---
source_url: https://gemini.google.com/share/9cb942ff065d
source_type: gemini-3-pro
scraped_at: 2026-02-04T05:30:52Z
purpose: Research SOTA progress logging patterns for populate_cache_resumable() Issue #70
tags: [progress-logging, tqdm, loguru, eta-estimation, financial-data, etl]

# REQUIRED provenance
model_name: Gemini 3 Pro Deep Research
model_version: gemini-3-pro-deep-research
tools: []

# REQUIRED for Claude Code backtracking + context
claude_code_uuid: 527eb0ed-e5c4-4338-98d2-c2ce29b726d2
claude_code_project_path: "~/.claude/projects/-Users-terryli-eon-rangebar-py/527eb0ed-e5c4-4338-98d2-c2ce29b726d2"

# REQUIRED backlink metadata
github_issue_url: https://github.com/terrylica/rangebar-py/issues/71
---

# Comprehensive Guide to Progress Observability Patterns for Long-Running Financial Data Pipelines

## 1\. Executive Summary

The engineering of high-volume financial data pipelines presents a unique intersection of challenges involving data volume, processing volatility, and operational opacity. The specific scenario under analysis—a Python function (`populate_cache_resumable()`) processing six years of financial tick data into ClickHouse—epitomizes the "black box" execution model that plagues batch processing systems. In this model, long-running functions operate in complete silence, offering operators no insight into liveness, throughput, or estimated time of arrival (ETA) until completion or failure. This report provides an exhaustive analysis of progress logging patterns designed to illuminate this black box, focusing on parameter-free adaptation, memory efficiency, and robust estimation algorithms capable of handling the "bursty" nature of financial markets.

Our research indicates that the industry standard has shifted decisively away from manual print statements and simple callbacks toward **wrapped iterator patterns** and **structured logging pipelines**. However, standard implementations often fail in financial contexts due to the non-uniform density of tick data. The "volatility smile," where market volume spikes at the open and close while collapsing during midday, renders simple linear interpolation for ETA calculation useless. A robust solution requires a composite architecture: a **Hybrid Iterator-Observer Pattern**. This architecture utilizes adaptive libraries like `tqdm` for immediate console feedback, enhanced by advanced smoothing algorithms (such as Double Exponential Smoothing or Kalman Filters) to handle volatility in processing rates.

Furthermore, to satisfy the requirement for machine readability without sacrificing human usability, we recommend decoupling the visual progress bar from the audit trail via a **Dual-Sink Logging Strategy**. This involves using a structured logging framework (e.g., `loguru`) to emit JSONL events at adaptive, time-based intervals, ensuring that downstream monitoring systems receive parseable metrics while the console remains clean. This report details the theoretical underpinnings, algorithmic implementations, and architectural integrations necessary to construct a "parameter-free," non-intrusive, and resilient observability layer for mission-critical financial ETL (Extract, Transform, Load) workloads.

## 2\. The Observability Gap in High-Frequency ETL

### 2.1 The Operational Cost of Silence

In the domain of Data Engineering, silence is ambiguous and operationally expensive. A script that produces no output for twenty minutes could be processing a particularly dense data partition efficiently, or it could be hung on a network socket, trapped in an infinite retry loop, or deadlocked waiting for a database lock. For a function like `populate_cache_resumable()`, which is expected to run for hours processing years of historical data, this ambiguity transforms routine maintenance into high-stress incident response. The "Silence is Failure" paradigm suggests that any process running longer than a human attention span—approximately ten seconds—must emit a "heartbeat" to confirm its liveness.

The challenge is exacerbated by the specific nature of the workload. Financial tick data is not merely "large"; it is structurally irregular. The volume of data generated during a market crash or a high-volatility event like the COVID-19 onset in March 2020 is orders of magnitude higher than during a stable, low-volatility period like 2017. A silent process creates a blind spot where operators cannot distinguish between a healthy "long tail" processing of a volatile day and a zombie process that has ceased to function. This lack of feedback forces operators to make binary choices—kill the process and potentially corrupt data, or wait indefinitely—based on intuition rather than metrics.

### 2.2 The Duality of Observability Consumers

Designing an effective logging system requires recognizing that there are two distinct consumers of progress data, each with contradictory requirements. The first consumer is the **Human Operator**. This user monitors the console during ad-hoc backfills or debugging sessions. Their primary need is psychological reassurance—"liveness"—and rough planning data. They prioritize visual feedback, such as progress bars, smoothed throughput numbers, and "Time Remaining" estimates that do not fluctuate wildly. They require information density to be low but highly relevant; a screen scrolling effectively at 100 lines per second is unreadable and useless to a human.

The second consumer is the **Machine Monitor**. This includes log aggregators like Datadog, Splunk, or the ELK Stack, as well as downstream alert systems. These consumers require structured, queryable events, typically in JSON format. They need precise timestamps, batch identifiers, and raw throughput metrics to detect regression trends over months. Crucially, they do not care about "ETA" or "Percentage Complete" in the visual sense, but rather the "Rate of Change" and the presence of errors. The tension between these two consumers—humans wanting smoothed, visual summaries and machines wanting granular, structured streams—dictates that a single output channel (e.g., `print` statements) is insufficient.

### 2.3 The Failure of Naive Logging Patterns

A pervasive anti-pattern in ETL script development is the "Log Every N Items" approach. Developers often implement a modulo check within the inner loop:

Python

    for i, row in enumerate(data):
        process(row)
        if i % 1000 == 0:
            print(f"Processed {i} rows")

This introduces "Magic Numbers" into the codebase. The value `1000` is arbitrary and brittle. On a modern CPU processing simple CSVs, 1000 rows might take 1 millisecond, causing the console to flood, the I/O buffers to fill, and the processing speed to degrade due to the overhead of synchronous printing. Conversely, if the processing involves complex quantitative analysis or database insertions, 1000 rows might take ten minutes, returning the system to a state of unacceptable silence.

As data density changes over the six-year dataset, the fixed interval `N` becomes obsolete. A setting appropriate for 2014 data might cause log flooding when processing 2020 data. The research goal, therefore, is **Parameter-Free Adaptability**, where the system self-regulates its logging frequency based on **Time** (e.g., "log every 5 seconds") or **Information Gain**, rather than arbitrary counts. This shift from count-based to time-based logging is fundamental to building robust, non-intrusive observability.  

## 3\. State-of-the-Art Progress Patterns in Python

To address the requirements of non-intrusive, memory-efficient logging, we must evaluate the structural patterns available in the Python ecosystem. The evolution of progress tracking has moved from primitive print statements to sophisticated iterator wrappers and context managers.

### 3.1 The Iterator/Generator Pattern (The "Wrapped" Approach)

The current industry standard for progress tracking in Python, popularized by libraries such as `tqdm` (an abbreviation for "taqaddum" meaning progress in Arabic) , is the **Wrapped Iterator** pattern. This pattern leverages Python's protocol for iterators and generators to inject observability.  

In this pattern, the progress object acts as a transparent proxy around the data iterable.

Python

    # The Standard Iterator Pattern
    from tqdm import tqdm
    for batch in tqdm(data_generator(), total=estimated_total):
        process(batch)

The `tqdm` object intercepts the `__iter__` and `__next__` calls. It maintains an internal counter and a timer. When `next()` is called, it increments its count, checks if enough time has passed to warrant a screen update, and then yields the item to the inner loop.

**Advantages**: This approach is highly **non-intrusive**. The inner loop logic—the actual business value of `populate_cache_resumable()`—remains untouched. The progress tracking is applied as a decoration to the data source. It is also **memory efficient**. Because it utilizes the generator protocol, it does not require loading the entire dataset into memory to count it. It effectively acts as a pass-through filter.  

**Limitations**: The primary limitation arises with **Unknown Totals**. If `data_generator()` is a stream (e.g., reading from a socket or a database cursor without a count query), `len()` is undefined. The progress bar defaults to "Indeterminate Mode," displaying a pulsing animation or a simple counter without a percentage or ETA. For the user's ClickHouse use case, where `count()` might be expensive to run on a 6-year dataset, this is a significant consideration.  

### 3.2 The Callback Pattern

The Callback pattern involves passing a function `on_progress(current, total)` into the worker function.

Python

    def worker(data, progress_callback=None):
        for i, item in enumerate(data):
            #... work...
            if progress_callback:
                progress_callback(i, len(data))

**Analysis**: While flexible, this pattern is generally considered less Pythonic than the iterator pattern for simple loops. It tightly couples the worker logic to the reporting mechanism. It requires modifying the function signature to accept the callback. However, for **resumable operations** where the state is complex—for example, tracking bytes read, rows inserted, and distinct partitions touched simultaneously—a callback object can carry a richer state payload than a simple iterator yield. It allows for "Event-Driven" logging where the worker decides when a significant milestone has been reached, rather than the loop driver.  

### 3.3 The Observer/Context Pattern (Enlighten/Rich)

Modern libraries like `enlighten` and `rich` often employ a Context Manager pattern that creates a "Display Surface" separate from the logging stream. This addresses the specific problem of **console interference**.

Python

    with enlighten.get_manager() as manager:
        pbar = manager.counter(total=100, unit='ticks')
        for i in range(100):
            #... work...
            pbar.update()

**Key Insight**: `enlighten` specifically solves the **logging interference problem**. In standard `tqdm` usage, if the application prints a log message to `stdout` while the progress bar is active, the bar is often broken or duplicated on the next line. `enlighten` manages the terminal cursor to keep the progress bar pinned to the bottom of the screen while allowing log messages to "scroll" past behind it. This is a critical feature for the user's requirement of simultaneous structured logging and visual progress tracking.  

### 3.4 Feature Comparison of Leading Libraries

The following table synthesizes the capabilities of the leading Python progress libraries relative to the user's specific constraints.

| Feature            | `tqdm`                 | `rich.progress`         | `enlighten`        | Manual Callback     |
| ------------------ | ---------------------- | ----------------------- | ------------------ | ------------------- |
| **Parameter-Free** | Yes (`mininterval`)    | Yes (Auto-refresh)      | Yes                | No (User defined)   |
| **Overhead**       | Ultra-low (~60ns/iter) | Moderate (Rich text)    | Moderate           | Variable            |
| **Visual Style**   | ASCII/Unicode          | Modern/Colorful         | Multi-bar Terminal | N/A                 |
| **Log Interop**    | Requires `tqdm.write`  | Explicit Console Object | Native Scrolling   | Manual Handling     |
| **ETA Algo**       | EMA (Simple)           | Weighted Average        | Simple Linear      | N/A                 |
| **Resumability**   | `initial` param        | `completed` param       | Counter setup      | Manual Logic        |
| **Relevance**      | **5/5 (Performance)**  | **4/5 (UX)**            | **4/5 (Log Mix)**  | **2/5 (Intrusive)** |

**Recommendation**: `tqdm` remains the most robust choice for high-performance ETL due to its minimal overhead and mature handling of standard streams. However, for applications requiring heavy simultaneous logging, `enlighten` or `rich` provide better terminal management.

## 4\. Algorithmic Solutions for Adaptive Intervals

The user's explicit request for **Parameter-Free** approaches necessitates moving beyond fixed-count logging. We must implement **Adaptive Throttling**. The core principle is to decouple the logging frequency from the processing rate, ensuring that the system remains observable without being overwhelmed by its own diagnostics.

### 4.1 Time-Based Throttling (The "MinInterval" Approach)

The most robust parameter-free algorithm is time-based. Instead of asking "Have we processed N items?", the logger asks "Has T time passed since the last update?".

`tqdm` implements this via the `mininterval` parameter (defaulting to 0.1 seconds). The internal logic performs a check against `time.time()` compared to `last_print_time`. If the delta is less than the interval, the update is skipped. This operation is O(1) and imposes negligible overhead (approximately 60ns per iteration).  

**The Auto-Tuning "MinIters" Logic**: To further optimize, `tqdm` employs a dynamic adjustment of the check frequency itself. If the loop is extremely fast (e.g., millions of iterations per second), even calling `time.time()` every iteration introduces significant system call overhead. `tqdm` solves this by maintaining a `miniters` variable. It skips checking the time for M iterations. If the eventual check reveals that the time delta was significantly less than `mininterval`, it doubles M. If the time delta was too large, it halves M. This allows the library to "learn" the processing speed and adjust its checking frequency dynamically, satisfying the requirement for **auto-detected defaults**.  

### 4.2 The Token Bucket Algorithm for Logging

For implementing custom structured logging without relying on `tqdm`'s internals, the **Token Bucket** algorithm provides a standard, parameter-free mechanism for rate limiting. In this context, the "bucket" holds permission tokens to write a log line. Tokens are added at a fixed rate (e.g., 1 token every 5 seconds). When the code attempts to log, it must consume a token. If the bucket is empty, the log is suppressed.

Python

    import time

    class RateLimitedLogger:
        def __init__(self, interval=5.0):
            self.interval = interval
            self.last_log = 0.0

        def should_log(self):
            now = time.time()
            if now - self.last_log > self.interval:
                self.last_log = now
                return True
            return False

**Why this is SOTA**: It completely decouples logging frequency from processing speed. Whether the `populate_cache` function is churning through 10 rows per second or 1,000,000 rows per second, the logs appear exactly every 5 seconds. This guarantees log volume stability, preventing the "log flooding" that often crashes logging agents like Fluentd or Filebeat during high-throughput bursts.

### 4.3 Throughput-Damped Logging (The Variance Trigger)

A purely time-based logger has a weakness: it might mask sudden, catastrophic drops in performance that happen between intervals. To address this, we can employ **Throughput-Damped Logging**, inspired by TCP congestion control algorithms like TCP Vegas.

**Algorithm: The Variance Trigger**

1. Maintain a running Exponential Moving Average (EMA) of the throughput (rows/sec), denoted as μ.

2. Maintain a running standard deviation of the throughput, σ.

3. On each potential update, calculate the instantaneous rate Rinst​.

4. **Trigger Condition**: If Rinst​<μ−2σ (i.e., the rate has dropped by more than two standard deviations), force a log event immediately, ignoring the time interval.

This logic creates a system that is "Quiet when happy, loud when struggling." It captures "events of interest"—such as a database lock or network throttle—while silencing steady-state operations. This directly addresses the user's need for insight into "variable-rate processing".  

## 5\. ETA Estimation for Variable-Rate (Financial) Data

Financial tick data poses a specific, severe problem for ETA estimation: **The "Smile" of Volatility**. Market activity is not uniform.

- **09:30 AM - 10:30 AM**: Heavy volume (Millions of ticks/sec). Processing is slow.

- **12:00 PM - 01:00 PM**: Low volume (The "Lunch Lull"). Processing is fast.

- **03:00 PM - 04:00 PM**: Heavy volume. Processing slows again.

Standard linear interpolation—ETA\=WorkDoneTimeElapsed​×WorkRemaining—fails catastrophically here. If the job starts at 12:00 PM, the estimator sees high speed and predicts an early finish. As the job hits the 3:00 PM volume wall, the speed collapses, and the ETA extends indefinitely, frustrating the user.  

### 5.1 Exponential Moving Average (EMA)

`tqdm` attempts to mitigate this using a smoothing factor (EMA) for speed estimation.  

St​\=α⋅Yt​+(1−α)⋅St−1​

Where:

- St​ is the smoothed speed.

- Yt​ is the instantaneous speed.

- α is the smoothing factor (default 0.3).

**Critique**: EMA adapts to _recent_ speed. While better than a global average, it is reactive, not predictive. It assumes the near future resembles the immediate past. In the context of the Volatility Smile, an EMA will essentially "chase" the curve, lagging behind the trend.  

### 5.2 The Kalman Filter Approach (SOTA for Burstiness)

For true robustness, we look to control theory and the **Kalman Filter**. The Kalman filter models the processing rate not just as a value, but as a system state with position, velocity, and potentially acceleration, all subject to "noise" (burstiness).  

**The Model**:

- **State**: Current Processing Rate (R) and Acceleration (R˙).

- **Measurement**: Observed items processed in the last Δt.

- **Prediction**: The filter predicts the rate for the _next_ step based on the current velocity and acceleration.

Unlike EMA, which is a lagging indicator, a Kalman filter configured with a **Constant Acceleration Model** can detect _trends_. If the system detects a consistent slowdown (e.g., entering a high-density date range like the 2008 crash), the Kalman filter projects this deceleration forward. It effectively says, "The rate is dropping at 5% per minute; therefore, the future rate will be lower than the current rate." This results in a "Pessimistic ETA" which is operationally safer—it is better to predict a late finish and finish early than vice versa.  

**Practical Implementation**: Implementing a full matrix-based Kalman filter in Python logging code introduces unnecessary complexity (O(N3) operations). A practical approximation for ETL pipelines is **Double Exponential Smoothing (Holt-Winters)**.

Lt​\=αYt​+(1−α)(Lt−1​+Tt−1​)

Tt​\=β(Lt​−Lt−1​)+(1−β)Tt−1​

Here, Lt​ represents the Level (Speed) and Tt​ represents the Trend (Acceleration). By factoring Tt​ into the ETA calculation, the estimator becomes sensitive to the _derivative_ of the processing speed, providing a much more stable estimate for U-shaped data profiles.

### 5.3 Proxy Units: The Ultimate Simplification

An alternative, strictly parameter-free approach to handling variable density is to change the **Unit of Progress**. Instead of counting "Rows" (which vary in density), the system should count "Time" or "files" (which are constant).

- **Rows**: Variable cost. 1M rows in 2018 is faster than 1M rows in 2020.

- **Days**: Constant metadata. The dataset is exactly "2500 Trading Days."

By setting the progress bar `total` to the number of days (metadata) rather than rows (data), the visual progress bar becomes linear. The variability is shifted to the "time per iteration" metric, which users intuitively understand ("Today is a slow day"), rather than breaking the "Percentage Complete" metric. This is a highly relevant "insight" for financial ETL: **Measure Metadata, Not Data**.  

## 6\. Structured Logging and Resumable Operations

The requirement for "useful for humans (console) and machines (JSONL)" dictates a **Dual-Sink Architecture**. We must decouple the visual presentation from the audit trail.

### 6.1 The Dual-Sink Strategy with Loguru

`loguru` is the optimal library for this pattern due to its flexible sink configuration and native serialization.  

**Architecture**:

1. **Sink A (Console)**: Uses a custom sink that writes formatted strings to `stderr`, but routes them through `tqdm.write()` to prevent bar corruption.

2. **Sink B (File/Agent)**: A file sink configured with `serialize=True`. This emits valid JSONL for every log event.

**Code Pattern**:

Python

    from loguru import logger
    from tqdm import tqdm

    # Remove default handler to prevent double-printing
    logger.remove()

    # Sink 1: Human Readable, respecting TQDM's cursor
    logger.add(lambda msg: tqdm.write(msg, end=""), format="{message}", level="INFO")

    # Sink 2: Machine Readable JSONL
    logger.add("pipeline_audit.json", serialize=True, level="DEBUG")

This ensures that while the human sees "Processed Batch 50...", the machine sees: `{"text": "Processed Batch 50", "record": {"extra": {"batch_id": 50, "throughput": 500}}, "time": "..."}`.

### 6.2 Resumability: The Checkpoint-Log Duality

Resumability requires two components: **State Recovery** and **Visual Continuity**. When `populate_cache_resumable()` restarts, it must know where it left off.

**The "Initial" Parameter**: `tqdm` provides an `initial` parameter specifically for this.  

1. **Recovery**: The script queries ClickHouse: `SELECT max(timestamp) FROM cache`.

2. **Calculation**: It calculates how many "days" or "rows" correspond to that timestamp.

3. **Initialization**: `pbar = tqdm(total=total_est, initial=recovered_count)`.

**Visual Consequence**: The bar renders immediately at `[#####-----] 50%`. Crucially, the rate calculation resets (it calculates speed based on new work), but the percentage reflects global state.

**Structured Correlation**: To allow machines to stitch together multiple runs, the structured logs must include a `run_id` (unique per execution) and a `logical_batch_id` (persistent across executions).

JSON

    {"event": "batch_complete", "run_id": "uuid-1", "logical_batch_id": 45, "status": "success"}
    {"event": "batch_complete", "run_id": "uuid-2", "logical_batch_id": 46, "status": "success"}

Aggregators like Datadog can then plot `max(logical_batch_id)` to show a monotonic progress line despite the process crashing and restarting.

### 6.3 Monitoring Async ClickHouse Inserts

A critical "gotcha" in ClickHouse pipelines is the **Asynchronous Insert**. If the Python script finishes its loop and exits, ClickHouse may still have data buffered in RAM. If the container dies immediately, that data is lost.

**The "Shadow" Progress**: We must monitor `system.asynchronous_inserts`. The logging context manager must include a `__exit__` phase that polls this system table.  

Python

    # Pseudo-code for Cleanup Phase
    while True:
        pending = client.execute("SELECT count() FROM system.asynchronous_inserts")
        if pending == 0: break
        logger.info(f"Waiting for ClickHouse flush: {pending} batches pending")
        time.sleep(1)

This effectively extends the "Progress Bar" beyond the Python loop to encompass the database's internal state.

## 7\. Implementation: The Resumable Observer Pattern

Based on the research, we propose a unified class, the `ResumableObserver`, which encapsulates `tqdm`, `loguru`, and the adaptive logic.

### 7.1 The Solution Code

Python

    import time
    from tqdm import tqdm
    from loguru import logger

    class ResumableObserver:
        """
        A parameter-free, adaptive progress observer for long-running ETL.
        Wraps TQDM for console output and Loguru for JSON audit trails.
        """
        def __init__(self, iterable, total=None, initial=0, desc="Processing", log_interval=5.0):
            self.iterable = iterable
            self.total = total
            self.initial = initial
            self.log_interval = log_interval
            self.last_log_time = time.time()

            # Initialize TQDM with parameter-free defaults
            # mininterval=1.0 ensures low overhead
            self.pbar = tqdm(total=total, initial=initial, desc=desc,
                             unit="items", mininterval=1.0)

        def __iter__(self):
            """
            Generator wrapper. Intercepts yields to update progress.
            """
            for item in self.iterable:
                yield item
                self.update(1)
            self.close()

        def update(self, n=1):
            # 1. Update Console (tqdm handles smoothing/throttling internally)
            self.pbar.update(n)

            # 2. Update Structured Log (Manually throttled via Token Bucket logic)
            now = time.time()
            if now - self.last_log_time >= self.log_interval:
                self._emit_structured_log()
                self.last_log_time = now

        def _emit_structured_log(self):
            # Extract robust metrics from TQDM's internal format dict
            info = self.pbar.format_dict

            logger.info("Progress Update",
                        extra={
                            "current": info['n'],
                            "total": info['total'],
                            "percent": round((info['n'] / info['total']) * 100, 2) if info['total'] else 0,
                            "rate": info['rate'],
                            "elapsed_sec": info['elapsed'],
                            "eta_sec": info.get('remaining', 0)
                        })

        def close(self):
            self._emit_structured_log() # Ensure final state is logged
            self.pbar.close()

### 7.2 Integration Strategy

To integrate this into `populate_cache_resumable`, the user wraps their existing date range generator.

Python

    def populate_cache_resumable(start_date, end_date):
        # 1. Checkpoint Recovery (Get 'initial')
        existing_count = db.query("SELECT count() FROM target_table WHERE date >=...")

        # 2. Generator Creation
        date_ranges = generate_ranges(start_date, end_date)

        # 3. Observability Injection
        # Note: total can be estimated by (end_date - start_date).days
        observer = ResumableObserver(date_ranges, total=(end_date-start_date).days, initial=existing_count)

        for date_range in observer:
            process_data(date_range)

## 8\. Conclusion

The transition from a silent "black box" to an observable pipeline for financial tick data requires more than simply adding a progress bar. It requires a fundamental shift in how "progress" is measured and reported. By adopting the **Wrapped Iterator** pattern, we ensure non-intrusiveness and memory efficiency. By utilizing **Time-Based Throttling** (via `tqdm`'s `mininterval` and our custom Token Bucket logger), we achieve the "parameter-free" requirement, allowing the system to self-regulate across orders of magnitude in data density.

Finally, by recognizing the unique volatility of financial data, we recommend moving beyond simple row counts to **Metadata-Based Progress** (counting days/files) or utilizing **Double Exponential Smoothing** for ETA estimation. The proposed **Dual-Sink Architecture** satisfies the conflicting needs of human operators (who need visual reassurance) and machine monitors (who need structured data), ensuring that the `populate_cache_resumable()` function is robust, observable, and production-ready.

## 9\. Findings Summary Table

The following table summarizes the key research findings mapped to the specific user questions.

| Finding ID | Research Question | Solution/Insight                                                                                                                                  | Relevance | Source |
| ---------- | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | --------- | ------ |
| **F1**     | SOTA Patterns     | **Wrapped Iterator**: Use `tqdm` as a decorator. It handles generator protocols and low-level I/O efficiently.                                    | 5/5       |        |
| **F2**     | Unknown Totals    | **Indeterminate Mode**: Bars pulse instead of fill. **Proxy Units**: Recommend counting "Days" (metadata) instead of "Rows" to force determinism. | 4/5       |        |
| **F3**     | Adaptive Interval | **MinInterval**: Check `time.time()` only after M iterations; adjust M dynamically. Log based on ΔT, not ΔN.                                      | 5/5       |        |
| **F4**     | ETA Volatility    | **Holt-Winters Smoothing**: Double exponential smoothing captures trend/acceleration better than simple EMA for U-shaped volume curves.           | 4/5       |        |
| **F5**     | Resumability      | **Initial Parameter**: Initialize bar with `initial=db_count`. **Checkpointing**: Log logical batch IDs to correlate logs across restarts.        | 5/5       |        |
| **F6**     | Structured Logs   | **Dual Sink**: Use `loguru` with one sink for `stderr` (visual) and one for `serialize=True` (JSON). Route console logs via `tqdm.write`.         | 5/5       |        |
| **F7**     | ClickHouse        | **Async Monitoring**: Monitor `system.asynchronous_inserts` after loop completion to ensure data durability.                                      | 5/5       |        |

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