Mahler: Symphony No. 2 in C Minor "Resurrection": V. (g) Mit Aufschwung aber nicht eilen. "O Schmerz!"

Gustav Mahler, Sir Simon Rattle, Berliner Philharmoniker, Kate Royal, Magdalena Kožená, Rundfunkchor Berlin

The song Mahler: Symphony No. 2 in C Minor "Resurrection": V. (g) Mit Aufschwung aber nicht eilen. "O Schmerz!" by Gustav Mahler, Sir Simon Rattle, Berliner Philharmoniker, Kate Royal, Magdalena Kožená, Rundfunkchor Berlin has been played 417,838 times on Spotify since its release on January 01, 2010.

Other songs from Gustav Mahler, Sir Simon Rattle, Berliner Philharmoniker, Kate Royal, Magdalena Kožená, Rundfunkchor Berlin

Barber: Adagio for Strings

Samuel Barber, Sir Simon Rattle, Berliner Philharmoniker
Piano Concerto No. 1 in B-Flat Minor, Op. 23: II. Andantino semplice – Prestissimo – Tempo I - Live at Philharmonie, Berlin

Pyotr Ilyich Tchaikovsky, Evgeny Kissin, Berliner Philharmoniker, Herbert von Karajan
Rachmaninov: Piano Concerto No. 2 in C Minor, Op. 18: II. Adagio sostenuto (Excerpt)

Sergei Rachmaninoff, Cecile Ousset, Sir Simon Rattle, City Of Birmingham Symphony Orchestra
Meeting Laura

Johnny Klimek, Reinhold Heil, Tom Tykwer, Sir Simon Rattle, Berliner Philharmoniker, Chen Reiss
Ravel: Boléro, M. 81

Maurice Ravel, Sir Simon Rattle, City Of Birmingham Symphony Orchestra

Frequently asked questions

: Due to Spotify's policy of not publicly sharing daily stream counts, our system independently records this data on a daily basis for millions of tracks. Given the vast number of tracks we monitor, there may be occasions where certain tracks are not processed on specific days, leading to missing data points. Rest assured, we continuously work to minimize these gaps and provide comprehensive coverage.
: There are two primary reasons you might notice irregularities in the data points:

- Inconsistent Stream Counts for a Day: Sometimes, a data point may represent the stream count for two days instead of one. This inconsistency arises because Spotify does not update its public stream count at a fixed time daily. If our data recording aligns with these fluctuations, it can lead to inaccuracies.
- Anomalous Data Points (including negative streams): On occasion, you might observe highly irregular data points, such as negative daily streams. These are typically the result of metadata discrepancies on Spotify's end, like different tracks sharing the same International Standard Recording Code (ISRC). In such cases, reaching out to us can be very helpful. We can often disregard these erroneous data points to present a more accurate graph.

In both situations, we're committed to continuously improving our data accuracy and are here to assist with any specific concerns you might have.
: The extent of our historical data varies depending on the track. For some tracks, our records go as far back as September 2023. However, there are tracks for which we may not have any historical data yet. But here's the good news: once you look up a specific track on our system, you activate our tracking for that track. From that point forward, we will diligently record its data daily, ensuring you have continuous and up-to-date insights from the moment you start using our service.
: Currently, our platform does not provide a direct option to export data. However, we understand the importance of this feature, and we're excited to announce that a CSV export functionality is on our roadmap and will be introduced soon. For developers seeking more integrated solutions, we offer an API service. If you're interested in accessing our API for your specific use case, please feel free to reach out to us. We're always eager to collaborate and support your unique needs!
: This occurs due to Spotify's policy of only displaying public stream counts for tracks that have surpassed 1,000 streams. Consequently, if you see a stream count marked as 0 in our system, it indicates that the total stream count for that track is currently below 1,000. As the track accumulates more streams and crosses this threshold, the accurate stream count will start being reflected in our data.