March 27, 2023

How Airbnb constructed a persistent, excessive availability and low latency key-value storage engine for accessing derived knowledge from offline and streaming occasions.

By: Chandramouli Rangarajan, Shouyan Guo, Yuxi Jin

Inside Airbnb, many on-line companies want entry to derived knowledge, which is knowledge computed with massive scale knowledge processing engines like Spark or streaming occasions like Kafka and saved offline. These companies require a top quality derived knowledge storage system, with robust reliability, availability, scalability, and latency ensures for serving on-line site visitors. For instance, the consumer profiler service shops and accesses real-time and historic consumer actions on Airbnb to ship a extra customized expertise.

On this submit, we’ll speak about how we leveraged numerous open supply applied sciences, together with HRegion, Helix, Spark, Zookeeper,and Kafka to construct a scalable and low latency key-value retailer for a whole bunch of Airbnb product and platform use instances.

Over the previous few years, Airbnb has advanced and enhanced our help for serving derived knowledge, shifting from groups rolling out customized options to a multi-tenant storage platform referred to as Mussel. This evolution will be summarized into three phases:

Stage 1 (01/2015): Unified read-only key-value retailer (HFileService)

Earlier than 2015, there was no unified key-value retailer resolution inside Airbnb that met 4 key necessities:

  1. Scale to petabytes of information
  2. Environment friendly bulk load (batch technology and importing)
  3. Low latency reads (<50ms p99)
  4. Multi-tenant storage service that can be utilized by a number of prospects

Additionally, not one of the present options had been in a position to meet these necessities. MySQL doesn’t help bulk loading, Hbase’s large bulk loading (distcp) will not be optimum and dependable, RocksDB had no built-in horizontal sharding, and we didn’t have sufficient C++ experience to construct a bulk load pipeline to help RocksDB file format.

So we constructed HFileService, which internally used HFile (the constructing block of Hadoop HBase, which is predicated on Google’s SSTable):

Fig. 1: HFileService Structure
  1. Servers had been sharded and replicated to handle scalability and reliability points
  2. The variety of shards was mounted (equal to the variety of Hadoop reducers within the bulk load jobs) and the mapping of servers to shards saved in Zookeeper. We configured the variety of servers mapped to a particular shard by manually altering the mapping in Zookeeper
  3. A each day Hadoop job remodeled offline knowledge to HFile format and uploaded it to S3. Every server downloaded the information of their very own partitions to native disk and eliminated the outdated variations of information
  4. Totally different knowledge sources had been partitioned by main key. Purchasers decided the proper shard their requests ought to go to by calculating the hash of the first key and modulo with the full variety of shards. Then queried Zookeeper to get an inventory of servers that had these shards and despatched the request to one in every of them

Stage 2 (10/2015): Retailer each real-time and derived knowledge (Nebula)

Whereas we constructed a multi-tenant key-value retailer that supported environment friendly bulk load and low latency learn, it had its drawbacks. For instance, it didn’t help level, low-latency writes, and any replace to the saved knowledge needed to undergo the each day bulk load job. As Airbnb grew, there was an elevated must have low latency entry to real-time knowledge.

Due to this fact, Nebula was constructed to help each batch-update and real-time knowledge in a single system. It internally used DynamoDB to retailer real-time knowledge and S3/HFile to retailer batch-update knowledge. Nebula launched timestamp primarily based versioning as a model management mechanism. For learn requests, knowledge can be learn from each an inventory of dynamic tables and the static snapshot in HFileService, and the outcome merged primarily based on timestamp.

To reduce on-line merge operations, Nebula additionally had scheduled spark jobs that ran each day and merged snapshots of DynamoDB knowledge with the static snapshot of HFileService. Zookeeper was used to coordinate write availability of dynamic tables, snapshots being marked prepared for learn, and dropping of stale tables.

Fig. 2: Nebula Structure

Stage 3 (2018): Scalable and low latency key-value storage engine (Mussel)

In Stage 3, we constructed a system that supported each learn and write on real-time and batch-update knowledge with timestamp-based battle decision. Nonetheless, there have been alternatives for enchancment:

  1. Scale-out problem: It was cumbersome to manually edit partition mappings inside Zookeeper with growing knowledge progress, or to horizontally scale the system for growing site visitors by including further nodes
  2. Enhance learn efficiency underneath spiky write site visitors
  3. Excessive upkeep overhead: We wanted to take care of HFileService and DynamoDB on the identical time
  4. Inefficient merging course of: The method of merging the delta replace from DynamoDB and HFileService each day turned very gradual as our whole knowledge measurement turned bigger. The each day replace knowledge in DynamoDB was simply 1–2% of the baseline knowledge in HFileService. Nonetheless, we re-published the complete snapshot (102% of whole knowledge measurement) again to HFileService each day

To unravel the drawbacks, we got here up with a brand new key-value retailer system referred to as Mussel.

  1. We launched Helix to handle the partition mapping throughout the cluster
  2. We leveraged Kafka as a replication log to copy the write to all the replicas as an alternative of writing on to the Mussel retailer
  3. We used HRegion as the one storage engine within the Mussel storage nodes
  4. We constructed a Spark pipeline to load the information from the information warehouse into storage nodes straight

Let’s go into extra particulars within the following paragraphs.

Fig. 3: Mussel Structure

Handle partitions with Helix

In Mussel, so as to make our cluster extra scalable, we elevated the variety of shards from 8 in HFileService to 1024. In Mussel, knowledge is partitioned into these shards by the hash of the first keys, so we launched Apache Helix to handle these many logical shards. Helix manages the mapping of logical shards to bodily storage nodes routinely. Every Mussel storage node may maintain a number of logical shards. Every logical shard is replicated throughout a number of Mussel storage nodes.

Leaderless Replication with Kafka

Since Mussel is a read-heavy retailer, we adopted a leaderless structure. Learn requests might be served by any of the Mussel storage nodes which have the identical logical shard, which will increase learn scalability. Within the write path, we wanted to contemplate the next:

  1. We need to clean the write site visitors to keep away from the affect on the learn path
  2. Since we don’t have the chief node in every shard, we’d like a approach to verify every Mussel storage node applies the write requests in the identical order so the information is constant throughout totally different nodes

To unravel these issues, we launched Kafka as a write-ahead-log right here. For write requests, as an alternative of straight writing to the Mussel storage node, it’ll first write to Kafka asynchronously. We now have 1024 partitions for the Kafka matter, every partition belonging to 1 logical shard within the Mussel. Every Mussel storage node will ballot the occasions from Kafka and apply the change to its native retailer. Since there isn’t any leader-follower relationship between the shards, this configuration permits the proper write ordering inside a partition, guaranteeing constant updates. The downside right here is that it might probably solely present eventual consistency. Nonetheless, given the derived knowledge use case, it’s a suitable tradeoff to compromise on consistency within the curiosity of guaranteeing availability and partition tolerance.

Supporting each learn, write, and compaction in a single storage engine

As a way to cut back the {hardware} value and operational load of managing DynamoDB, we determined to take away it and prolong HFileService as the one storage engine to serve each real-time and offline knowledge. To raised help each learn and write operations, we used HRegion as an alternative of Hfile. HRegion is a completely useful key-value retailer with MemStore and BlockCache. Internally it makes use of a Log Structured Merged (LSM) Tree to retailer the information and helps each learn and write operations.

An HRegion desk comprises column households, that are the logical and bodily grouping of columns. There are column qualifiers inside a column household, that are the columns. Column households comprise columns with time stamped variations. Columns solely exist when they’re inserted, which makes HRegion a sparse database. We mapped our shopper knowledge to HRegion as the next:

With this mapping, for learn queries, we’re in a position to help:

  1. Level question by wanting up the information with main key
  2. Prefix/vary question by scanning knowledge on secondary key
  3. Queries for the most recent knowledge or knowledge inside a particular time vary, as each real-time and offline knowledge written to Mussel may have a timestamp

As a result of we’ve got over 4000 shopper tables in Mussel, every consumer desk is mapped to a column household in HRegion as an alternative of its personal desk to scale back scalability challenges on the metadata administration layer. Additionally, as HRegion is a column-based storage engine, every column household is saved in a separate file to allow them to be learn/written independently.

For write requests, it consumes the write request from Kafka and calls the HRegion put API to jot down the information straight. For every desk, it might probably additionally help customizing the max model and TTL (time-to-live).

Once we serve write requests with HRegion, one other factor to contemplate is compaction. Compaction must be run so as to clear up knowledge that’s deleted or has reached max model or max TTL. Additionally when the MemStore in HRegion reaches a sure measurement, it’s flushed to disk right into a StoreFile. Compaction will merge these recordsdata collectively so as to cut back disk search and enhance learn efficiency. Nonetheless, alternatively, when compaction is working, it causes greater cpu and reminiscence utilization and blocks writes to stop JVM (Java Digital Machine) heap exhaustion, which impacts the learn and write efficiency of the cluster.

Right here we use Helix to mark Mussel storage nodes for every logical shard into two varieties of sources: on-line nodes and batch nodes. For instance, if we’ve got 9 Mussel storage nodes for one logical shard, 6 of them are on-line nodes and three of them are batch nodes. The connection between on-line and batch are:

  1. They each serve write requests
  2. Solely on-line nodes serve learn requests and we price restrict the compaction on on-line nodes to have good learn efficiency
  3. Helix schedules a each day rotation between on-line nodes and batch nodes. Within the instance above, it strikes 3 on-line nodes to batch and three batch nodes to on-line so these 3 new batch nodes can carry out full velocity main compaction to wash up outdated knowledge

With this modification, now we’re in a position to help each learn and write with a single storage engine.

Supporting bulk load from knowledge warehouse

We help two varieties of bulk load pipelines from knowledge warehouse to Mussel by way of Airflow jobs: merge kind and change kind. Merge kind means merging the information from the information warehouse and the information from earlier write with older timestamps in Mussel. Exchange means importing the information from the information warehouse and deleting all the information with earlier timestamps.

We make the most of Spark to remodel knowledge from the information warehouse into HFile format and add to S3. Every Mussel storage node downloads the recordsdata and makes use of HRegion bulkLoadHFiles API to load these HFiles into the column household.

With this bulk load pipeline, we are able to simply load the delta knowledge into the cluster as an alternative of the complete knowledge snapshot day by day. Earlier than the migration, the consumer profile service wanted to load about 4TB knowledge into the cluster each day. After, it solely must load about 40–80GB, drastically lowering the fee and bettering the efficiency of the cluster.

In the previous couple of years, Airbnb has come a great distance in offering a high-quality derived knowledge retailer for our engineers. The newest key-value retailer Mussel is extensively used inside Airbnb and has change into a foundational constructing block for any key-value primarily based utility with robust reliability, availability, scalability, and efficiency ensures. Since its introduction, there have been ~4000 tables created in Mussel, storing ~130TB knowledge in our manufacturing clusters with out replication. Mussel has been working reliably to serve massive quantities of learn, write, and bulk load requests: For instance, mussel-general, our largest cluster, has achieved >99.9% availability, common learn QPS > 800k and write QPS > 35k, with common P95 learn latency lower than 8ms.

Though Mussel can serve our present use instances nicely, there are nonetheless many alternatives to enhance. For instance, we’re wanting ahead to offering the read-after-write consistency to our prospects. We additionally need to allow auto-scale and repartition primarily based on the site visitors within the cluster. We’re wanting ahead to sharing extra particulars about this quickly.

Mussel is a collaborative effort of Airbnb’s storage staff together with: Calvin Zou, Dionitas Santos, Ruan Maia, Wonhee Cho, Xiaomou Wang, Yanhan Zhang.

Interested by engaged on the Airbnb Storage staff? Take a look at this position: Staff Software Engineer, Distributed Storage