I've tried to repro the issue with 0.1.38 with main as of today, but failed so far. Given that the data store has been entirely rewritten with chunks, this is not entirely surprising that we don't trigger the leak in the same way.

For record, here is my attempt setup:

Viewer launched with:

RERUN_CHUNK_MAX_ROWS=1 cargo r -p rerun-cli --no-default-features --features native_viewer --release -- --memory-limit 256MB

This python script, executed with `RERUN_FLUSH_NUM_ROWS=0`

# %%
from numpy.typing import NDArray
import numpy as np
import rerun as rr


# %%
def gen_color_list(num_colors: int):
    """
    Generates a list of random RGB color values.

    Args:
        num_colors (int): The number of colors to generate.

    Returns:
        list: A list of RGB color values, where each value is a list of three integers between 0 and 255.
    """
    color_list = []
    for _ in range(num_colors):
        r = np.random.randint(0, 256)
        g = np.random.randint(0, 256)
        b = np.random.randint(0, 256)
        color_list.append([r, g, b])
    return color_list


def scale_tsx(tsy: NDArray, ch_count: int) -> NDArray:
    """
    Scales a time series array by adding a constant value to each channel.

    Args:
        tsy (NDArray): _description_
        ch_count (int): _description_

    Returns:
        NDArray: _description_
    """
    scale_factor = tsy.max() + (tsy.std() * 2)
    channel_scale_factor = np.arange(ch_count) * scale_factor
    tsx_scaled = tsy.T + channel_scale_factor.reshape(-1, 1)
    return tsx_scaled


# %%
# mock data

# Define sampling parameters
num_channels = 16  # Number of channels
sample_rate = 24000  # Sampling rate in Hz
duration_min = 30  # Duration in minutes

# Calculate total number of samples
total_samples = sample_rate * 60 * duration_min

# Generate random samples for each channel
random_samples = np.random.uniform(-1.0, 1.0, (total_samples, num_channels)).astype(
    np.float32
)

# Print the shape of the generated random samples array
print("Shape of random samples array:", random_samples.shape)
# %%
traces_scaled = scale_tsx(random_samples[:, :], 16)
ch_colors = gen_color_list(16)
# %%
rr.version()
rr.init("testSubject2", spawn=True)

for ch_id in np.arange(16):
    rr.log(
        f"mockdata/ch{ch_id}",
        rr.SeriesLine(color=ch_colors[ch_id], name=f"ch{ch_id}", width=0.5),
        timeless=True,
    )
# %%
# Log the data on a timeline called "step".
for t in range(0, traces_scaled.shape[1]):
    rr.set_time_sequence("step", t)
    for ch_id in np.arange(16):
        rr.log(f"mockdata/ch{ch_id}", rr.Scalar(traces_scaled[ch_id, t]))
# %%

Example run with 0.1.38 (appears stable over time):

Investigate mimalloc leaks starting with 0.1.38 #5875

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Investigate `mimalloc` leaks starting with 0.1.38 #5875