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Announcing General Availability of Timeplus Enterprise v2.8

  • Writer: Jove Zhong
    Jove Zhong
  • May 30
  • 5 min read

Timeplus Enterprise v2.8 is now Generally Available! This release incorporates 3 key advancements: 

  • Integration with Apache Iceberg

  • A novel compute node server role, facilitating the elastic execution of Materialized Views with S3 checkpoints

  • Tiered storage, optimizing data retention by allocating recent data to local SSDs while archiving older data in S3 for cost efficiency


Powered by our lightweight, powerful, and efficient single-binary platform, Timeplus Enterprise offers flexible deployment options. Whether fully managed in the public cloud, self-hosted in your data center, running on your laptop, or even on edge devices, our platform adapts to diverse user needs and unblocks massive use cases, from handling large-scale workloads to operating in resource-contained edge environments, capturing real-time insights where data resides. Most importantly, Timeplus Enterprise drives down operational efforts and costs down with simplicity. 


Our software has been rigorously tested and proven in a variety of industries and mission-critical use cases, including cybersecurity, algorithm-based trading, and real-time streaming pipeline and analytics.


In a recent case study, we explored how a leading cryptocurrency data platform leveraged Timeplus Enterprise to streamline fragmented tooling, reduce analytics latency, and improve cost efficiency. While the use case centers on blockchain data, the lessons learned apply broadly to industries dealing with high-cardinality, mutable data and real-time analytics demands. 


“We tried countless tools and rewrote our pipeline multiple times. Timeplus was the first solution that let us index the full Ethereum chain—and now, any chain—in real-time. It’s transformed how we deliver data to our customers.”

— Mathew Haji, Founder at Zyre


Today, we are excited to upgrade our offerings with the release of Timeplus Enterprise v2.8. This unified, enterprise-grade product can be installed on various infrastructures, including bare metal, Docker, and Kubernetes.


Check out our v2.8 launch video here:


For more feature introductions and demos, check out the full webinar recording here.



Read/Write data in Apache Iceberg Open Table Format


Apache Iceberg is an open table format for large-scale analytic datasets, designed for high performance and reliability. It provides an open, vendor-neutral solution that supports multiple engines, making it ideal for various analytics workloads. Initially, the Iceberg ecosystem was primarily built around Java, but with the increasing adoption of the REST catalog specification, Timeplus is among the first vendors to integrate with Iceberg purely in C++. This allows Timeplus users to stream data to Iceberg with a high performance, low memory footprint, and easy installation without relying on Java dependencies.

In this release, we provide native support for Apache Iceberg as a new database type. This allows you to read and write data using the Apache Iceberg open table format, with support for the Iceberg REST Catalog (IRC). In the initial release, we focused on writing data to Iceberg, with basic query optimization for reading data from Iceberg. The integration with Amazon S3, AWS Glue's Iceberg REST endpoint and the Apache Gravitino Iceberg REST Server are validated. More REST catalog implementations are planned.



Key Benefits for Timeplus Iceberg Integration


  • Using Timeplus materialized views, users can continuously process and transform streaming data (from Apache Kafka for example) and write to the cost-effective object storage in Apache Iceberg open table format.

  • Apache Iceberg's open table format ensures you're never locked into a single vendor or query engine

  • Query your Iceberg tables with multiple engines including Timeplus, Apache Spark, Apache Flink, ClickHouse, DuckDB, and AWS Athena

  • Future-proof your data architecture with broad industry support and an active open-source community



Example: AWS S3 Table REST Catalog


The General Availability of AWS S3 Table offers a streamlined approach to leveraging Iceberg through a fully managed, serverless solution. To establish a connection with S3 Table, execute the following SQL statement within Timeplus.

CREATE DATABASE demo
SETTINGS  type='iceberg',
          catalog_uri='https://glue.us-west-2.amazonaws.com/iceberg',
          catalog_type='rest',
          warehouse='(aws-12-id):s3tablescatalog/(bucket-name)',
          rest_catalog_sigv4_enabled=true,
          rest_catalog_signing_region='us-west-2',
          rest_catalog_signing_name='glue';

Next, you can list existing Iceberg tables via:

SHOW STREAMS FROM demo

Or, you can query them via demo.table_name in Timeplus.



Writing to Iceberg via a Materialized View


You can run INSERT INTO statements to write data to Iceberg tables, or set up a materialized view to continuously write data to Iceberg tables.

CREATE MATERIALIZED VIEW mv_write_iceberg INTO demo.transformed AS
SELECT now() AS timestamp, org_id, float_value,
       length(`array_of_records.a_num`) AS array_length,
       array_max(`array_of_records.a_num`) AS max_num,
       array_min(`array_of_records.a_num`) AS min_num
FROM msk_stream_read
SETTINGS s3_min_upload_file_size=1024;


Demo Video



Elastic Materialized Views with Auto-Scaling Compute Nodes


Built for fault tolerance, auto-recovery, performance, and efficiency, materialized views are the key building blocks in Timeplus for real-time data pipelines.


Timeplus Enterprise 2.8 introduces Compute Nodes, a new type of server node that leverages AWS Spot Instances or Auto Scaling Group. This allows Timeplus clusters to dynamically add or remove nodes to schedule the materialized views on-demand, in response to changing workloads.



Materialized views configured with S3 disk-based checkpoints are scheduled on ephemeral compute nodes. These nodes read data from remote sources (e.g., Apache Kafka), process it locally, and send the results to downstream destinations (e.g., ClickHouse or another Kafka topic).



A new page has been created in Timeplus Console to visualize the dynamic nodes in the cluster.




To learn more, check out this intro and demo video by our Chief Architect, Ken Chen:



S3 Tiered Storage


Timeplus is designed to be both high-performance and user-friendly. You can deploy it in your data center without any additional dependencies. By utilizing high-performance NVMe SSD local disks, Timeplus can achieve the highest possible disk I/O and deliver the lowest latency streaming analytics.


To reduce local disk costs for scenarios where multiple terabytes or even petabytes of data are stored in a Timeplus cluster, Timeplus Enterprise v2.8 introduces Tiered Storage. This feature allows users to combine local and remote storage, storing hot data in local high-performance storage (like NVMe SSD) for quick access, and moving older data to object storage (like S3) for long-term retention.



To configure Tiered Storage, users define a storage policy that specifies the storage tiers and their priorities. The storage policy can then be applied to one or more streams.



In the example, a stream called “my_stream" is created with a Time-To-Live (TTL) policy that moves data older than 7 days to cold storage. The cold storage is within the "hcs" storage policy, which utilizes an S3 disk named "tiered_s3_volume." Data can be continuously added to "my_stream" through materialized views, the ingestion API, or INSERT commands. The most recent seven days of data are stored on Timeplus cluster disks, while older data is automatically moved to the S3 bucket. Users can query both local and remote data using SQL within Timeplus.



Other Enhancements


  • Direct read and write access to PostgreSQL tables, or data lookup via dictionaries, through PostgreSQL External Table.

  • New SQL command for renaming streams and columns.

  • JavaScript UDFs benefit from multiple V8 instances, improving concurrency and isolation.

  • Clean up cache when updating Protobuf schemas

  • New page providing detailed views for streams and materialized views.



Learn More


Thank you for being a part of our journey. We look forward to your continued support and collaboration as we move towards a future powered by real-time streaming analytics.


To explore the capabilities of Timeplus Enterprise v2.8, please check out https://docs.timeplus.com/enterprise-v2.8 for the installation guide and change logs.


Ready to try Timeplus Enterprise? Try for free for 30-days. 


Join our Timeplus Community! Connect with other users or get support in our Slack community.




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