42× Faster DELETEs: Accelerating Analytics and High-Volume Ingestion with TimescaleDB 2.21

Modern data-intensive applications – from IoT sensor platforms to high-frequency financial systems to crypto analytics – generate massive amounts of time-series data that often needs to be deleted or pruned regularly. Whether it’s dropping obsolete sensor readings, purging old trading ticks, or removing reorged blocks on a blockchain, deleting large swaths of data is a necessary task. Unfortunately, vanilla PostgreSQL struggles with massive DELETE operations, which can become slow and resource-intensive. 

In a standard PostgreSQL setup, deleting millions of rows means each row is marked as dead, left for background vacuum processes to clean up later, resulting in table bloat and degraded performance. Large DELETEs can also lock up tables and slow down your application. In practice, many Postgres users avoid huge deletes altogether – opting instead for partitioning and dropping partitions— because row-by-row deletion is slow and painful at scale.

Addressing the Challenge of Large-Scale Data Deletion in Postgres

TimescaleDB, an open-source time-series database built on PostgreSQL (created by us here at TigerData) has long recognized this challenge and provided better strategies. For example, TimescaleDB hypertables (Postgres partitions managed automatically) enable easy use of data retention policies to drop whole chunks of old data in one operation. Dropping an entire chunk (partition) is essentially just deleting a file, which is much faster and avoids the bloat and vacuum overhead of row-by-row deletes. In fact, we historically recommended using its drop_chunks retention policy (or automatic scheduling of such) instead of large DELETE ... WHERE time < X commands. But what about cases where you can’t drop an entire time-partition? What if you need to delete specific subsets of data (e.g. one device’s data, or a particular time range within each chunk)? Until recently, even with TimescaleDB’s optimizations, those targeted deletes would revert to the slow, bloat-inducing process under the covers.

TimescaleDB 2.21 changes this. In the latest release, we introduced massive optimizations for DELETE operations on compressed data, making such deletes up to 42× faster in real-world scenarios. With these improvements, TigerData positions itself as the fastest Postgres for heavy data management tasks – allowing you to delete large volumes of data with minimal performance impact, something standard Postgres cannot handle nearly as efficiently. Let’s dive into how this works and why it’s so impactful for IoT, finance, crypto, and other demanding workloads.

The Cost of Traditional DELETEs (and Why They Were Slow)

To appreciate the improvement, it helps to understand what makes deleting data (especially in compressed, columnar storage) so expensive in a typical database setup:

• Row-by-Row Deletion & Bloat: In vanilla PostgreSQL, a DELETE doesn’t physically remove rows immediately – it marks them as dead (tombstones), leaving the actual row data in place. The table and indexes grow with “dead tuples” that still occupy space, leading to bloat. A background VACUUM must later purge these dead rows to reclaim space and update indexes. Until vacuum runs, those deleted rows consume disk space and can slow down sequential scans. This whole cycle makes large deletes both slow to execute and costly afterward (vacuuming can be heavy on I/O and CPU). If you delete millions of rows, you’re left with millions of dead tuples and a bloated table that might even grow before it gets smaller.
• Deleting Data in the Columnstore: TimescaleDB stores compressed chunks in a columnstore layout, grouping values from each column together in dense, columnar batches. Before TimescaleDB 2.21, deleting a single row from those batches triggered a costly chain reaction: the engine had to locate the columnstore batch that held the row, decompress that entire batch into a temporary row‑oriented table, rebuild any needed indexes on the fly, delete the targeted tuples, and then either recompress the batch or leave it partially empty. In other words, one DELETE on a compressed hypertable became: find batch → decompress → copy rows into temp storage → delete matches → recompress (or not). This multi‑step dance consumed significant CPU and I/O, and it left behind “ghost” space until a later recompression or vacuum reclaimed it.
• Limited Optimizations in Earlier Versions: An earlier optimization in TimescaleDB allowed a special case: if your DELETE filter targeted an entire segment-by key (a column by which the data within a chunk is grouped for compression), TimescaleDB could skip decompression and simply drop that portion of data. For example, if your hypertable was compressed with SEGMENTBY device_id, then deleting all data for device_id = 42 could be done by removing the compressed batch for device 42 directly – no decompression needed. This was already a big improvement for certain whole-device or whole-customer deletions (similar to satisfying GDPR “right to be forgotten” by deleting all user data in one shot). However, this optimization was limited – as soon as you added any other condition (e.g. WHERE device_id = 42 AND time < '2025-01-01'), the system fell back to the old behavior of decompressing and row-by-row deletion. In practice, that meant you only got the benefit for very broad deletes (all data for a key, or all data in a chunk if dropping chunk). Many real-world delete scenarios couldn’t use it.

The end result in the past was that targeted deletions on large datasets were something to be wary of. You either had to design your data retention strictly around chunk drops (deleting everything older than X), or bite the bullet with slow, bloating deletes for anything more granular. This was an area where vanilla Postgres was clearly lacking and TimescaleDB, while better due to chunking, still had room for improvement.

TimescaleDB 2.21: Smarter Batch Deletes – Up to 42× Faster

TimescaleDB 2.21 introduces a huge leap forward by eliminating most of the overhead described above for a much broader range of DELETE operations. The core idea is simple: if a DELETE can be satisfied by removing whole compressed batches or whole chunks, do that instead of picking through individual rows. TimescaleDB now smartly detects patterns in your WHERE clause to determine if entire segments of data can be dropped in one go. This avoids the expensive decompression and per-row deletion process whenever possible.

Here are the key improvements that make deletes so much faster (for real-world use cases) in 2.21:

• Batch-Level Deletion on Segmentby Filters (Beyond Exact Matches): Previously, only a pure segmentby filter (e.g. device_id = 42) would trigger batch deletion. In 2.21, TimescaleDB can handle segmentby + additional filters. For example, consider DELETE FROM readings WHERE device_id = 42 AND time >= '2025-07-01'; – if your data is segmented by device_id, TimescaleDB will examine the compressed batches for device 42 and identify entire batches that fall within the time range to delete. Those entire batches will be dropped without decompression. Any batches that partially fall in range may still require finer processing, but much of the work is avoided. This means you can now target a single device’s data for a specific sub-period and still get the performance boost. In general, queries that filter on a segmentby column and any other column(s) can benefit, as long as large portions of data (whole batches) meet the criteria.
• Faster Deletes on Time-Range Filters: Even if you aren’t using an extra segmentby column in compression, you often delete by a time range (for example, remove a day’s worth of data that is bad or all data older than X date when doing custom retention). TimescaleDB 2.21 extends the batch-deletion logic to time filters on compressed chunks as well. If a DELETE’s WHERE clause covers entire chunk(s) or entire compressed batches within chunks (say, deleting all rows in a certain day), TimescaleDB will drop those batches/chunks outright instead of iterating through rows. Effectively, this is similar to what the automated retention policy (drop_chunks) does, but now it can happen automatically as part of a DELETE command on a compressed hypertable. For example, DELETE FROM readings WHERE time < '2025-01-01'; might end up just dropping all chunks entirely before 2025 (which is nearly instantaneous), and even for chunks that span the cutoff, it will drop the fully covered portion of each. In our internal benchmarks, these improvements led to DELETE operations running up to 42× faster than before. That’s not a typo – a delete that previously took, say, 10 minutes might now complete in under 15 seconds in the best cases. Even for more mixed cases, the speedups are dramatic.
• Minimal Bloat and Lower Resource Usage: By avoiding the decompression and row-marking process, TimescaleDB 2.21 also dramatically reduces the bloat and cleanup overhead associated with large columnstore deletes. This means subsequent queries run faster (no tombstones to scan over) and VACUUM has little or nothing to do. CPU and I/O usage during the delete is also far lower, since we’re not churning through each row. The net effect is not just a faster delete operation, but a healthier database afterward. In real-world use, this translates to less load on your system and more predictable performance. You can run heavy deletions during the day without knocking over your workload, something that was risky with vanilla Postgres.

All these enhancements make TimescaleDB uniquely capable of handling both extremes of data lifecycle management: dropping entire large partitions instantly and surgically deleting specific data fast. The 2.21 release essentially bridges the gap, so that you no longer need to fear a DELETE query on a compressed hypertable. It’s going to be efficient in a wide range of cases now. TimescaleDB 2.21 has turned one of PostgreSQL’s pain points into a strength.

TimescaleDB 2.21 has turned one of PostgreSQL’s pain points into a strength.

Real-World Impact: IoT, Finance, and Crypto Use Cases

Who benefits from 42× faster deletes and slimmer tables? Any application dealing with large time-series or event data. Let’s look at a few scenarios:

• Industrial IoT Platforms: Imagine an IoT deployment with thousands of sensors (devices) each writing data every second. It’s common to remove data for devices that are decommissioned or malfunctioning. With vanilla Postgres, deleting all readings for a given device could mean millions of row deletes and a bloated table, possibly taking hours and heavily impacting performance. TimescaleDB’s segmented compression turns this into a fast operation – if your hypertable is segmented by device_id, a query like DELETE FROM sensor_data WHERE device_id = 123; will instantly drop the compressed batches for that device. Now with 2.21, even if you scoped it further (say a specific date range for that device), TimescaleDB will still drop whole segments whenever possible, avoiding row-by-row work. This means IoT companies can retire a sensor or erase bad data readings on the fly, without downtime. Likewise, periodic cleanup of old data (data older than 1 year, for example) can be done chunk-wise. Many IoT use cases also use data retention policies – e.g. keep only 6 months of high-resolution data – which TimescaleDB handles via automated chunk drops. With the new improvements, if you need custom deletion beyond what retention policies cover, you’re still in good shape performance-wise.
• Financial Market Data and Trading Systems: Financial databases often store tick-by-tick or trade-by-trade data, which accumulates to billions of rows. Typically, firms aggregate this detailed data into daily or hourly summaries for long-term analysis, and then purge the raw ticks after some months to save space. Continuous Aggregates (discussed below) in TimescaleDB are perfect for creating those summaries. Now, with TimescaleDB 2.21, deleting the old raw data is incredibly efficient. For example, a stock exchange might delete all order book events older than 90 days after rolling them up into daily stats. In plain Postgres, deleting billions of rows (even partitioned) would generate huge bloat and likely require scheduling downtime or performing cumbersome partition swaps. Under TimescaleDB, this can translate to simply dropping entire chunks for older dates (a metadata operation) or issuing a time-range delete that the engine optimizes into batch drops. The speed difference is enormous – what was once a slow batch job can become a quick maintenance query. Moreover, if a particular instrument’s data (say all trades for symbol = XYZ) needs to be removed (due to a correction or compliance request), TimescaleDB can eliminate those quickly if the data was segmented by symbol. In short, financial tech teams get a PostgreSQL that can handle big data cleanup as nimbly as it handles inserts.
• Crypto and Blockchain Analytics: Time-series aren’t just sensor readings – they include crypto exchange logs, blockchain node data, telemetry from decentralized networks, etc. These systems often ingest high-volume data that needs periodic pruning. For instance, a blockchain data platform might store every transaction or event from multiple chains, but only need detailed data for the past year, with older data summarized. With TimescaleDB, one can keep a rolling window of detailed blockchain data and rely on continuous aggregates for older periods. The heavy lifting of deletion (removing last year’s low-level logs, for example) can be done by TimescaleDB efficiently: issuing a DELETE ... WHERE time < last_year will essentially drop dozens of old chunks in one shot. Another scenario is removing spam or irrelevant on-chain events – if identified by an attribute, those can be deleted batch-wise too. What’s key for the crypto use case is minimizing downtime and cost: running a full vacuum on a terabyte-scale table in Postgres after deletes could take hours of high I/O, but TimescaleDB’s approach avoids that, keeping the database lean as it goes. This translates to lower storage use and stable query performance, which is crucial when your system is analyzing real-time crypto data with no tolerance for slowdowns.

Across these examples, the common theme is that TimescaleDB lets you manage data lifecycle (aging out or correcting data) at scale without the headaches. You don’t have to hack around PostgreSQL’s limitations or schedule maintenance windows for big deletions. The 42× improvement isn’t just a benchmark number – it’s enabling use cases that were previously impractical on Postgres without help. And importantly, these benefits come while preserving PostgreSQL’s reliability and rich features. You still get full SQL, strong ACID guarantees, and the entire Postgres ecosystem.

TigerData: Delivering Speed Without Sacrifice

TimescaleDB 2.21 cements TigerData’s position as the creator of the fastest and most scalable PostgreSQL solution for time-series and analytics data. By making DELETE operations on compressed data up to 42 times faster and virtually eliminating the bloat and maintenance pains that plague standard Postgres in such cases, TimescaleDB removes another bottleneck for big data workloads. 

It’s worth noting that all of this comes without sacrificing PostgreSQL’s core strengths.

It’s worth noting that all of this comes without sacrificing PostgreSQL’s core strengths. TimescaleDB is not a fork but an extension – you still use standard SQL, the same ecosystem of tools, and have strong ACID guarantees. What TimescaleDB 2.21 demonstrates is that you can have speed and scale and SQL reliability together. Vanilla Postgres alone often forces painful choices: either keep all data and suffer slow queries and huge storage, or delete data and suffer slow deletes and downtime. TimescaleDB lets you keep your system lean and fast at every stage: you can ingest high volumes, query complex aggregates in real-time, compress data to save 90%+ storage, and now delete or downsample old data aggressively – all while improving performance rather than hurting it.

For organizations in IoT, finance, crypto, SaaS analytics, or any domain dealing with large datasets, TimescaleDB 2.21 offers a compelling value: a PostgreSQL that simply handles more data, faster. If you’ve been managing a time-series application on Postgres and struggling with upkeep (manual partition management, vacuum tuning, etc.), TimescaleDB can likely drop in and solve those problems. The continuous innovation – like this new deletion optimization – means the gap is only growing between what’s possible with TimescaleDB versus stock Postgres.

Wrapping it up, TimescaleDB 2.21 makes PostgreSQL deletions (and overall data management) faster and easier than ever before. By leveraging intelligent batch deletion, it turns a once-costly operation into a trivial one. Coupled with its other features (hypertables, continuous aggregates, high-performance compression), TimescaleDB empowers you to maintain a high-performance, low-footprint database even as your time-series data scales to billions of rows. Stay tuned for later next week when we’ll drop an overview on all of the other goodies that have landed in TimescaleDB’s 2.21 release!

About the Author

Noah Hein is a Senior Product Marketing Engineer at TigerData, where he helps developers understand, adopt, and succeed with the fastest PostgreSQL platform for real‑time and analytical workloads. Day‑to‑day, he translates deep technical capabilities—like hypertables, hypercore compression, and continuous aggregates—into clear product narratives and customer stories that drive adoption and growth.

Before joining TigerData, Noah spent several years on the “builder” side of the house as both a founding engineer and an educator. He co‑created Latent Space’s three‑week AI Engineering Fundamentals course and has taught hundreds of engineers how to apply LLMs in production. Noah frequently speaks on AI‑data convergence topics; at the first ever AI Engineer Summit he led the “AI Engineering 101” workshop, walking participants through hands‑on projects.

Outside of work, Noah tries to help more people land jobs with his side project JobMosaic. When he’s not crafting launch posts, you’ll find him experimenting with edge‑AI devices, tinkering with homelab Postgres clusters, or giving impromptu botany lessons to anyone who will listen.