# Druid 系统架构


Druid has a multi-process, distributed architecture that is designed to be cloud-friendly and easy to operate. Each
Druid process type can be configured and scaled independently, giving you maximum flexibility over your cluster. This
design also provides enhanced fault tolerance: an outage of one component will not immediately affect other components.

## Processes and Servers

Druid has several process types, briefly described below:

* [**Coordinator**](../design/coordinator.md) processes manage data availability on the cluster.
* [**Overlord**](../design/overlord.md) processes control the assignment of data ingestion workloads.
* [**Broker**](../design/broker.md) processes handle queries from external clients.
* [**Router**](../design/router.md) processes are optional processes that can route requests to Brokers, Coordinators, and Overlords.
* [**Historical**](../design/historical.md) processes store queryable data.
* [**MiddleManager**](../design/middlemanager.md) processes are responsible for ingesting data.

Druid processes can be deployed any way you like, but for ease of deployment we suggest organizing them into three server types: Master, Query, and Data.

* **Master**: Runs Coordinator and Overlord processes, manages data availability and ingestion.
* **Query**: Runs Broker and optional Router processes, handles queries from external clients.
* **Data**: Runs Historical and MiddleManager processes, executes ingestion workloads and stores all queryable data.

For more details on process and server organization, please see [Druid Processes and Servers](../design/processes.md).

## External dependencies

In addition to its built-in process types, Druid also has three external dependencies. These are intended to be able to
leverage existing infrastructure, where present.

### Deep storage
Shared file storage accessible by every Druid server. In a clustered deployment, this is typically going to
be a distributed object store like S3 or HDFS, or a network mounted filesystem. In a single-server deployment,
this is typically going to be local disk. Druid uses deep storage to store any data that has been ingested into the
system.

Druid uses deep storage only as a backup of your data and as a way to transfer data in the background between
Druid processes. To respond to queries, Historical processes do not read from deep storage, but instead read prefetched
segments from their local disks before any queries are served. This means that Druid never needs to access deep storage
during a query, helping it offer the best query latencies possible. It also means that you must have enough disk space
both in deep storage and across your Historical processes for the data you plan to load.

Deep storage is an important part of Druid's elastic, fault-tolerant design. Druid can bootstrap from deep storage even
if every single data server is lost and re-provisioned.

For more details, please see the [Deep storage](../dependencies/deep-storage.md) page.

### Metadata storage
The metadata storage holds various shared system metadata such as segment usage information and task information. In a
clustered deployment, this is typically going to be a traditional RDBMS like PostgreSQL or MySQL. In a single-server
deployment, it is typically going to be a locally-stored Apache Derby database.

For more details, please see the [Metadata storage](../dependencies/metadata-storage.md) page.

### ZooKeeper
Used for internal service discovery, coordination, and leader election.

For more details, please see the [ZooKeeper](../dependencies/zookeeper.md) page.

## Architecture diagram

The following diagram shows how queries and data flow through this architecture, using the suggested Master/Query/Data server organization:

<img src="../assets/druid-architecture.png" width="800"/>



## Storage design

### Datasources and segments

Druid data is stored in "datasources", which are similar to tables in a traditional RDBMS. Each datasource is
partitioned by time and, optionally, further partitioned by other attributes. Each time range is called a "chunk" (for
example, a single day, if your datasource is partitioned by day). Within a chunk, data is partitioned into one or more
["segments"](../design/segments.md). Each segment is a single file, typically comprising up to a few million rows of data. Since segments are
organized into time chunks, it's sometimes helpful to think of segments as living on a timeline like the following:

<img src="../assets/druid-timeline.png" width="800" />

A datasource may have anywhere from just a few segments, up to hundreds of thousands and even millions of segments. Each
segment starts life off being created on a MiddleManager, and at that point, is mutable and uncommitted. The segment
building process includes the following steps, designed to produce a data file that is compact and supports fast
queries:

- Conversion to columnar format
- Indexing with bitmap indexes
- Compression using various algorithms
    - Dictionary encoding with id storage minimization for String columns
    - Bitmap compression for bitmap indexes
    - Type-aware compression for all columns

Periodically, segments are committed and published. At this point, they are written to [deep storage](#deep-storage),
become immutable, and move from MiddleManagers to the Historical processes. An entry about the segment is also written
to the [metadata store](#metadata-storage). This entry is a self-describing bit of metadata about the segment, including
things like the schema of the segment, its size, and its location on deep storage. These entries are what the
Coordinator uses to know what data *should* be available on the cluster.

For details on the segment file format, please see [segment files](segments.md).

For details on modeling your data in Druid, see [schema design](../ingestion/schema-design.md).

### Indexing and handoff

_Indexing_ is the mechanism by which new segments are created, and _handoff_ is the mechanism by which they are published
and begin being served by Historical processes. The mechanism works like this on the indexing side:

1. An _indexing task_ starts running and building a new segment. It must determine the identifier of the segment before
   it starts building it. For a task that is appending (like a Kafka task, or an index task in append mode) this will be
   done by calling an "allocate" API on the Overlord to potentially add a new partition to an existing set of segments. For
   a task that is overwriting (like a Hadoop task, or an index task _not_ in append mode) this is done by locking an
   interval and creating a new version number and new set of segments.
2. If the indexing task is a realtime task (like a Kafka task) then the segment is immediately queryable at this point.
   It's available, but unpublished.
3. When the indexing task has finished reading data for the segment, it pushes it to deep storage and then publishes it
   by writing a record into the metadata store.
4. If the indexing task is a realtime task, at this point it waits for a Historical process to load the segment. If the
   indexing task is not a realtime task, it exits immediately.

And like this on the Coordinator / Historical side:

1. The Coordinator polls the metadata store periodically (by default, every 1 minute) for newly published segments.
2. When the Coordinator finds a segment that is published and used, but unavailable, it chooses a Historical process
   to load that segment and instructs that Historical to do so.
3. The Historical loads the segment and begins serving it.
4. At this point, if the indexing task was waiting for handoff, it will exit.

### Segment identifiers

Segments all have a four-part identifier with the following components:

- Datasource name.
- Time interval (for the time chunk containing the segment; this corresponds to the `segmentGranularity` specified
  at ingestion time).
- Version number (generally an ISO8601 timestamp corresponding to when the segment set was first started).
- Partition number (an integer, unique within a datasource+interval+version; may not necessarily be contiguous).

For example, this is the identifier for a segment in datasource `clarity-cloud0`, time chunk
`2018-05-21T16:00:00.000Z/2018-05-21T17:00:00.000Z`, version `2018-05-21T15:56:09.909Z`, and partition number 1:

```
clarity-cloud0_2018-05-21T16:00:00.000Z_2018-05-21T17:00:00.000Z_2018-05-21T15:56:09.909Z_1
```

Segments with partition number 0 (the first partition in a chunk) omit the partition number, like the following
example, which is a segment in the same time chunk as the previous one, but with partition number 0 instead of 1:

```
clarity-cloud0_2018-05-21T16:00:00.000Z_2018-05-21T17:00:00.000Z_2018-05-21T15:56:09.909Z
```

### Segment versioning

You may be wondering what the "version number" described in the previous section is for. Or, you might not be, in which
case good for you and you can skip this section!

It's there to support batch-mode overwriting. In Druid, if all you ever do is append data, then there will be just a
single version for each time chunk. But when you overwrite data, what happens behind the scenes is that a new set of
segments is created with the same datasource, same time interval, but a higher version number. This is a signal to the
rest of the Druid system that the older version should be removed from the cluster, and the new version should replace
it.

The switch appears to happen instantaneously to a user, because Druid handles this by first loading the new data (but
not allowing it to be queried), and then, as soon as the new data is all loaded, switching all new queries to use those
new segments. Then it drops the old segments a few minutes later.

### Segment lifecycle

Each segment has a lifecycle that involves the following three major areas:

1. **Metadata store:** Segment metadata (a small JSON payload generally no more than a few KB) is stored in the
   [metadata store](../dependencies/metadata-storage.md) once a segment is done being constructed. The act of inserting
   a record for a segment into the metadata store is called _publishing_. These metadata records have a boolean flag
   named `used`, which controls whether the segment is intended to be queryable or not. Segments created by realtime tasks will be
   available before they are published, since they are only published when the segment is complete and will not accept
   any additional rows of data.
2. **Deep storage:** Segment data files are pushed to deep storage once a segment is done being constructed. This
   happens immediately before publishing metadata to the metadata store.
3. **Availability for querying:** Segments are available for querying on some Druid data server, like a realtime task
   or a Historical process.

You can inspect the state of currently active segments using the Druid SQL
[`sys.segments` table](../querying/sql.md#segments-table). It includes the following flags:

- `is_published`: True if segment metadata has been published to the metadata store and `used` is true.
- `is_available`: True if the segment is currently available for querying, either on a realtime task or Historical
  process.
- `is_realtime`: True if the segment is _only_ available on realtime tasks. For datasources that use realtime ingestion,
  this will generally start off `true` and then become `false` as the segment is published and handed off.
- `is_overshadowed`: True if the segment is published (with `used` set to true) and is fully overshadowed by some other
  published segments. Generally this is a transient state, and segments in this state will soon have their `used` flag
  automatically set to false.

### Availability and consistency

Druid has an architectural separation between ingestion and querying, as described above in
[Indexing and handoff](#indexing-and-handoff). This means that when understanding Druid's availability and
consistency properties, we must look at each function separately.

On the **ingestion side**, Druid's primary [ingestion methods](../ingestion/index.md#ingestion-methods) are all
pull-based and offer transactional guarantees. This means that you are guaranteed that ingestion using these will
publish in an all-or-nothing manner:

- Supervised "seekable-stream" ingestion methods like [Kafka](../development/extensions-core/kafka-ingestion.md) and
  [Kinesis](../development/extensions-core/kinesis-ingestion.md). With these methods, Druid commits stream offsets to its
  [metadata store](#metadata-storage) alongside segment metadata, in the same transaction. Note that ingestion of data
  that has not yet been published can be rolled back if ingestion tasks fail. In this case, partially-ingested data is
  discarded, and Druid will resume ingestion from the last committed set of stream offsets. This ensures exactly-once
  publishing behavior.
- [Hadoop-based batch ingestion](../ingestion/hadoop.md). Each task publishes all segment metadata in a single
  transaction.
- [Native batch ingestion](../ingestion/native-batch.md). In parallel mode, the supervisor task publishes all segment
  metadata in a single transaction after the subtasks are finished. In simple (single-task) mode, the single task
  publishes all segment metadata in a single transaction after it is complete.

Additionally, some ingestion methods offer an _idempotency_ guarantee. This means that repeated executions of the same
ingestion will not cause duplicate data to be ingested:

- Supervised "seekable-stream" ingestion methods like [Kafka](../development/extensions-core/kafka-ingestion.md) and
  [Kinesis](../development/extensions-core/kinesis-ingestion.md) are idempotent due to the fact that stream offsets and
  segment metadata are stored together and updated in lock-step.
- [Hadoop-based batch ingestion](../ingestion/hadoop.md) is idempotent unless one of your input sources
  is the same Druid datasource that you are ingesting into. In this case, running the same task twice is non-idempotent,
  because you are adding to existing data instead of overwriting it.
- [Native batch ingestion](../ingestion/native-batch.md) is idempotent unless
  [`appendToExisting`](../ingestion/native-batch.md) is true, or one of your input sources is the same Druid datasource
  that you are ingesting into. In either of these two cases, running the same task twice is non-idempotent, because you
  are adding to existing data instead of overwriting it.

On the **query side**, the Druid Broker is responsible for ensuring that a consistent set of segments is involved in a
given query. It selects the appropriate set of segments to use when the query starts based on what is currently
available. This is supported by _atomic replacement_, a feature that ensures that from a user's perspective, queries
flip instantaneously from an older set of data to a newer set of data, with no consistency or performance impact.
This is used for Hadoop-based batch ingestion, native batch ingestion when `appendToExisting` is false, and compaction.

Note that atomic replacement happens for each time chunk individually. If a batch ingestion task or compaction
involves multiple time chunks, then each time chunk will undergo atomic replacement soon after the task finishes, but
the replacements will not all happen simultaneously.

Typically, atomic replacement in Druid is based on a _core set_ concept that works in conjunction with segment versions.
When a time chunk is overwritten, a new core set of segments is created with a higher version number. The core set
must _all_ be available before the Broker will use them instead of the older set. There can also only be one core set
per version per time chunk. Druid will also only use a single version at a time per time chunk. Together, these
properties provide Druid's atomic replacement guarantees.

Druid also supports an experimental _segment locking_ mode that is activated by setting
[`forceTimeChunkLock`](../ingestion/tasks.md#context) to false in the context of an ingestion task. In this case, Druid
creates an _atomic update group_ using the existing version for the time chunk, instead of creating a new core set
with a new version number. There can be multiple atomic update groups with the same version number per time chunk. Each
one replaces a specific set of earlier segments in the same time chunk and with the same version number. Druid will
query the latest one that is fully available. This is a more powerful version of the core set concept, because it
enables atomically replacing a subset of data for a time chunk, as well as doing atomic replacement and appending
simultaneously.

If segments become unavailable due to multiple Historicals going offline simultaneously (beyond your replication
factor), then Druid queries will include only the segments that are still available. In the background, Druid will
reload these unavailable segments on other Historicals as quickly as possible, at which point they will be included in
queries again.

## Query processing

Queries first enter the [Broker](../design/broker.md), where the Broker will identify which segments have data that may pertain to that query.
The list of segments is always pruned by time, and may also be pruned by other attributes depending on how your
datasource is partitioned. The Broker will then identify which [Historicals](../design/historical.md) and
[MiddleManagers](../design/middlemanager.md) are serving those segments and send a rewritten subquery to each of those processes. The Historical/MiddleManager processes will take in the
queries, process them and return results. The Broker receives results and merges them together to get the final answer,
which it returns to the original caller.

Broker pruning is an important way that Druid limits the amount of data that must be scanned for each query, but it is
not the only way. For filters at a more granular level than what the Broker can use for pruning, indexing structures
inside each segment allow Druid to figure out which (if any) rows match the filter set before looking at any row of
data. Once Druid knows which rows match a particular query, it only accesses the specific columns it needs for that
query. Within those columns, Druid can skip from row to row, avoiding reading data that doesn't match the query filter.

So Druid uses three different techniques to maximize query performance:

- Pruning which segments are accessed for each query.
- Within each segment, using indexes to identify which rows must be accessed.
- Within each segment, only reading the specific rows and columns that are relevant to a particular query.

For more details about how Druid executes queries, refer to the [Query execution](../querying/query-execution.md)
documentation.