August 2026 Summaries

Companion.energy migrated its real-time energy-optimization platform from Azure PostgreSQL’s limited TimescaleDB extension to managed Tiger Cloud after growing sensor volumes caused dashboard timeouts, slow latest-reading queries, storage pressure, and an unannounced Azure outage. After evaluating MongoDB, InfluxDB, and ClickHouse, the company retained its PostgreSQL-based architecture to gain TimescaleDB features including skip-scan, compression policies, continuous aggregates, managed observability, database forking, and LiveSync replication. The company reports that skip-scan reduced key meter-reading queries from 4,950 milliseconds to 202 milliseconds, columnstore compression reduced 1.7 TB of logical data to 39 GB of physical storage, and continuous aggregates now support energy-cost dashboards by precomputing data from an 807-million-row raw table. The eight-week migration replicated changes with low latency and was completed with a coordinated connection-string cutover, while the new setup separates market-data workloads from customer-facing operations. Companion.energy expects the platform to accommodate customers ranging from gigabyte to petabyte-scale data requirements without separating its workloads across multiple database systems or redesigning its core data model.

Cardinality in time-series systems arises from the number and distinct values of indexed dimensions, but the costs differ by database architecture: tag-indexed engines often face memory and series-index limits, while PostgreSQL primarily incurs wider indexes, write amplification, vacuum overhead, and planner errors caused by correlated columns. A benchmark using 100 million correlated fleet readings in PostgreSQL with TimescaleDB found that adding one indexed firmware column increased index size by about 865 MB and reduced insert throughput, costing far more at that scale than adding one million rows, while increasing distinct tag counts alone had little effect on index size or estimate accuracy within the tested range. The analysis argues that these effects are schema decisions rather than unavoidable engine ceilings, recommending normalization of stable descriptors into a metadata table and retaining a narrow readings table indexed mainly by tag identifier and timestamp. This design can substantially reduce storage and indexing costs, improve complex filtered rollups, and confine planner misestimates to a much smaller metadata table, although it requires migration work, metadata joins, handling of changing tag attributes and mixed value types, and may be unnecessary when extended PostgreSQL statistics alone resolve the primary problem.

TimescaleDB introduces DeferredChunkAppend in version 2.30 to address slow planning for common hypertable queries such as retrieving the latest device metric with ORDER BY time DESC LIMIT 1. Previously, queries without a time-dimension constraint required TimescaleDB to expand and plan scans for every chunk, causing planning and executor startup costs to grow linearly with chunk count even though ChunkAppend could stop scanning after finding enough rows. DeferredChunkAppend leaves the hypertable unexpanded during planning and lazily opens chunks during execution in the required order, allowing a LIMIT to stop work after only the necessary chunks are visited. It supports direct LIMIT queries on a single hypertable, with limited ordering and filtering conditions, while more complex queries involving joins, aggregates, dimension constraints, row-level security, or certain partitioning schemes retain the existing append-based plans. Benchmarks show planning remains roughly constant at about 0.014 milliseconds with DeferredChunkAppend across up to 10,000 chunks, compared with hundreds of milliseconds or several seconds for expanded plans, and the feature is enabled by default.

Robotics is increasingly constrained less by AI reasoning than by the physical and operational challenges of dexterity, reliability, and data collection, as vision-language-action models advance faster than robot hardware can safely act in unstructured environments. While systems such as Google DeepMind’s Gemini Robotics and other embodied AI models show rapid progress in planning and perception, tasks requiring contact-rich manipulation, near-perfect reliability, and extensive real-world training data remain difficult to deploy autonomously at scale. The text argues that specialized robots are therefore succeeding first: Zipline’s delivery drones, warehouse automation systems, Intuitive’s surgeon-controlled da Vinci platform, and Boston Dynamics’ Spot inspection robots create value by operating within carefully bounded tasks and environments. Even companies pursuing general-purpose robot intelligence, such as Skild AI, currently generate revenue from narrow enterprise applications rather than household humanoids. As deployed fleets grow, the ability to capture, replay, annotate, and reuse operational telemetry may become a crucial infrastructure challenge, enabling machines to improve from failures over time.

PostgreSQL’s EXPLAIN (ANALYZE, BUFFERS) separates Planning Time, spent selecting a query plan, from Execution Time, spent performing the selected operations, and the distinction determines the appropriate optimization strategy. A large partitioned table can incur substantial planning overhead because PostgreSQL must inspect and price many partitions even when pruning ultimately leaves only one to scan, while adding indexes may worsen this cost by creating more paths to evaluate and increasing write overhead. By contrast, execution-bound queries are indicated by long node runtimes, disk reads, temporary-file spills from undersized work memory, or scans that discard many rows; these may benefit from memory adjustments, indexing, or improved storage layout. Incorrect cardinality estimates can also cause poor plans, particularly for correlated columns, and may be improved through extended statistics, higher statistics targets, and ANALYZE, although dependency statistics are limited for range predicates. For planning-heavy repeated queries, prepared statements and generic plan caching can sharply reduce recurring planning time, while reducing excessive partition counts through coarser partitions addresses the underlying structural cost. The text also presents Tiger Data hypertables and continuous aggregates as architectural approaches intended to reduce planning overhead and avoid repeatedly scanning large raw datasets.

Time-series dashboard totals can change without errors in raw data when late-arriving or corrected records fall outside the refresh behavior of precomputed aggregates. Four common approaches handle this differently: scheduled full recomputes eventually capture all changes but repeatedly rebuild everything, insert-triggered incremental views are efficient for new data but do not automatically reconcile history, streaming dataflow engines process late events and updates continuously at the cost of operating an additional system, and TimescaleDB continuous aggregates track invalidations and selectively recompute affected buckets within a configured time window. In TimescaleDB, the critical policy settings are start_offset, which determines how far back refreshes revisit potentially late or changed data, and end_offset, which excludes the newest buckets from materialization while they are still receiving data; together with the schedule interval, they define both correctness coverage and publication delay. Late writes or updates outside the start_offset window can leave aggregates permanently stale, while real-time aggregation only exposes newer, unmaterialized data and does not repair old buckets. Effective operation therefore requires sizing refresh windows according to observed data lateness, aligning raw-data retention with those windows, accounting for additional delay in hierarchical aggregates, and monitoring refresh-job success and timing.

pg_stat_statements is a PostgreSQL extension for identifying database cost centers by aggregating structurally similar queries into fingerprints and tracking their cumulative execution, planning, row, and buffer statistics. The discussion emphasizes ranking queries by total resource use rather than individual latency, since frequent millisecond-scale queries can consume more capacity than occasional slow reports. It explains how to enable the extension with shared preload settings, reset accumulated counters for meaningful measurement, and interpret metrics such as calls, total_exec_time, mean_exec_time, total_plan_time, cache hits, and block reads. A sample workload shows a reporting query dominating execution time through broad disk-heavy scans, while a seemingly fast partitioned-table lookup consumes most server planning time because a stable now() expression prevents plan-time partition pruning. Suggested remedies include passing fixed timestamps, using prepared statements, adjusting partition design, batching or caching high-frequency calls, and considering rollups or columnar storage for recurring large scans. The text also notes limitations including normalized values masking parameter-specific behavior, lack of percentiles and stored plans, entry eviction, incomplete accounting for DELETE-related vacuum work, and differences across PostgreSQL versions.

ControlCom Connect is a vendor-neutral industrial IoT platform from ControlCom Technologies that integrates telemetry from facility equipment, including PLCs, meters, generators, switchgear, and SCADA systems, to provide unified dashboards, automated alerts, an asset relationship graph, and an AI assistant for sectors such as healthcare, manufacturing, utilities, and data centers. Built by a six-person software and electrical engineering team, the platform processes roughly 1,000 to 10,000 data points per second through edge servers, MQTT, Kafka, Redis, and local and cloud-hosted TimescaleDB instances, retaining operational resilience during connectivity losses while maintaining a central portfolio-wide record. The company selected TimescaleDB after evaluating InfluxDB and AWS options because its PostgreSQL foundation allowed it to use SQL, existing tooling, hypertables, compression, and fast time-series queries without introducing a separate database system. ControlCom reports that the system can instantly re-bucket and visualize more than 300 million monthly data points, support real-time AI queries, identify over $160,000 in utility billing errors at a healthcare site, and alert a hospital that backup generators had been left in manual mode. Looking ahead, the company plans to expand real-time stream processing and anomaly detection as its connected asset base and telemetry volumes grow.