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Here you will find a collection of ready-made sample applications and examples for real-world data
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The Pinot Controller is responsible for the following:
Maintaining global metadata (e.g. configs and schemas) of the system with the help of Zookeeper which is used as the persistent metadata store.
Hosting the Helix Controller and managing other Pinot components (brokers, servers, minions)
Maintaining the mapping of which servers are responsible for which segments. This mapping is used by the servers to download the portion of the segments that they are responsible for. This mapping is also used by the broker to decide which servers to route the queries to.
Serving admin endpoints for viewing, creating, updating, and deleting configs, which are used to manage and operate the cluster.
Serving endpoints for segment uploads, which are used in offline data pushes. They are responsible for initializing real-time consumption and coordination of persisting real-time segments into the segment store periodically.
Undertaking other management activities such as managing retention of segments, validations.
For redundancy, there can be multiple instances of Pinot controllers. Pinot expects that all controllers are configured with the same back-end storage system so that they have a common view of the segments (e.g. NFS). Pinot can use other storage systems such as HDFS or ADLS.
The Controller runs several periodic tasks in the background, to perform activities such as management and validation. Each periodic task has its own configs to define the run frequency and default frequency. Each task runs at its own schedule or can also be triggered manually if needed. The task runs on the lead controller for each table.
Here's a list of all the periodic tasks
This task rebuilds the BrokerResource if the instance set has changed.
controller.broker.resource.validation.frequencyPeriod
1h
controller.broker.resource.validation.initialDelayInSeconds
between 2m-5m
TBD
This task manages the segment ValidationMetrics (missingSegmentCount, offlineSegmentDelayHours, lastPushTimeDelayHours, TotalDocumentCount, NonConsumingPartitionCount, SegmentCount), to ensure that all offline segments are contiguous (no missing segments) and that the offline push delay isn't too high.
controller.offline.segment.interval.checker.frequencyPeriod
24h
controller.statuschecker.waitForPushTimePeriod
10m
controller.offlineSegmentIntervalChecker.initialDelayInSeconds
between 2m-5m
TBD
This task validates the ideal state and segment zk metadata of realtime tables,
fixing any partitions which have stopped consuming
starting consumption from new partitions
uploading segments to deep store if segment download url is missing
This task ensures that the consumption of the realtime tables gets fixed and keeps going when met with erroneous conditions.
This task does not fix consumption stalled due to
CONSUMING segment being deleted
Kafka OOR exceptions
controller.realtime.segment.validation.frequencyPeriod
1h
controller.realtime.segment.validation.initialDelayInSeconds
between 2m-5m
This task manages retention of segments for all tables. During the run, it looks at the retentionTimeUnit
and retentionTimeValue
inside the segmentsConfig
of every table, and deletes segments which are older than the retention. The deleted segments are moved to a DeletedSegments folder colocated with the dataDir on segment store, and permanently deleted from that folder in a configurable number of days.
controller.retention.frequencyPeriod
6h
controller.retentionManager.initialDelayInSeconds
between 2m-5m
controller.deleted.segments.retentionInDays
7d
This task is applicable only if you have tierConfig or tagOverrideConfig. It runs rebalance in the background to
relocate COMPLETED segments to tag overrides
relocate ONLINE segments to tiers if tier configs are set
At most one replica is allowed to be unavailable during rebalance.
controller.segment.relocator.frequencyPeriod
1h
controller.segmentRelocator.initialDelayInSeconds
between 2m-5m
This task manages segment status metrics such as realtimeTableCount, offlineTableCount, disableTableCount, numberOfReplicas, percentOfReplicas, percentOfSegments, idealStateZnodeSize, idealStateZnodeByteSize, segmentCount, segmentsInErrorState, tableCompressedSize.
controller.statuschecker.frequencyPeriod
5m
controller.statusChecker.initialDelayInSeconds
between 2m-5m
TBD
Use the GET /periodictask/names
API to fetch the names of all the Periodic Tasks running on your Pinot cluster.
To manually run a named Periodic Task use the GET /periodictask/run
API
The Log Request Id
(api-09630c07
) can be used to search through pinot-controller log file to see log entries related to execution of the Periodic task that was manually run.
If tableName
(and its type OFFLINE
or REALTIME
) is not provided, the task will run against all tables.
Make sure you've setup Zookeeper. If you're using docker, make sure to pull the pinot docker image. To start a controller
Learn about the different components and logical abstractions
This section is a reference for the definition of major components and logical abstractions used in Pinot.
For a general overview that ties together all of the reference material in this section, see Basic Concepts.
Apache Pinot, a real-time distributed OLAP datastore, purpose-built for low-latency high throughput analytics, perfect for user-facing analytical workloads.
Join us in our Slack channel for questions, troubleshooting, and feedback. You can request an invite from - https://communityinviter.com/apps/apache-pinot/apache-pinot.
We'd love to hear from you!
Pinot is a real-time distributed OLAP datastore, purpose-built to provide ultra low-latency analytics, even at extremely high throughput. It can ingest directly from streaming data sources - such as Apache Kafka and Amazon Kinesis - and make the events available for querying instantly. It can also ingest from batch data sources such as Hadoop HDFS, Amazon S3, Azure ADLS, and Google Cloud Storage.
At the heart of the system is a columnar store, with several smart indexing and pre-aggregation techniques for low latency. This makes Pinot the most perfect fit for user-facing realtime analytics. At the same time, Pinot is also a great choice for other analytical use-cases, such as internal dashboards, anomaly detection, and ad-hoc data exploration.
Pinot was built by engineers at LinkedIn and Uber and is designed to scale up and out with no upper bound. Performance always remains constant based on the size of your cluster and an expected query per second (QPS) threshold.
User-facing analytics, or site-facing analytics, is the analytical tools and applications that you would expose directly to the end-users of your product. In a user-facing analytics application, think of the user-base as ALL end users of an App. This App could be a social networking app, or a food delivery app - anything at all. It’s not just a few analysts doing offline analysis, or a handful of data scientists in a company running ad-hoc queries. This is ALL end-users, receiving personalized analytics on their personal devices (think 100s of 1000s of queries per second). These queries are triggered by apps, and not written by people, and so the scale will be as much as the active users on that App (think millions of events/sec)
And, this is for all the freshest possible data, which touches on the other aspect here - realtime analytics. "Yesterday" might be a long time ago for some businesses and they cannot wait for ETLs and batch jobs. The data needs to be used for analytics, as soon as it is generated (think latencies < 1s).
Wanting such a user-facing analytics application, using realtime events, sounds great. But what does it mean for the underlying infrastructure, to support such an analytical workload?
Such applications require the freshest possible data, and so the system needs to be able to ingest data in realtime and make it available for querying, also in realtime.
Data for such apps tend to be event data, for a wide range of actions, coming from multiple sources, and so the data comes in at a very high velocity and tends to be highly dimensional.
Queries are triggered by end-users interacting with apps - with queries per second in hundreds of thousands, with arbitrary query patterns, and latencies are expected to be in milliseconds for good user-experience.
And further do all of the above, while being scalable, reliable, highly available and have a low cost to serve.
This video talks more about user-facing realtime analytics, and how Pinot is used to achieve that.
Here's another great video that goes into the details of how Pinot tackles some of the challenges faced in handling a user-facing analytics workload.
Pinot originated at LinkedIn which currently has one of the largest deployment powering more than 50+ user facing applications such as Viewed My Profile, Talent Analytics, Company Analytics, Ad Analytics and many more. At LinkedIn, Pinot also serves as the backend for to visualize and monitor 10,000+ business metrics.
With Pinot's growing popularity, several companies are now using it in production to power a variety of analytics use cases. A detailed list of companies using Pinot can be found here.
A column-oriented database with various compression schemes such as Run Length, Fixed Bit Length
Pluggable indexing technologies - Sorted Index, Bitmap Index, Inverted Index, StarTree Index, Bloom Filter, Range Index, Text Search Index(Lucence/FST), Json Index, Geospatial Index
Ability to optimize query/execution plan based on query and segment metadata
Near real-time ingestion from streams such as Kafka, Kinesis and batch ingestion from sources such as Hadoop, S3, Azure, GCS
SQL-like language that supports selection, aggregation, filtering, group by, order by, distinct queries on data
Support for multi-valued fields
Horizontally scalable and fault-tolerant
Pinot is designed to execute OLAP queries with low latency. It is suited in contexts where fast analytics, such as aggregations, are needed on immutable data, possibly, with real-time data ingestion.
User facing Analytics Products
Pinot is the perfect choice for user-facing analytics products. Pinot was originally built at LinkedIn to power rich interactive real-time analytic applications such as Who Viewed Profile, Company Analytics, Talent Insights, and many more. UberEats Restaurant Manager is another example of a customer-facing Analytics App. At LinkedIn, Pinot powers 50+ user-facing products, ingesting millions of events per second and serving 100k+ queries per second at millisecond latency.
Real-time Dashboard for Business Metrics
Pinot can be also be used to perform typical analytical operations such as slice and dice, drill down, roll up, and pivot on large scale multi-dimensional data. For instance, at LinkedIn, Pinot powers dashboards for thousands of business metrics. One can connect various BI tools such Superset, Tableau, or PowerBI to visualize data in Pinot.
Instructions to connect Pinot with Superset can found here.
Anomaly Detection
In addition to visualizing data in Pinot, one can run Machine Learning Algorithms to detect Anomalies on the data stored in Pinot. See ThirdEye for more information on how to use Pinot for Anomaly Detection and Root Cause Analysis.
While Pinot doesn't match the typical mold of a database product, it is best understood based on your role as either an analyst, data scientist, or application developer.
Enterprise business intelligence
For analysts and data scientists, Pinot is best viewed as a highly-scalable data platform for business intelligence. In this view, Pinot converges big data platforms with the traditional role of a data warehouse, making it a suitable replacement for analysis and reporting.
Enterprise application development
For application developers, Pinot is best viewed as an immutable aggregate store that sources events from streaming data sources, such as Kafka, and makes it available for query using SQL.
As is the case with a microservice architecture, data encapsulation ends up requiring each application to provision its own data store, as opposed to sharing one OLTP database for reads and writes. In this case, it becomes difficult to query the complete view of a domain because it becomes stored in many different databases. This is costly in terms of performance, since it requires joins across multiple microservices that expose their data over HTTP under a REST API. To prevent this, Pinot can be used to aggregate all of the data across a microservice architecture into one easily queryable view of the domain.
Pinot tenants prevent any possibility of sharing ownership of database tables across microservice teams. Developers can create their own query models of data from multiple systems of record depending on their use case and needs. As with all aggregate stores, query models are eventually consistent and immutable.
Our documentation is structured to let you quickly get to the content you need and is organized around the different concerns of users, operators, and developers. If you're new to Pinot and want to learn things by example, please take a look at our getting started section.
To start importing data into Pinot, check out our guides on batch import and stream ingestion based on our plugin architecture.
Pinot works very well for querying time series data with many dimensions and metrics over a vast unbounded space of records that scales linearly on a per node basis. Filters and aggregations are both easy and fast.
Pinot supports SQL for querying read-only data. Learn more about querying Pinot for time series data in our PQL (Pinot Query Language) guide.
Pinot may be deployed to and operated on a cloud provider or a local or virtual machine. You may get started either with a bare-metal installation or a Kubernetes one (either locally or in the cloud). To get immediately started with Pinot, check out these quick start guides for bootstrapping a Pinot cluster using Docker or Kubernetes.
For a high-level overview that explains how Pinot works, please take a look at our basic concepts section.
To understand the distributed systems architecture that explains Pinot's operating model, please take a look at our basic architecture section.
Cluster is a set of nodes comprising of servers, brokers, controllers and minions.
Pinot uses Apache Helix for cluster management. Helix is a cluster management framework that manages replicated, partitioned resources in a distributed system. Helix uses Zookeeper to store cluster state and metadata.
Helix divides nodes into logical components based on their responsibilities:
The nodes that host distributed, partitioned resources
Pinot Servers are modeled as Participants. For more details about server nodes, see Server.
The nodes that observe the current state of each Participant and use that information to access the resources. Spectators are notified of state changes in the cluster (state of a participant, or that of a partition in a participant).
Pinot Brokers are modeled as Spectators. For more details about broker nodes, see Broker.
The node that observes and controls the Participant nodes. It is responsible for coordinating all transitions in the cluster and ensuring that state constraints are satisfied while maintaining cluster stability.
Pinot Controllers are modeled as Controllers. For more details about controller nodes, see Controller.
Another way to visualize the cluster is a logical view, where:
Typically, there is only one cluster per environment/data center. There is no need to create multiple Pinot clusters since Pinot supports the concept of tenants. At LinkedIn, the largest Pinot cluster consists of 1000+ nodes.
To set up a cluster, see one of the following guides:
Learn about the various components of Pinot and terminologies used to describe data stored in Pinot
Pinot is designed to deliver low latency queries on large datasets. In order to achieve this performance, Pinot stores data in a columnar format and adds additional indices to perform fast filtering, aggregation and group by.
Raw data is broken into small data shards and each shard is converted into a unit known as a segment. One or more segments together form a table, which is the logical container for querying Pinot using SQL/PQL.
Pinot uses a variety of terms that can refer to either abstractions that model the storage of data or infrastructure components that drive the functionality of the system.
Similar to traditional databases, Pinot has the concept of a table—a logical abstraction to refer to a collection of related data.
As is the case with RDBMS, a table is a construct that consists of columns and rows (documents) that are queried using SQL. A table is associated with a schema that defines the columns in a table as well as their data types.
In contrast to RDBMS schemas, multiple tables in Pinot (real-time or batch) can inherit a single schema definition. Tables are independently configured for concerns such as indexing strategies, partitioning, tenants, data sources, and/or replication.
Pinot has a distributed systems architecture that scales horizontally. Pinot expects the size of a table to grow infinitely over time. In order to achieve this, all data needs to be distributed across multiple nodes. Pinot achieves this by breaking data into smaller chunks known as segments (similar to shards/partitions in HA relational databases). Segments can also be seen as time-based partitions.
In order to support multi-tenancy, Pinot has first class support for tenants. A table is associated with a tenant. This allows all tables belonging to a particular logical namespace to be grouped under a single tenant name and isolated from other tenants. This isolation between tenants provides different namespaces for applications and teams to prevent sharing tables or schemas. Development teams building applications will never have to operate an independent deployment of Pinot. An organization can operate a single cluster and scale it out as new tenants increase the overall volume of queries. Developers can manage their own schemas and tables without being impacted by any other tenant on a cluster.
By default, all tables belong to a default tenant named "default". The concept of tenants is very important, as it satisfies the architectural principle of a "database per service/application" without having to operate many independent data stores. Further, tenants will schedule resources so that segments (shards) are able to restrict a table's data to reside only on a specified set of nodes. Similar to the kind of isolation that is ubiquitously used in Linux containers, compute resources in Pinot can be scheduled to prevent resource contention between tenants.
Logically, a cluster is simply a group of tenants. As with the classical definition of a cluster, it is also a grouping of a set of compute nodes. Typically, there is only one cluster per environment/data center. There is no needed to create multiple clusters since Pinot supports the concept of tenants. At LinkedIn, the largest Pinot cluster consists of 1000+ nodes distributed across a data center. The number of nodes in a cluster can be added in a way that will linearly increase performance and availability of queries. The number of nodes and the compute resources per node will reliably predict the QPS for a Pinot cluster, and as such, capacity planning can be easily achieved using SLAs that assert performance expectations for end-user applications.
Auto-scaling is also achievable, however, a set amount of nodes is recommended to keep QPS consistent when query loads vary in sudden unpredictable end-user usage scenarios.
A Pinot cluster comprises multiple distributed system components. These components are useful to understand for operators that are monitoring system usage or are debugging an issue with a cluster deployment.
Controller
Server
Broker
Minion (optional)
The benefits of scale that make Pinot linearly scalable for an unbounded number of nodes is made possible through its integration with Apache Zookeeper and Apache Helix.
Helix is a cluster management solution that was designed and created by the authors of Pinot at LinkedIn. Helix drives the state of a Pinot cluster from a transient state to an ideal state, acting as the fault-tolerant distributed state store that guarantees consistency. Helix is embedded as agents that operate within a controller, broker, and server, and does not exist as an independent and horizontally scaled component.
A controller is the core orchestrator that drives the consistency and routing in a Pinot cluster. Controllers are horizontally scaled as an independent component (container) and has visibility of the state of all other components in a cluster. The controller reacts and responds to state changes in the system and schedules the allocation of resources for tables, segments, or nodes. As mentioned earlier, Helix is embedded within the controller as an agent that is a participant responsible for observing and driving state changes that are subscribed to by other components.
In addition to cluster management, resource allocation, and scheduling, the controller is also the HTTP gateway for REST API administration of a Pinot deployment. A web-based query console is also provided for operators to quickly and easily run SQL/PQL queries.