Repository: incubator-distributedlog
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[BLOG] A technical review of kafka and distributedlog

Author: Sijie Guo <>

Reviewers: Leigh Stewart <>

Closes #35 from sijie/sijie/kafka_dl_blog_post and squashes the following 

3d6bfd2 [Sijie Guo] Fix the format
a78175f [Sijie Guo] Add all references
74d538e [Sijie Guo] Use the old url as the permantlink
b760cb4 [Sijie Guo] Fix the broken link and the date
c170fdc [Sijie Guo] Change the table to html and set 1px border
4452709 [Sijie Guo] Add a blog post about "kafka vs distributedlog"


Branch: refs/heads/master
Commit: 463f4972700d6389085544b19745e27fdaafd6d5
Parents: ae69fff
Author: Sijie Guo <>
Authored: Thu Oct 13 00:38:25 2016 -0700
Committer: Sijie Guo <>
Committed: Thu Oct 13 00:38:25 2016 -0700

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+layout: post
+title:  'A Technical Review of Kafka and DistributedLog'
+date:   2016-09-19 10:00:00
+categories: technical-review
+permalink: /technical-review/2015/09/19/kafka-vs-distributedlog
+- sijie
+We open sourced [DistributedLog]( [^distributedlog] 
in May 2016.
+It generated a lot of interest in the community. One frequent question we are 
asked is how does DistributedLog
+compare to [Apache Kafka]( [^kafka]. Technically 
DistributedLog is not a full fledged partitioned
+pub/sub system like Apache Kafka. DistributedLog is a replicated log stream 
store, using [Apache BookKeeper]( [^bookkeeper] 
as its log segment store.
+It focuses on offering *durability*, *replication* and *strong consistency* as 
essentials for building reliable
+real-time systems. One can use DistributedLog to build and experiment with 
different messaging models
+(such as Queue, Pub/Sub). 
+Since both of them share very similar data model around `log`, this blog post 
will discuss the difference
+between Apache Kafka and DistributedLog from a technical perspective. We will 
try our best to be objective.
+However, since we are not experts in Apache Kafka, we may have made wrong 
assumptions about Apache Kafka.
+Please do correct us if that is the case.
+First, let's have a quick overview of Kafka and DistributedLog.
+## What is Kafka?
+Kafka is a distributed messaging system originally built at Linkedin and now 
part of [Apache Software Foundation](
+It is a partition based pub/sub system. The key abstraction in Kafka is a 
+A topic is partitioned into multiple partitions. Each partition is stored and 
replicated in multiple brokers.
+Producers publish their records to partitions of a topic (round-robin or 
partitioned by keys), and consumers
+consume the published records of that topic. Data is published to a leader 
broker for a partition and
+replicated to follower brokers; all read requests are subsequently served by 
the leader broker.
+The follower brokers are used for redundancy and backup in case the leader can 
no longer serve requests.
+The left diagram in Figure 1 shows the data flow in Kafka.
+## What is DistributedLog?
+Unlike Kafka, DistributedLog is not a partitioned pub/sub system. It is a 
replicated log stream store.
+The key abstraction in DistributedLog is a continuous replicated log stream. A 
log stream is segmented
+into multiple log segments. Each log segment is stored as
+a [ledger]( 
[^ledger] in Apache BookKeeper,
+whose data is replicated and distributed evenly across multiple bookies (a 
bookie is a storage node in Apache BookKeeper).
+All the records of a log stream are sequenced by the owner of the log stream - 
a set of write proxies that
+manage the ownership of log streams [^corelibrary]. Each of the log records 
appended to a log stream will
+be assigned a sequence number. The readers can start reading the log stream 
from any provided sequence number.
+The read requests will be load balanced across the storage replicas of that 
+The right diagram in Figure 1 shows the data flow in DistributedLog.
+<img class="center-block" src="{{ 
"/images/blog/kafka-distributedlog-dataflow.png" | prepend: site.baseurl }}" 
alt="Kafka vs DistributedLog" width="500">
+<div class="text-center">
+<i>Figure 1 - Apache Kafka vs Apache DistributedLog</i>
+## How do Kafka and DistributedLog differ?
+For an apples-to-apples comparison, we will only compare Kafka partitions with 
DistributedLog streams in this blog post. The table below lists the most 
important differences between the two systems.
+<table border="1px">
+  <thead>
+    <tr>
+      <th>&nbsp;</th>
+      <th><strong>Kafka</strong></th>
+      <th><strong>DistributedLog</strong></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>Data Segmentation/Distribution</strong></td>
+      <td>The data of a Kafka partition lives in a set of brokers; The data is 
segmented into log segment files locally on each broker.</td>
+      <td>The data of a DL stream is segmented into multiple log segments. The 
log segments are evenly distributed across the storage nodes.</td>
+    </tr>
+  </tbody>
+  <tbody>
+    <tr>
+      <td><strong>Data Retention</strong></td>
+      <td>The data is expired after configured retention time or compacted to 
keep the latest values for keys.</td>
+      <td>The data can be either expired after configured retention time or 
explicitly truncated to given positions.</td>
+    </tr>
+  </tbody>
+  <tbody>
+    <tr>
+      <td><strong>Write</strong></td>
+      <td>Multiple-Writers via Broker</td>
+      <td>Multiple-Writers via Write Proxy; Single-Writer via Core Library</td>
+    </tr>
+  </tbody>
+  <tbody>
+    <tr>
+      <td><strong>Read</strong></td>
+      <td>Read from leader broker</td>
+      <td>Read from any storage node which has the data</td>
+    </tr>
+  </tbody>
+  <tbody>
+    <tr>
+      <td><strong>Replication</strong></td>
+      <td>ISR (In-Sync-Replica) Replication - Both leader and followers are 
+      <td>Quorum vote replication - Leaders are write proxies; Followers are 
+    </tr>
+  </tbody>
+  <tbody>
+    <tr>
+      <td><strong>Replication Repair</strong></td>
+      <td>Done by adding new replica to copy data from the leader broker.</td>
+      <td>The replication repair is done by bookie AutoRecovery mechanism to 
ensure replication factor.</td>
+    </tr>
+  </tbody>
+  <tbody>
+    <tr>
+      <td><strong>Cluster Expand</strong></td>
+      <td>Need to re-distribute (balance) partition when adding new brokers. 
Carefully track the size of each partition and make sure rebalancing doesn’t 
saturate network and I/O.</td>
+      <td>No data redistribution when either adding write proxies or bookies; 
New log segment will be automatically allocated on newly added bookies.</td>
+    </tr>
+  </tbody>
+  <tbody>
+    <tr>
+      <td><strong>Storage</strong></td>
+      <td>File (set of files) per partition</td>
+      <td>Interleaved Storage Format</td>
+    </tr>
+  </tbody>
+  <tbody>
+    <tr>
+      <td><strong>Durability</strong></td>
+      <td>Only write to filesystem page cache. Configurable to either wait for 
acknowledgement from leader or from all replicas in ISR.</td>
+      <td>All writes are persisted to disk via explicit fsync. Wait for 
acknowledgements from a configurable quorum of bookies.</td>
+    </tr>
+  </tbody>
+  <tbody>
+    <tr>
+      <td><strong>I/O Isolation</strong></td>
+      <td>No physical I/O isolation. Rely on filesystem page cache.</td>
+      <td>Physical I/O isolation. Separate journal disk for write and ledger 
disks for reads.</td>
+    </tr>
+  </tbody>
+### Data Model
+A Kafka partition is a log stored as a (set of) file(s) in the broker's disks.
+Each record is a key/value pair (key can be omitted for round-robin 
+The key is used for assigning the record to a Kafka partition and also for 
+All the data of a partition is stored only on a set of brokers, replicated 
from leader broker to follower brokers.
+A DistributedLog stream is a `virtual` stream stored as a list of log segments.
+Each log segment is stored as a BookKeeper ledger and replicated to multiple 
+There will be only one active log segment accepting writes at any time.
+An old log segment is sealed and a new log segment will be opened when a given 
time period elapses,
+an old log segment reaches a configured size threshold (based on configured 
log segment rolling policies),
+or when the owner of the log stream fails. 
+The differences in data segmentation and distribution of Kafka partition and 
DistributedLog stream result in differences in data retention policies and 
cluster operations (e.g. cluster expansion).
+Figure 2 shows the differences between DistributedLog and Kafka data model.
+<img class="center-block" src="{{ 
"/images/blog/kafka-distributedlog-segmentation.png" | prepend: site.baseurl 
}}" alt="Kafka Partition and DistributedLog Stream" width="500">
+<div class="text-center">
+<i>Figure 2 - Kafka Partition and DistributedLog Stream</i>
+#### Data Retention
+All the data of a Kafka partition is stored on one broker (replicated to other 
brokers). Data is expired and deleted after a configured retention period. 
Additionally, a Kafka partition can be configured to do log compaction to keep 
only the latest values for keys.
+Similar to Kafka, DistributedLog also allows configuring retention periods for 
individual streams and expiring / deleting log segments after they are expired. 
Besides that, DistributedLog also provides an explicit-truncation mechanism. 
Application can explicitly truncate a log stream to a given position in the 
stream. This is important for building replicated state machines as the 
replicated state machines require persisting state before deleting log records.
[^consistency] is one example of a system that uses this functionality.
+#### Operations
+The differences in data segmentation/distribution also result in different 
experiences in operating the cluster, one example is when expanding the cluster.
+When expanding a Kafka cluster, the partitions typically need to be 
rebalanced. The rebalancing moves Kafka partitions around to different replicas 
to try to achieve an even distribution. This involves copying the entire stream 
data from one replica to another. It has been mentioned in several talks that 
rebalancing needs to be executed carefully to avoid saturating disk and network 
+Expanding a DistributedLog cluster works in a very different way. 
DistributedLog consists of two layers: a storage layer - Apache BookKeeper, and 
a serving layer - the write and read proxies. When expanding the storage layer, 
we typically just add more bookies. The new bookies will be discovered by the 
writers of the log streams and will immediately be available for writing of new 
log segments. There is no rebalancing involved when expanding the data storage 
layer. Rebalancing only comes into play when adding capacity to the serving 
layer, however this rebalancing only moves the ownership of the log streams to 
allow network bandwidth to be evenly used across proxies, there is no data 
copying involved, only accounting of ownership. This separation of storage and 
serving layers not only makes it easy to integrate the system with any 
auto-scaling mechanism, but also allows scaling resources independently.
+### Writer & Producer
+As shown in Figure 1, Kafka producers write batches of records to the leader 
broker of a Kafka partition. The follower brokers in the [ISR (in-sync-replica) 
[^kafkareplication] will replicate the records from the leader broker. A record 
is considered as committed only when the leader receives acknowledgments from 
all the replicas in the ISR. The producer can be configured to wait for the 
response from leader broker or from all brokers in the ISR.
+There are two ways in DistributedLog to write log records to a DistributedLog 
stream, one is using a thin thrift client to write records through the write 
proxies (aka multiple-writer semantic), while the other one is using the 
DistributedLog core library to talk directly to the storage nodes (aka 
single-writer semantics). The first approach is common for building messaging 
systems while the second approach is common for building replicated state 
machines. You can check the [Best 
 section in DistributedLog documentation for more details about what should be 
+The owner of a log stream writes a batch of records as BookKeeper entries in 
parallel to the bookies and waits for responses from a quorum of bookies. The 
size of a quorum is known as the `ack_quorum_size` of a BookKeeper ledger and 
it can be configured per DistributedLog stream. It provides similar flexibility 
on durability as Kafka producers. We will discuss more about the differences on 
replication algorithms later in 'Replication' section.
+Both Kafka and DistributedLog support end-to-end batching and compression. One 
slight difference between Kafka and DistributedLog is all the writes in 
DistributedLog are flushed (via fsync) to disk before acknowledging (We are not 
aware of Kafka providing a similar guarantee).
+### Reader & Consumer
+Kafka consumers read the records from the leader broker. The assumption of 
this design is that the leader has all the latest data in filesystem page cache 
most of the time. This is a good choice to fully utilize the filesystem page 
cache and achieve high performance.
+DistributedLog takes a different approach. As there is no distinguished 
`leadership` on storage nodes, DistributedLog reads the records from any of the 
storage nodes that store the data. In order to achieve predictable low latency, 
DistributedLog deploys a speculative read mechanism to retry reading from 
different replicas after a configured speculative read timeout. This will 
result in more read traffic to storage nodes than Kafka's approach. However 
configuring the speculative read timeout to be around the 99th percentile of 
read latency can help mask the latency variabilities on the storage node with 
an additional 1% traffic overhead.
+The differences in considerations and mechanisms on reads are mostly due to 
the differences in replication mechanism and the I/O system on storage nodes. 
They will be discussed below.
+### Replication
+Kafka uses an ISR replication algorithm - a broker is elected as the leader. 
All the writes are published to the leader broker and all the followers in a 
ISR set will read and replicate data from the leader. The leader maintains a 
high watermark (HW), which is the offset of last committed record for a 
partition. The high watermark is continuously propagated to the followers and 
is checkpointed to disk in each broker periodically for recovery. The HW is 
updated when all replicas in ISR successfully write the records to the 
filesystem (not necessarily to disk) and acknowledge back to the leader. 
+ISR mechanism allows adding and dropping replicas to achieve tradeoff between 
availability and performance. However the side effect of allowing adding and 
shrinking replica set is increased probability of [data 
+DistributedLog uses a quorum-vote replication algorithm, which is typically 
seen in consensus algorithms like Zab, Raft and Viewstamped Replication. The 
owner of the log stream writes the records to all the storage nodes in parallel 
and waits until a configured quorum of storage nodes have acknowledged before 
they are considered to be committed. The storage nodes acknowledge the write 
requests only after the data has been persisted to disk by explicitly calling 
flush. The owner of the log stream also maintains the offset of last committed 
record for a log stream, which is known as LAC (LastAddConfirmed) in Apache 
BookKeeper. The LAC is piggybacked into entries (to save extra rpc calls) and 
continuously propagated to the storage nodes. The size of replica set in 
DistributedLog is configured and fixed per log segment per stream. The change 
of replication settings only affect the newly allocated log segments but not 
the old log segments.
+### Storage
+Each Kafka partition is stored as a (set of) file(s) on the broker's disks. It 
achieves good performance by leveraging the filesystem page cache and I/O 
scheduling. It is also the way Kafka leverages Java's sendfile api to get data 
in and out of brokers efficiently. However, it can potentially be exposed to 
unpredictabilities when page cache evictions become frequent due to various 
reasons (e.g. consumer falls behind, random writes/reads and such).
+DistributedLog uses a different I/O model. Figure 3 illustrates the I/O system 
on the bookies (BookKeeper's storage nodes). The three common I/O workloads - 
writes (blue lines), tailing-reads (red lines) and catchup-reads (purple lines) 
are separated into 3 physically different I/O subsystems. All the writes are 
sequentially appended to journal files on the journal disks and group committed 
to the disk. After the writes are durably persisted on the disks, they are put 
into a memtable and acknowledged back to the clients. The data in the memtable 
is asynchronously flushed into an interleaved indexed data structure: the 
entries are appended into entry log files and the offsets are indexed by entry 
ids in the ledger index files. The latest data is guaranteed to be in memtable 
to serve tailing reads. Catch-up reads read from the entry log files. With 
physical isolation, a bookie node can fully utilize the network ingress 
bandwidth and sequential write bandwidth on the journal disk for w
 rites and utilize the network egress bandwidth and IOPS on multiple ledger 
disks for reads, without interfering with each other.
+<img class="center-block" src="{{ 
"/images/blog/kafka-distributedlog-bookkeeper-io.png" | prepend: site.baseurl 
}}" alt="BookKeeper I/O Isolation" width="500">
+<div class="text-center">
+<i>Figure 3 - BookKeeper I/O Isolation</i>
+## Summary
+Both Kafka and DistributedLog are designed for log stream related workloads. 
They share some similarities but different design principles for storage and 
replication lead to different implementations. Hopefully this blog post 
clarifies the differences and address some of the questions. We are also 
working on  followup blog posts to explain the performance characteristics of 
+## References
+[^distributedlog]: DistributedLog Website:
+[^kafka]: Apache Kafka Website:
+[^bookkeeper]: Apache BookKeeper Website:
+[^ledger]: BookKeeper Ledger:
+[^logcompaction]: Kafka Log Compaction:
+[^consistency]: Strong consistency in Manhattan:
+[^kafkareplication]: Kafka Replication:
+[^jepsen]: Jepsen: Kafka:
+[^corelibrary]: Applications can also use the core library directly to append 
log records. This is very useful for use cases like replicated state machines 
that require ordering and exclusive write semantics.
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