Imagine you have to store data whose massive influx increases by the hour. Your first priority, after making sure you can easily add storage capacity, is to try and reduce the data’s footprint to save space. But how? This is the story of Uber Engineering’s comprehensive encoding protocol and compression algorithm test and how this discipline saved space in our Schemaless datastores.


As of early 2016, many millions of trips flow through Uber’s platform every day across 400+ cities in over 60 countries on 6 continents. Between services, trip data is often passed around as JSON blobs, which typically take up 20 kilobytes (KB) a piece. Eventually, trip data is stored in Mezzanine, Uber’s Schemaless-backed datastore, for further processing, billing, and analytics. Schemaless is inherently append-only storage, so data piles up.

Let’s do the math: each million trips at 20 KB yields 20 gigabytes (GB) of trip data per day. Schemaless stores its data across many physical hosts. If Uber were not a hypergrowth company and trip growth instead expanded linearly, a single disk of 1 terabyte (TB) would last just 51 days. Subtract from that ~40% of space used by system components, and you’re down to 30 days per host. Thus an installation of 32 TB lasts  < 3 years for 1 million trips, < 1 year for 3 million trips, and < 4 months for 10 million trips—that is, if you store raw JSON.

Since JSON data lends itself very well to compression, we were convinced we could find an algorithm that could squeeze the data without sacrificing performance. Reclaiming several KB per trip across hundreds of millions of trips during the year would save us lots of space and give us room to grow.


A Matter of Protocol

JSON bridges the gap between human readable and machine parsable via a more compact syntax than SGML and XML, but it is still ultimately ASCII.  The first natural step when optimizing for space is to use a binary encoding instead, and then put a compression algorithm on top.

Encoding protocols fall into two major categories: protocols using an IDL and those that don’t. IDL-based encodings require schema definitions. They offer peace of mind with respect to data format and validation for consumers while sacrificing flexibility in the schema’s evolution. Non-IDL-based encodings are typically generic object serialization specifications, which define a compact format on top of a fixed type system. They provide a flexible serialization mechanism but only give basic validation on types. We evaluated three IDL based encoding protocols and seven non-IDL based encodings:

Encoding Protocol Schema-based (IDL)
1 Thrift Yes
2 Protocol Buffers Yes
3 Avro Yes
8 MessagePack No
9 Marshal No
10 Pickle¹ No

For compression, we put three lossless and widely accepted libraries to the test:

  1. Snappy
  2. zlib
  3. Bzip2 (BZ2)

Snappy aims to provide high speeds and reasonable compression. BZ2 trades speed for better compression, and zlib falls somewhere between them.



Our goal was to find the combination of encoding protocol and compression algorithm with the most compact result at the highest speed. We tested encoding protocol and compression algorithm combinations on 2,219 pseudorandom anonymized trips from Uber New York City (put in a text file as JSON). We wrote a test script in Python to benchmark each option (IDL files were handcrafted from trip JSON data for Thrift, Protocol Buffers, and Avro). Then, the script was put to work. Looping through all, the script measured time spent encoding/decoding, compressing/inflating, and the gain or loss in size. Here it is in pseudocode:


Comparing Results by Size and Speed

The following graphs show the results: Results_Size

For size, the combination Protocol Buffers (PROTOBUF) compressed with zlib was just slightly better than Thrift with BZ2, squeezing data to just above 8% of its uncompressed JSON equivalent. Disturbingly, storing pickled data was worse than just persisting raw JSON.


For speed, Thrift won the race by spending only 1548 ms: 23% of the 6535 ms it takes to use JSON with the native C++ backed implementation, and vice versa. The native Python Avro implementation, on the other hand, ran at 211,540 ms: more than 32 times slower than the native JSON encoder. There is a fastavro implementation, which claims to be an order of magnitude better, but it is not feature complete and thus wasn’t tested.


The Verdict

The tradeoffs of each encoding and compressing option can be evaluated against each other in a scatter diagram. The pareto front, shown as a red line on the graph, potentially gives us the most optimal solutions:


Essentially, the bottom left corner is what we were aiming for: small size and a short time to encode and decode.

Key conclusions:

  1. Simply compressing JSON with zlib would yield a reasonable tradeoff in size and speed. The result would be just a little bigger, but execution was much faster than using BZ2 on JSON.
  2. Going with IDL-based protocols, Thrift and Protocol Buffers compressed with zlib or Snappy would give us the best gain in size and/or speed.

Since JSON compressed with zlib was in fact a good candidate, we decided to measure up the remaining contenders against that baseline. So we immediately ruled out any option that fell below JSON/zlib in either speed or size. We were left with the following shortlist:

Encoder Encode (ms) Decode (ms) Size (bytes) Size Factor Speed Factor
PROTOBUF zlib 2158 925 10,885,018 46% 34%
THRIFT bz2 5531 2003 11,178,018 47% 82%
PROTOBUF bz2 5111 1738 12,023,408 51% 75%
THRIFT zlib 1817 1147 12,451,285 53% 32%
PROTOBUF Snappy 1224 790 14,694,130 62% 22%
CBOR zlib 2573 2611 18,290,630 78% 57%
MESSAGEPACK zlib 4231 715 18,312,106 78% 54%
MARSHAL zlib 2095 1416 18,567,296 79% 38%
THRIFT Snappy 628 1011 19,003,267 81% 18%
UJSON zlib 2956 1165 19,917,716 85% 45%
JSON zlib 5561 3586 23,560,699 100% 100%

Prior to Fall 2014, JSON structures passed between Uber’s services were not under strict schema enforcement. Using an IDL-based encoding protocol would require us to define IDL schemas and enforce them in Schemaless. This reduced the original list to the following contenders:

Encoder Encode (ms) Decode (ms) Size (bytes) Size Factor Speed Factor
MESSAGEPACK zlib 4231 715 18,312,106 78% 54%
CBOR zlib 2573 2611 18,290,630 78% 57%
MARSHAL zlib 2095 1416 18,567,296 79% 38%
UJSON zlib 2956 1165 19,917,716 85% 45%
JSON zlib 5561 3586 23,560,699 100% 100%

Marshal, being Python only, was out by default. JSON with zlib was bigger and slower than the rest of the pack, and while that left UJSON as the fastest candidate, the size was still a bit larger than CBOR and MessagePack. The final round of evaluation was between MessagePack and CBOR. CBOR proved to be slower, so when the scripts and judging ended, MessagePack with zlib was left standing.

MessagePack/zlib is a much better choice than using the Python JSON encoder with no compression. While encoding is slower, decoding is much faster, and the relative size is an order of magnitude better:  

Encoder Encode (ms) Decode (ms) Size (bytes) Size Factor Speed Factor
JSON 3260 3275 132,852,837 564% 71%
MESSAGEPACK zlib 4231 715 18,312,106 78% 54%


What We Learned

There is a plethora of encoding protocols out there, and plentiful compression algorithms as well. We settled on MessagePack with zlib. We felt this was the best choice for our Python-based, sharded datastore with no strict schema enforcement (Schemaless). We only discovered this combination because we took a disciplined approach to test a wide range of protocols and algorithm combinations on real data and production hardware. First lesson learned: when in doubt, invest in benchmarking.

How much did we save in this instance? Let’s do the math again, this time reducing 20 KB by 86% to get 2,822 bytes, the size gain yielded by MessagePack+zlib over raw JSON. Multiply by a million trips and the storage space only increases by just under 3 GB, compared to 20 GB without compression. A 1 TB disk will now last almost a year (347 days), compared to a month (30 days) without compression. Assuming a Schemaless installation with 32 TB capacity and linear growth, we now have enough space to last over 30 years compared to just under 1 year, thanks to putting the squeeze on the data.

Encoding and compressing data is a smart move, and like a three star Michelin restaurant, it’s worth the journey to get there. Not only does it save space; it also significantly reduces the amount of time spent processing data. In everyday operations, this translates directly to hardware, which does not have to be bought, provisioned, and configured.


¹ Marshal and Pickle are Python only. In general, it’s ill-advised to tie the persistent representation of data to any particular language, but we decided to include them in the test for reference nevertheless.


Kåre Kjelstrøm is a software engineer and engineering manager on the Schemaless project and works at the Uber Engineering office in Aarhus, Denmark.

Photo Credits for Header: “Boa constrictor, Atlantic forest, northeastern Bahia, Brazil” by Alex Popovkin, Bahia, Brazil licensed under CC-BY 2.0. Image cropped for header dimensions and color corrected.

Header Explanation: Boa constrictors immobilize and incapacitate prey by putting the squeeze on them with pressures of over 100 kPa (the same order of magnitude of a champagne bottle popping.)

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