StarTree Cloud vs. Elasticsearch for Real-Time Analytics
Say goodbye to indexing challenges, data scalability issues, and high operational costs with StarTree Cloud, the leading Elasticsearch alternative for OLAP.
Elasticsearch is a great search engine, but it wasn’t designed to be an analytics database. StarTree Cloud, the ultimate Elasticsearch alternative for real-time analytics, powered by Apache Pinot is the leading platform providing unmatched performance and ease of use.
6 Reasons to Switch from Elasticsearch to StarTree Cloud
Low-Latency Aggregations at Scale
Maintain performant aggregations against petabytes of data, with latencies measured in milliseconds.
Fast & Flexible Indexing
Leverage multiple indexing options, including the star-tree index for fast and efficient query results.
Better Tiered Storage Solution
Get results in seconds, not minutes with tiered storage designed for fast analytics.
User-Facing Analytics
Unlock your data for internal & external users by supporting extremely high concurrency queries (100,000+ QPS).
Reduce Spend
Save significantly on infrastructure spend with more efficient memory & column storage.
Designed for Purpose
Built for fast, real-time user-facing online analytics processing (OLAP).
StarTree Cloud, Powered by Apache Pinot™
StarTree Cloud, the leading Elasticsearch alternative for OLAP, was purposefully designed for real-time analytics and reporting. It is well-suited for applications that require instant insights into large datasets without needing additional customizations, configurations, or plugins for complex queries. StarTree Cloud can handle high-throughput, low-latency queries, making it ideal for a variety of use cases across social media and collaboration platforms, delivery and ridesharing services, retail and telecommunications companies, financial services, and more.
StarTree Cloud also has additional advantages over Apache Pinot, including StarTree Data Manager, which makes for easy no-code ingestion of data from event streaming systems like Apache Kafka®, live Change Data Capture (CDC) from transactional databases, as well as batch-oriented systems, object stores and a wide variety of data formats.
Real User Stories
"We use Apache Pinot as a core system to empower mission-critical use cases."
— Yupeng Fu, Uber
Uber fully replaced Elasticsearch with Apache Pinot for its time-sensitive real-time analytics. By migrating, they saved more than $2M on infrastructure costs per year and reduced their overall data size by 120TB. The team also saw a 50% reduction in database cores and reduced page load time from 14 seconds to less than 5 seconds. With Apache Pinot, Uber can do real-time upserts and get query results from 1.5PB of data with <100ms P99 latencies.
"Once we saw the raw numbers we decided Elasticsearch should no longer be considered for our further analyses."
— Vaibhav Mittal, Cisco WebEx
Cisco WebEx moved from Elasticsearch to Apache Pinot after seeing how it outperformed their existing infrastructure. Apache Pinot brought query times of 10-30 seconds in Elasticsearch down to sub-second speeds. Apache Pinot was also found to be 4× faster than Clickhouse in most cases in Cisco WebEx’ head-to-head benchmarking comparison.
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Performance Comparison
Cisco
Cisco WebEx found that Apache Pinot provided 5x to 150x lower latencies than Elasticsearch
Cisco Webex obtained subsecond latencies with Apache Pinot in most tested cases, whereas Elasticsearch timed out (>30 seconds) in 67% of cases
Cisco Webex shrank their cluster by >500 nodes moving from Elasticsearch to Apache Pinot
Uniqode
Uniqode (formerly Beaconstac) found that Apache Pinot provided 10x faster queries overall, and 20x lower P95 latencies than Elasticsearch
Uniqode shrank their cluster footprint by 50%, reducing cloud infrastructure spending
Uber
Uber was able to shrink the number of cores used by 50% switching to Apache Pinot vs. Elasticsearch, saving >$2 million in infrastructure costs
Advantages of StarTree Cloud
Compare the features of Apache Pinot to Elasticsearch and you’ll see Pinot offers far more flexible indexing and ingestion capabilities to perform real-time analytics.
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Column Store for Efficient Analytics |
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| SQL | ||
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Multi-Stage Query-Time JOINs |
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Conformance to ANSI SQL |
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| Indexing Strategies | ||
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Inverted Index |
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Sorted Index |
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Range Index |
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JSON Index |
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Geospatial Index |
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Star-Tree Index |
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Bloom Filter |
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Text Index |
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Timestamp Index |
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Sparse Index |
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| Ingestion | ||
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Upserts (Full-row and partial row) |
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Change Data Capture (CDC) |
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Out-of-Order Handling |
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Real-Time Deduplication |
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| Event Streaming Integration | ||
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Apache Kafka |
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Apache Kinesis |
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Apache Pulsar |
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Google PubSub |
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| Data Warehouse Connectors (Batch Ingestion) | ||
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Snowflake |
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Delta Lake |
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Google Big Query |
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| Object Store Support (Batch Ingestion) | ||
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Amazon S3 |
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Via Amazon Lambda |
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Google Cloud Storage (GCS) |
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Azure Data Lake Storage (ADLS) Gen2 |
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Hadoop Distributed File System (HDFS) |
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| Batch Ingestion File Formats | ||
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Avro |
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As Logstash Events |
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CSV |
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JSON |
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ORC |
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Parquet |
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Protocol Buffers (Profobuf) |
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As Logstash Events |
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Thrift |
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TSV |
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| Data Analytics Integration | ||
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Apache Spark 3 |
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| Tiered Storage | ||
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Multi-Volume Tiering |
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Compute Node Separation |
Via Mulitple Tenants |
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Cloud Object Storage* |
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Via "Frozen Tier" |
*Not All Tiered Storage is the Same
StarTree Cloud offers tiered storage in a way that far exceeds the performance of Elasticsearch. StarTree allows you to run your application’s fastest data on locally attached NVMe storage. Or you can also use block storage for greater resiliency. Plus you can use cost-effective distributed object stores such as Amazon S3, Google Cloud Storage, or Azure Data Lake Storage. Performance on these objects stores will be far faster than Elasticsearch’s “Frozen Tier,” which will produce query results in scales measured by minutes, not seconds.
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