of messages per RDD partition in my solution, it would be very straightforward to do so. Regarding the holy grail, I'm pretty certain you can't have end-to-end transactional semantics without the client code being in charge of offset state. That means the client code is going to also need to be in charge of setting up an initial state for updateStateByKey that makes sense; as long as they can do that, the job should be safe to restart from arbitrary failures. On Mon, Dec 29, 2014 at 4:33 PM, Tathagata Das tathagata.das1...@gmail.com wrote: Hey all, Some wrap up thoughts on this thread. Let me first reiterate what Patrick said, that Kafka is super super important as it forms the largest fraction of Spark Streaming user base. So we really want to improve the Kafka + Spark Streaming integration. To this end, some of the things that needs to be considered can be broadly classified into the following to sort facilitate the discussion. 1. Data rate control 2. Receiver failure semantics - partially achieving this gives at-least once, completely achieving this gives exactly-once 3. Driver failure semantics - partially achieving this gives at-least once, completely achieving this gives exactly-once Here is a run down of what is achieved by different implementations (based on what I think). 1. Prior to WAL in Spark 1.2, the KafkaReceiver could handle 3, could handle 1 partially (some duplicate data), and could NOT handle 2 (all previously received data lost). 2. In Spark 1.2 with WAL enabled, the Saisai's ReliableKafkaReceiver can handle 3, can almost completely handle 1 and 2 (except few corner cases which prevents it from completely guaranteeing exactly-once). 3. I believe Dibyendu's solution (correct me if i am wrong) can handle 1 and 2 perfectly. And 3 can be partially solved with WAL, or possibly completely solved by extending the solution further. 4. Cody's solution (again, correct me if I am wrong) does not use receivers at all (so eliminates 2). It can handle 3 completely for simple operations like map and filter, but not sure if it works completely for stateful ops like windows and updateStateByKey. Also it does not handle 1. The real challenge for Kafka is in achieving 3 completely for stateful operations while also handling 1. (i.e., use receivers, but still get driver failure guarantees). Solving this will give us our holy grail solution, and this is what I want to achieve. On that note, Cody submitted a PR on his style of achieving exactly-once semantics - https://github.com/apache/spark/pull/3798 . I am reviewing it. Please follow the PR if you are interested. TD On Wed, Dec 24, 2014 at 11:59 PM, Cody Koeninger c...@koeninger.org wrote: The conversation was mostly getting TD up to speed on this thread since he had just gotten back from his trip and hadn't seen it. The jira has a summary of the requirements we
Re: Which committers care about Kafka?
in failure scenarios (for example, the transaction is processed but before ZK is updated the machine fails, causing a new node to process it again). I don't think it is impossible to do this in Spark Streaming as well and I'd be really interested in working on it at some point in the near future. On Fri, Dec 19, 2014 at 1:44 AM, Dibyendu Bhattacharya dibyendu.bhattach...@gmail.com wrote: Hi, Thanks to Jerry for mentioning the Kafka Spout for Trident. The Storm Trident has done the exact-once guarantee by processing the tuple in a batch and assigning same transaction-id for a given batch . The replay for a given batch with a transaction-id will have exact same set of tuples and replay of batches happen in exact same order before the failure. Having this paradigm, if downstream system process data for a given batch for having a given transaction-id , and if during failure if same batch is again emitted , you can check if same transaction-id is already processed or not and hence can guarantee exact once semantics. And this can only be achieved in Spark if we use Low Level Kafka consumer API to process the offsets. This low level Kafka Consumer ( https://github.com/dibbhatt/kafka-spark-consumer) has implemented the Spark Kafka consumer which uses Kafka Low Level APIs . All of the Kafka related logic has been taken from Storm-Kafka spout and which manages all Kafka re-balance and fault tolerant aspects and Kafka metadata managements. Presently this Consumer maintains that during Receiver failure, it will re-emit the exact same Block with same set of messages . Every message have the details of its partition, offset and topic related details which can tackle the SPARK-3146. As this Low Level consumer has complete control over the Kafka Offsets , we can implement Trident like feature on top of it like having implement a transaction-id for a given block , and re-emit the same block with same set of message during Driver failure. Regards, Dibyendu On Fri, Dec 19, 2014 at 7:33 AM, Shao, Saisai saisai.s...@intel.com wrote: Hi all, I agree with Hari that Strong exact-once semantics is very hard to guarantee, especially in the failure situation. From my understanding even current implementation of ReliableKafkaReceiver cannot fully guarantee the exact once semantics once failed, first is the ordering of data replaying from last checkpoint, this is hard to guarantee when multiple partitions are injected in; second is the design complexity of achieving this, you can refer to the Kafka Spout in Trident, we have to dig into the very details of Kafka metadata management system to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto: langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about
Re: Which committers care about Kafka?
by processing the tuple in a batch and assigning same transaction-id for a given batch . The replay for a given batch with a transaction-id will have exact same set of tuples and replay of batches happen in exact same order before the failure. Having this paradigm, if downstream system process data for a given batch for having a given transaction-id , and if during failure if same batch is again emitted , you can check if same transaction-id is already processed or not and hence can guarantee exact once semantics. And this can only be achieved in Spark if we use Low Level Kafka consumer API to process the offsets. This low level Kafka Consumer ( https://github.com/dibbhatt/kafka-spark-consumer) has implemented the Spark Kafka consumer which uses Kafka Low Level APIs . All of the Kafka related logic has been taken from Storm-Kafka spout and which manages all Kafka re-balance and fault tolerant aspects and Kafka metadata managements. Presently this Consumer maintains that during Receiver failure, it will re-emit the exact same Block with same set of messages . Every message have the details of its partition, offset and topic related details which can tackle the SPARK-3146. As this Low Level consumer has complete control over the Kafka Offsets , we can implement Trident like feature on top of it like having implement a transaction-id for a given block , and re-emit the same block with same set of message during Driver failure. Regards, Dibyendu On Fri, Dec 19, 2014 at 7:33 AM, Shao, Saisai saisai.s...@intel.com wrote: Hi all, I agree with Hari that Strong exact-once semantics is very hard to guarantee, especially in the failure situation. From my understanding even current implementation of ReliableKafkaReceiver cannot fully guarantee the exact once semantics once failed, first is the ordering of data replaying from last checkpoint, this is hard to guarantee when multiple partitions are injected in; second is the design complexity of achieving this, you can refer to the Kafka Spout in Trident, we have to dig into the very details of Kafka metadata management system to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto:langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted
Re: Which committers care about Kafka?
:44 AM, Dibyendu Bhattacharya dibyendu.bhattach...@gmail.com wrote: Hi, Thanks to Jerry for mentioning the Kafka Spout for Trident. The Storm Trident has done the exact-once guarantee by processing the tuple in a batch and assigning same transaction-id for a given batch . The replay for a given batch with a transaction-id will have exact same set of tuples and replay of batches happen in exact same order before the failure. Having this paradigm, if downstream system process data for a given batch for having a given transaction-id , and if during failure if same batch is again emitted , you can check if same transaction-id is already processed or not and hence can guarantee exact once semantics. And this can only be achieved in Spark if we use Low Level Kafka consumer API to process the offsets. This low level Kafka Consumer ( https://github.com/dibbhatt/kafka-spark-consumer) has implemented the Spark Kafka consumer which uses Kafka Low Level APIs . All of the Kafka related logic has been taken from Storm-Kafka spout and which manages all Kafka re-balance and fault tolerant aspects and Kafka metadata managements. Presently this Consumer maintains that during Receiver failure, it will re-emit the exact same Block with same set of messages . Every message have the details of its partition, offset and topic related details which can tackle the SPARK-3146. As this Low Level consumer has complete control over the Kafka Offsets , we can implement Trident like feature on top of it like having implement a transaction-id for a given block , and re-emit the same block with same set of message during Driver failure. Regards, Dibyendu On Fri, Dec 19, 2014 at 7:33 AM, Shao, Saisai saisai.s...@intel.com wrote: Hi all, I agree with Hari that Strong exact-once semantics is very hard to guarantee, especially in the failure situation. From my understanding even current implementation of ReliableKafkaReceiver cannot fully guarantee the exact once semantics once failed, first is the ordering of data replaying from last checkpoint, this is hard to guarantee when multiple partitions are injected in; second is the design complexity of achieving this, you can refer to the Kafka Spout in Trident, we have to dig into the very details of Kafka metadata management system to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto:langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store
Re: Which committers care about Kafka?
Hi, Thanks to Jerry for mentioning the Kafka Spout for Trident. The Storm Trident has done the exact-once guarantee by processing the tuple in a batch and assigning same transaction-id for a given batch . The replay for a given batch with a transaction-id will have exact same set of tuples and replay of batches happen in exact same order before the failure. Having this paradigm, if downstream system process data for a given batch for having a given transaction-id , and if during failure if same batch is again emitted , you can check if same transaction-id is already processed or not and hence can guarantee exact once semantics. And this can only be achieved in Spark if we use Low Level Kafka consumer API to process the offsets. This low level Kafka Consumer ( https://github.com/dibbhatt/kafka-spark-consumer) has implemented the Spark Kafka consumer which uses Kafka Low Level APIs . All of the Kafka related logic has been taken from Storm-Kafka spout and which manages all Kafka re-balance and fault tolerant aspects and Kafka metadata managements. Presently this Consumer maintains that during Receiver failure, it will re-emit the exact same Block with same set of messages . Every message have the details of its partition, offset and topic related details which can tackle the SPARK-3146. As this Low Level consumer has complete control over the Kafka Offsets , we can implement Trident like feature on top of it like having implement a transaction-id for a given block , and re-emit the same block with same set of message during Driver failure. Regards, Dibyendu On Fri, Dec 19, 2014 at 7:33 AM, Shao, Saisai saisai.s...@intel.com wrote: Hi all, I agree with Hari that Strong exact-once semantics is very hard to guarantee, especially in the failure situation. From my understanding even current implementation of ReliableKafkaReceiver cannot fully guarantee the exact once semantics once failed, first is the ordering of data replaying from last checkpoint, this is hard to guarantee when multiple partitions are injected in; second is the design complexity of achieving this, you can refer to the Kafka Spout in Trident, we have to dig into the very details of Kafka metadata management system to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto:langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec
Re: Which committers care about Kafka?
Hi Dibyendu, Thanks for the details on the implementation. But I still do not believe that it is no duplicates - what they achieve is that the same batch is processed exactly the same way every time (but see it may be processed more than once) - so it depends on the operation being idempotent. I believe Trident uses ZK to keep track of the transactions - a batch can be processed multiple times in failure scenarios (for example, the transaction is processed but before ZK is updated the machine fails, causing a new node to process it again). I don't think it is impossible to do this in Spark Streaming as well and I'd be really interested in working on it at some point in the near future. On Fri, Dec 19, 2014 at 1:44 AM, Dibyendu Bhattacharya dibyendu.bhattach...@gmail.com wrote: Hi, Thanks to Jerry for mentioning the Kafka Spout for Trident. The Storm Trident has done the exact-once guarantee by processing the tuple in a batch and assigning same transaction-id for a given batch . The replay for a given batch with a transaction-id will have exact same set of tuples and replay of batches happen in exact same order before the failure. Having this paradigm, if downstream system process data for a given batch for having a given transaction-id , and if during failure if same batch is again emitted , you can check if same transaction-id is already processed or not and hence can guarantee exact once semantics. And this can only be achieved in Spark if we use Low Level Kafka consumer API to process the offsets. This low level Kafka Consumer ( https://github.com/dibbhatt/kafka-spark-consumer) has implemented the Spark Kafka consumer which uses Kafka Low Level APIs . All of the Kafka related logic has been taken from Storm-Kafka spout and which manages all Kafka re-balance and fault tolerant aspects and Kafka metadata managements. Presently this Consumer maintains that during Receiver failure, it will re-emit the exact same Block with same set of messages . Every message have the details of its partition, offset and topic related details which can tackle the SPARK-3146. As this Low Level consumer has complete control over the Kafka Offsets , we can implement Trident like feature on top of it like having implement a transaction-id for a given block , and re-emit the same block with same set of message during Driver failure. Regards, Dibyendu On Fri, Dec 19, 2014 at 7:33 AM, Shao, Saisai saisai.s...@intel.com wrote: Hi all, I agree with Hari that Strong exact-once semantics is very hard to guarantee, especially in the failure situation. From my understanding even current implementation of ReliableKafkaReceiver cannot fully guarantee the exact once semantics once failed, first is the ordering of data replaying from last checkpoint, this is hard to guarantee when multiple partitions are injected in; second is the design complexity of achieving this, you can refer to the Kafka Spout in Trident, we have to dig into the very details of Kafka metadata management system to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto:langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014
Re: Which committers care about Kafka?
Please feel free to correct me if I’m wrong, but I think the exactly once spark streaming semantics can easily be solved using updateStateByKey. Make the key going into updateStateByKey be a hash of the event, or pluck off some uuid from the message. The updateFunc would only emit the message if the key did not exist, and the user has complete control over the window of time / state lifecycle for detecting duplicates. It also makes it really easy to detect and take action (alert?) when you DO see a duplicate, or make memory tradeoffs within an error bound using a sketch algorithm. The kafka simple consumer is insanely complex, if possible I think it would be better (and vastly more flexible) to get reliability using the primitives that spark so elegantly provides. Cheers, Sean On Dec 19, 2014, at 12:06 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Dibyendu, Thanks for the details on the implementation. But I still do not believe that it is no duplicates - what they achieve is that the same batch is processed exactly the same way every time (but see it may be processed more than once) - so it depends on the operation being idempotent. I believe Trident uses ZK to keep track of the transactions - a batch can be processed multiple times in failure scenarios (for example, the transaction is processed but before ZK is updated the machine fails, causing a new node to process it again). I don't think it is impossible to do this in Spark Streaming as well and I'd be really interested in working on it at some point in the near future. On Fri, Dec 19, 2014 at 1:44 AM, Dibyendu Bhattacharya dibyendu.bhattach...@gmail.com wrote: Hi, Thanks to Jerry for mentioning the Kafka Spout for Trident. The Storm Trident has done the exact-once guarantee by processing the tuple in a batch and assigning same transaction-id for a given batch . The replay for a given batch with a transaction-id will have exact same set of tuples and replay of batches happen in exact same order before the failure. Having this paradigm, if downstream system process data for a given batch for having a given transaction-id , and if during failure if same batch is again emitted , you can check if same transaction-id is already processed or not and hence can guarantee exact once semantics. And this can only be achieved in Spark if we use Low Level Kafka consumer API to process the offsets. This low level Kafka Consumer ( https://github.com/dibbhatt/kafka-spark-consumer) has implemented the Spark Kafka consumer which uses Kafka Low Level APIs . All of the Kafka related logic has been taken from Storm-Kafka spout and which manages all Kafka re-balance and fault tolerant aspects and Kafka metadata managements. Presently this Consumer maintains that during Receiver failure, it will re-emit the exact same Block with same set of messages . Every message have the details of its partition, offset and topic related details which can tackle the SPARK-3146. As this Low Level consumer has complete control over the Kafka Offsets , we can implement Trident like feature on top of it like having implement a transaction-id for a given block , and re-emit the same block with same set of message during Driver failure. Regards, Dibyendu On Fri, Dec 19, 2014 at 7:33 AM, Shao, Saisai saisai.s...@intel.com wrote: Hi all, I agree with Hari that Strong exact-once semantics is very hard to guarantee, especially in the failure situation. From my understanding even current implementation of ReliableKafkaReceiver cannot fully guarantee the exact once semantics once failed, first is the ordering of data replaying from last checkpoint, this is hard to guarantee when multiple partitions are injected in; second is the design complexity of achieving this, you can refer to the Kafka Spout in Trident, we have to dig into the very details of Kafka metadata management system to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto:langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014
Re: Which committers care about Kafka?
Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Cody, I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight for that one. For the Kafka DStream skipping the WAL implementation - this is something I discussed with TD a few weeks ago. Though it is a good idea to implement this to avoid unnecessary HDFS writes, it is an optimization. For that reason, we must be careful in implementation. There are a couple of issues that we need to ensure works properly - specifically ordering. To ensure we pull messages from different topics and partitions in the same order after failure, we’d still have to persist the metadata to HDFS (or some other system) - this metadata must contain the order of messages consumed, so we know how to re-read the messages. I am planning to explore this once I have some time (probably in Jan). In addition, we must also ensure bucketing functions work fine as well. I will file a placeholder jira for this one. I also wrote an API to write data back to Kafka a while back - https://github.com/apache/spark/pull/2994 . I am hoping that this will get pulled in soon, as this is something I know people want. I am open to feedback on that - anything that I can do to make it better. Thanks, Hari On Thu, Dec 18, 2014 at 11:14 AM, Patrick Wendell pwend...@gmail.com wrote: Hey Cody, Thanks for reaching out with this. The lead on streaming is TD - he is traveling this week though so I can respond a bit. To the high level point of whether Kafka is important - it definitely is. Something like 80% of Spark Streaming deployments (anecdotally) ingest data from Kafka. Also, good support for Kafka is something we generally want in Spark and not a library. In some cases IIRC there were user libraries that used unstable Kafka API's and we were somewhat waiting on Kafka to stabilize them to merge things upstream. Otherwise users wouldn't be able to use newer Kakfa versions. This is a high level impression only though, I haven't talked to TD about this recently so it's worth revisiting given the developments in Kafka. Please do bring things up like this on the dev list if there are blockers for your usage - thanks for pinging it. - Patrick On Thu, Dec 18, 2014 at 7:07 AM, Cody Koeninger c...@koeninger.org wrote: Now that 1.2 is finalized... who are the go-to people to get some long-standing Kafka related issues resolved
Re: Which committers care about Kafka?
metadata management system to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto:langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Cody, I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight for that one. For the Kafka DStream skipping the WAL implementation - this is something I discussed with TD a few weeks ago. Though it is a good idea to implement this to avoid unnecessary HDFS writes, it is an optimization. For that reason, we must be careful in implementation. There are a couple of issues that we need to ensure works properly - specifically ordering. To ensure we pull messages from different topics and partitions in the same order after failure, we’d still have to persist the metadata to HDFS (or some other system) - this metadata must contain the order of messages consumed, so we know how to re-read the messages. I am planning to explore this once I have some time (probably in Jan). In addition, we must also ensure bucketing functions work fine as well. I will file a placeholder jira for this one. I also wrote an API to write data back to Kafka a while back - https://github.com/apache/spark/pull/2994 . I am hoping that this will get pulled in soon, as this is something I know people want. I am open to feedback on that - anything that I can do to make it better. Thanks, Hari On Thu, Dec 18, 2014 at 11:14 AM, Patrick Wendell pwend...@gmail.com wrote: Hey Cody, Thanks for reaching out with this. The lead on streaming is TD - he is traveling this week though so I can respond a bit. To the high level point of whether Kafka is important - it definitely is. Something like 80% of Spark Streaming deployments (anecdotally) ingest data from Kafka. Also, good support for Kafka is something we generally want in Spark and not a library. In some cases IIRC there were user libraries that used unstable Kafka API's and we were somewhat waiting on Kafka to stabilize them to merge things upstream. Otherwise users wouldn't be able to use newer Kakfa versions. This is a high level impression only though, I haven't talked to TD about this recently so it's worth revisiting given the developments in Kafka
Re: Which committers care about Kafka?
Kafka consumer API to process the offsets. This low level Kafka Consumer ( https://github.com/dibbhatt/kafka-spark-consumer) has implemented the Spark Kafka consumer which uses Kafka Low Level APIs . All of the Kafka related logic has been taken from Storm-Kafka spout and which manages all Kafka re-balance and fault tolerant aspects and Kafka metadata managements. Presently this Consumer maintains that during Receiver failure, it will re-emit the exact same Block with same set of messages . Every message have the details of its partition, offset and topic related details which can tackle the SPARK-3146. As this Low Level consumer has complete control over the Kafka Offsets , we can implement Trident like feature on top of it like having implement a transaction-id for a given block , and re-emit the same block with same set of message during Driver failure. Regards, Dibyendu On Fri, Dec 19, 2014 at 7:33 AM, Shao, Saisai saisai.s...@intel.com wrote: Hi all, I agree with Hari that Strong exact-once semantics is very hard to guarantee, especially in the failure situation. From my understanding even current implementation of ReliableKafkaReceiver cannot fully guarantee the exact once semantics once failed, first is the ordering of data replaying from last checkpoint, this is hard to guarantee when multiple partitions are injected in; second is the design complexity of achieving this, you can refer to the Kafka Spout in Trident, we have to dig into the very details of Kafka metadata management system to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto:langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Cody, I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight for that one. For the Kafka DStream skipping the WAL implementation - this is something I discussed with TD a few weeks ago. Though it is a good idea to implement this to avoid unnecessary HDFS writes, it is an optimization. For that reason, we must be careful in implementation. There are a couple of issues that we need to ensure works properly - specifically ordering. To ensure we pull messages from different topics
Re: Which committers care about Kafka?
for a given batch for having a given transaction-id , and if during failure if same batch is again emitted , you can check if same transaction-id is already processed or not and hence can guarantee exact once semantics. And this can only be achieved in Spark if we use Low Level Kafka consumer API to process the offsets. This low level Kafka Consumer ( https://github.com/dibbhatt/kafka-spark-consumer) has implemented the Spark Kafka consumer which uses Kafka Low Level APIs . All of the Kafka related logic has been taken from Storm-Kafka spout and which manages all Kafka re-balance and fault tolerant aspects and Kafka metadata managements. Presently this Consumer maintains that during Receiver failure, it will re-emit the exact same Block with same set of messages . Every message have the details of its partition, offset and topic related details which can tackle the SPARK-3146. As this Low Level consumer has complete control over the Kafka Offsets , we can implement Trident like feature on top of it like having implement a transaction-id for a given block , and re-emit the same block with same set of message during Driver failure. Regards, Dibyendu On Fri, Dec 19, 2014 at 7:33 AM, Shao, Saisai saisai.s...@intel.com wrote: Hi all, I agree with Hari that Strong exact-once semantics is very hard to guarantee, especially in the failure situation. From my understanding even current implementation of ReliableKafkaReceiver cannot fully guarantee the exact once semantics once failed, first is the ordering of data replaying from last checkpoint, this is hard to guarantee when multiple partitions are injected in; second is the design complexity of achieving this, you can refer to the Kafka Spout in Trident, we have to dig into the very details of Kafka metadata management system to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto:langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Cody, I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight
Re: Which committers care about Kafka?
Hey Cody, Thanks for reaching out with this. The lead on streaming is TD - he is traveling this week though so I can respond a bit. To the high level point of whether Kafka is important - it definitely is. Something like 80% of Spark Streaming deployments (anecdotally) ingest data from Kafka. Also, good support for Kafka is something we generally want in Spark and not a library. In some cases IIRC there were user libraries that used unstable Kafka API's and we were somewhat waiting on Kafka to stabilize them to merge things upstream. Otherwise users wouldn't be able to use newer Kakfa versions. This is a high level impression only though, I haven't talked to TD about this recently so it's worth revisiting given the developments in Kafka. Please do bring things up like this on the dev list if there are blockers for your usage - thanks for pinging it. - Patrick On Thu, Dec 18, 2014 at 7:07 AM, Cody Koeninger c...@koeninger.org wrote: Now that 1.2 is finalized... who are the go-to people to get some long-standing Kafka related issues resolved? The existing api is not sufficiently safe nor flexible for our production use. I don't think we're alone in this viewpoint, because I've seen several different patches and libraries to fix the same things we've been running into. Regarding flexibility https://issues.apache.org/jira/browse/SPARK-3146 has been outstanding since August, and IMHO an equivalent of this is absolutely necessary. We wrote a similar patch ourselves, then found that PR and have been running it in production. We wouldn't be able to get our jobs done without it. It also allows users to solve a whole class of problems for themselves (e.g. SPARK-2388, arbitrary delay of messages, etc). Regarding safety, I understand the motivation behind WriteAheadLog as a general solution for streaming unreliable sources, but Kafka already is a reliable source. I think there's a need for an api that treats it as such. Even aside from the performance issues of duplicating the write-ahead log in kafka into another write-ahead log in hdfs, I need exactly-once semantics in the face of failure (I've had failures that prevented reloading a spark streaming checkpoint, for instance). I've got an implementation i've been using https://github.com/koeninger/spark-1/tree/kafkaRdd/external/kafka /src/main/scala/org/apache/spark/rdd/kafka Tresata has something similar at https://github.com/tresata/spark-kafka, and I know there were earlier attempts based on Storm code. Trying to distribute these kinds of fixes as libraries rather than patches to Spark is problematic, because large portions of the implementation are private[spark]. I'd like to help, but i need to know whose attention to get. - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
Re: Which committers care about Kafka?
Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Cody, I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight for that one. For the Kafka DStream skipping the WAL implementation - this is something I discussed with TD a few weeks ago. Though it is a good idea to implement this to avoid unnecessary HDFS writes, it is an optimization. For that reason, we must be careful in implementation. There are a couple of issues that we need to ensure works properly - specifically ordering. To ensure we pull messages from different topics and partitions in the same order after failure, we’d still have to persist the metadata to HDFS (or some other system) - this metadata must contain the order of messages consumed, so we know how to re-read the messages. I am planning to explore this once I have some time (probably in Jan). In addition, we must also ensure bucketing functions work fine as well. I will file a placeholder jira for this one. I also wrote an API to write data back to Kafka a while back - https://github.com/apache/spark/pull/2994 . I am hoping that this will get pulled in soon, as this is something I know people want. I am open to feedback on that - anything that I can do to make it better. Thanks, Hari On Thu, Dec 18, 2014 at 11:14 AM, Patrick Wendell pwend...@gmail.com wrote: Hey Cody, Thanks for reaching out with this. The lead on streaming is TD - he is traveling this week though so I can respond a bit. To the high level point of whether Kafka is important - it definitely is. Something like 80% of Spark Streaming deployments (anecdotally) ingest data from Kafka. Also, good support for Kafka is something we generally want in Spark and not a library. In some cases IIRC there were user libraries that used unstable Kafka API's and we were somewhat waiting on Kafka to stabilize them to merge things upstream. Otherwise users wouldn't be able to use newer Kakfa versions. This is a high level impression only though, I haven't talked to TD about this recently so it's worth revisiting given the developments in Kafka. Please do bring things up like this on the dev list if there are blockers for your usage - thanks for pinging it. - Patrick On Thu, Dec 18, 2014 at 7:07 AM, Cody Koeninger c...@koeninger.org wrote: Now that 1.2 is finalized... who are the go-to people to get some long-standing Kafka related issues resolved? The existing api is not sufficiently safe nor flexible for our production use. I don't think we're alone in this viewpoint, because I've seen several different patches and libraries to fix the same things we've been running into. Regarding flexibility https://issues.apache.org/jira/browse/SPARK-3146 has been outstanding since August, and IMHO an equivalent of this is absolutely necessary. We wrote a similar patch ourselves, then found that PR and have been running it in production. We wouldn't be able to get our jobs done without it. It also allows users to solve a whole class of problems for themselves (e.g. SPARK-2388, arbitrary delay of messages, etc). Regarding safety, I understand the motivation behind WriteAheadLog as a general solution for streaming unreliable sources, but Kafka already is a reliable source. I think there's a need for an api that treats it as such. Even aside from the performance issues of duplicating the write-ahead log in kafka into another write-ahead log in hdfs, I need exactly-once semantics in the face of failure (I've had failures that prevented reloading a spark streaming checkpoint, for instance). I've got an implementation i've been using https://github.com/koeninger/spark-1/tree/kafkaRdd/external/kafka /src/main/scala/org/apache/spark/rdd/kafka Tresata has something similar at https://github.com/tresata/spark-kafka, and I know there were earlier attempts based on Storm code. Trying to distribute these kinds of fixes as libraries rather than patches to Spark is problematic, because large portions of the implementation are private[spark]. I'd like to help, but i need to know whose attention to get. - To
Re: Which committers care about Kafka?
I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Cody, I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight for that one. For the Kafka DStream skipping the WAL implementation - this is something I discussed with TD a few weeks ago. Though it is a good idea to implement this to avoid unnecessary HDFS writes, it is an optimization. For that reason, we must be careful in implementation. There are a couple of issues that we need to ensure works properly - specifically ordering. To ensure we pull messages from different topics and partitions in the same order after failure, we’d still have to persist the metadata to HDFS (or some other system) - this metadata must contain the order of messages consumed, so we know how to re-read the messages. I am planning to explore this once I have some time (probably in Jan). In addition, we must also ensure bucketing functions work fine as well. I will file a placeholder jira for this one. I also wrote an API to write data back to Kafka a while back - https://github.com/apache/spark/pull/2994 . I am hoping that this will get pulled in soon, as this is something I know people want. I am open to feedback on that - anything that I can do to make it better. Thanks, Hari On Thu, Dec 18, 2014 at 11:14 AM, Patrick Wendell pwend...@gmail.com wrote: Hey Cody, Thanks for reaching out with this. The lead on streaming is TD - he is traveling this week though so I can respond a bit. To the high level point of whether Kafka is important - it definitely is. Something like 80% of Spark Streaming deployments (anecdotally) ingest data from Kafka. Also, good support for Kafka is something we generally want in Spark and not a library. In some cases IIRC there were user libraries that used unstable Kafka API's and we were somewhat waiting on Kafka to stabilize them to merge things upstream. Otherwise users wouldn't be able to use newer Kakfa versions. This is a high level impression only though, I haven't talked to TD about this recently so it's worth revisiting given the developments in Kafka. Please do bring things up like this on the dev list if there are blockers for your usage - thanks for pinging it. - Patrick On Thu, Dec 18, 2014 at 7:07 AM, Cody Koeninger c...@koeninger.org wrote: Now that 1.2 is finalized... who are the go-to people to get some long-standing Kafka related issues resolved? The existing api is not sufficiently safe nor flexible for our production use. I don't think we're alone in this viewpoint, because I've seen several different patches and libraries to fix the same things we've been running into. Regarding flexibility https://issues.apache.org/jira/browse/SPARK-3146 has been outstanding since August, and IMHO an equivalent of this is absolutely necessary. We wrote a similar patch ourselves, then found that PR and have been running it in production. We wouldn't be able to get our jobs done without it. It also allows users to solve a whole class of problems for themselves (e.g. SPARK-2388, arbitrary delay of messages, etc). Regarding safety, I understand the motivation behind WriteAheadLog as a general solution for streaming unreliable sources, but Kafka already is a reliable source. I think there's a need for an api that treats it as such. Even aside from the performance
Re: Which committers care about Kafka?
If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Cody, I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight for that one. For the Kafka DStream skipping the WAL implementation - this is something I discussed with TD a few weeks ago. Though it is a good idea to implement this to avoid unnecessary HDFS writes, it is an optimization. For that reason, we must be careful in implementation. There are a couple of issues that we need to ensure works properly - specifically ordering. To ensure we pull messages from different topics and partitions in the same order after failure, we’d still have to persist the metadata to HDFS (or some other system) - this metadata must contain the order of messages consumed, so we know how to re-read the messages. I am planning to explore this once I have some time (probably in Jan). In addition, we must also ensure bucketing functions work fine as well. I will file a placeholder jira for this one. I also wrote an API to write data back to Kafka a while back - https://github.com/apache/spark/pull/2994 . I am hoping that this will get pulled in soon, as this is something I know people want. I am open to feedback on that - anything that I can do to make it better. Thanks, Hari On Thu, Dec 18, 2014 at 11:14 AM, Patrick Wendell pwend...@gmail.com wrote: Hey Cody, Thanks for reaching out with this. The lead on streaming is TD - he is traveling this week though so I can respond a bit. To the high level point of whether Kafka is important - it definitely is. Something like 80% of Spark Streaming deployments (anecdotally) ingest data from Kafka. Also, good support for Kafka is something we generally want in Spark and not a library. In some cases IIRC there were user libraries that used unstable Kafka API's and we were somewhat waiting on Kafka to stabilize them to merge things upstream. Otherwise users wouldn't be able to use newer Kakfa versions. This is a high level impression only though, I haven't talked to TD about this recently so it's worth revisiting given the developments in Kafka. Please do bring things up like this on the dev list if there are blockers for your usage - thanks for pinging it. - Patrick On Thu, Dec 18, 2014 at 7:07 AM, Cody Koeninger c...@koeninger.org wrote: Now that 1.2 is finalized... who are the go-to people to get some long-standing Kafka related issues resolved? The existing api is not sufficiently safe nor flexible for our production use. I don't think we're alone in this viewpoint, because I've seen several different patches and libraries to fix the same things we've been running into. Regarding flexibility https://issues.apache.org/jira/browse/SPARK-3146 has been outstanding since August, and IMHO an equivalent of this is absolutely necessary. We wrote a similar patch ourselves, then found
Re: Which committers care about Kafka?
But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Cody, I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight for that one. For the Kafka DStream skipping the WAL implementation - this is something I discussed with TD a few weeks ago. Though it is a good idea to implement this to avoid unnecessary HDFS writes, it is an optimization. For that reason, we must be careful in implementation. There are a couple of issues that we need to ensure works properly - specifically ordering. To ensure we pull messages from different topics and partitions in the same order after failure, we’d still have to persist the metadata to HDFS (or some other system) - this metadata must contain the order of messages consumed, so we know how to re-read the messages. I am planning to explore this once I have some time (probably in Jan). In addition, we must also ensure bucketing functions work fine as well. I will file a placeholder jira for this one. I also wrote an API to write data back to Kafka a while back - https://github.com/apache/spark/pull/2994 . I am hoping that this will get pulled in soon, as this is something I know people want. I am open to feedback on that - anything that I can do to make it better. Thanks, Hari On Thu, Dec 18, 2014 at 11:14 AM, Patrick Wendell pwend...@gmail.com wrote: Hey Cody, Thanks for reaching out with this. The lead on streaming is TD - he is traveling this week though so I can respond a bit. To the high level point of whether Kafka is important - it definitely is. Something like 80% of Spark Streaming deployments (anecdotally) ingest data from Kafka. Also, good support for Kafka is something we generally want in Spark and not a library. In some cases IIRC there were user libraries that used unstable Kafka API's and we were somewhat waiting on Kafka to stabilize them to merge things upstream. Otherwise users wouldn't be able to use newer Kakfa versions. This is a high level impression only though, I haven't talked to TD about this recently so it's worth revisiting given the developments in Kafka. Please do bring things up like this on the dev list if there are blockers for your usage - thanks for pinging it. - Patrick On Thu, Dec 18, 2014 at 7:07 AM, Cody Koeninger c...@koeninger.org wrote: Now that 1.2 is finalized... who are the go-to people to get some long-standing Kafka related issues resolved? The existing api is not sufficiently safe nor flexible for our production use. I don't think we're alone
RE: Which committers care about Kafka?
Hi all, I agree with Hari that Strong exact-once semantics is very hard to guarantee, especially in the failure situation. From my understanding even current implementation of ReliableKafkaReceiver cannot fully guarantee the exact once semantics once failed, first is the ordering of data replaying from last checkpoint, this is hard to guarantee when multiple partitions are injected in; second is the design complexity of achieving this, you can refer to the Kafka Spout in Trident, we have to dig into the very details of Kafka metadata management system to achieve this, not to say rebalance and fault-tolerance. Thanks Jerry -Original Message- From: Luis Ángel Vicente Sánchez [mailto:langel.gro...@gmail.com] Sent: Friday, December 19, 2014 5:57 AM To: Cody Koeninger Cc: Hari Shreedharan; Patrick Wendell; dev@spark.apache.org Subject: Re: Which committers care about Kafka? But idempotency is not that easy t achieve sometimes. A strong only once semantic through a proper API would be superuseful; but I'm not implying this is easy to achieve. On 18 Dec 2014 21:52, Cody Koeninger c...@koeninger.org wrote: If the downstream store for the output data is idempotent or transactional, and that downstream store also is the system of record for kafka offsets, then you have exactly-once semantics. Commit offsets with / after the data is stored. On any failure, restart from the last committed offsets. Yes, this approach is biased towards the etl-like use cases rather than near-realtime-analytics use cases. On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: I get what you are saying. But getting exactly once right is an extremely hard problem - especially in presence of failure. The issue is failures can happen in a bunch of places. For example, before the notification of downstream store being successful reaches the receiver that updates the offsets, the node fails. The store was successful, but duplicates came in either way. This is something worth discussing by itself - but without uuids etc this might not really be solved even when you think it is. Anyway, I will look at the links. Even I am interested in all of the features you mentioned - no HDFS WAL for Kafka and once-only delivery, but I doubt the latter is really possible to guarantee - though I really would love to have that! Thanks, Hari On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger c...@koeninger.org wrote: Thanks for the replies. Regarding skipping WAL, it's not just about optimization. If you actually want exactly-once semantics, you need control of kafka offsets as well, including the ability to not use zookeeper as the system of record for offsets. Kafka already is a reliable system that has strong ordering guarantees (within a partition) and does not mandate the use of zookeeper to store offsets. I think there should be a spark api that acts as a very simple intermediary between Kafka and the user's choice of downstream store. Take a look at the links I posted - if there's already been 2 independent implementations of the idea, chances are it's something people need. On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan hshreedha...@cloudera.com wrote: Hi Cody, I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight for that one. For the Kafka DStream skipping the WAL implementation - this is something I discussed with TD a few weeks ago. Though it is a good idea to implement this to avoid unnecessary HDFS writes, it is an optimization. For that reason, we must be careful in implementation. There are a couple of issues that we need to ensure works properly - specifically ordering. To ensure we pull messages from different topics and partitions in the same order after failure, we’d still have to persist the metadata to HDFS (or some other system) - this metadata must contain the order of messages consumed, so we know how to re-read the messages. I am planning to explore this once I have some time (probably in Jan). In addition, we must also ensure bucketing functions work fine as well. I will file a placeholder jira for this one. I also wrote an API to write data back to Kafka a while back - https://github.com/apache/spark/pull/2994 . I am hoping that this will get pulled in soon, as this is something I know people want. I am open to feedback on that - anything that I can do to make it better. Thanks, Hari On Thu, Dec 18, 2014 at 11:14 AM, Patrick Wendell pwend...@gmail.com wrote: Hey Cody, Thanks for reaching out with this. The lead on streaming is TD - he is traveling this week though so I can respond a bit. To the high level point of whether Kafka is important - it definitely is. Something like 80% of Spark
