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NoSQL databases like Cassandra have exploded in popularity in recent years.
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One driver is the insatiable demand to ingest huge amounts of
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data from an ever-growing number of sources like mobile application and IoT devices.
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The secret sauce behind many of these NoSQL databases is a data structure called Log Structure Merge Tree.
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An LSM tree is optimized for fast writes.
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To understand how an LSM tree works, let's first take a look at how data is typically stored in a relational database.
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A relational database is commonly backed by a data structure called a B-tree.
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A B-tree is optimized for reads.
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Updating the B-tree is relatively expensive as it involves random I.O and might include updating multiple pages on disk.
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This limits how fast a B tree can ingest data.
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An LSM tree works differently.
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Writes are bashed in memory as they arrive in a structure called a mem table.
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A mem table is ordered by object key and is usually implemented as a balanced binary tree.
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As a mem table reaches a certain size, it is flushed to disk as an immutable sorted string table.
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An ss table stores the key value pairs in a sorted sequence.
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These writes are all sequential I.O.
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They are fast on any storage media.
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The new ss table becomes the most recent segment of the LSM tree.
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As more data comes in, more and more of these immutable SS tables are created and added to the LSM tree,
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with each one representing a small chronological subset of the incoming changes.
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Since SS tables are immutable, an update to an existing object key does not override an existing table.
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Instead, a new entry is added to the most recent SS table, which supersedes any entries in the old SS tables for the object key.
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Deleting an object requires special handling too, since we cannot mark anything in the SS table as deleted.
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To perform a delete, it adds a marker called a tombstone to the most recent SS table for the object key.
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When we encounter the tombstone on read, we know the object has been deleted.
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And yes, it is a bit unintuitive that a delete takes up extra space.
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To serve a read request, we first try to find the key in the mem table, then in the most recent SS table in the LSM tree,
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then the next SS table, and so on.
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Since an SS table is sorted, the lookup can be done efficiently.
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The accumulation of SS tables presents two issues.
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As the number of SSTables grows, it will take an increasingly long time to look up a key.
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And as the SSTables accumulate, there are more and more outdated entries as keys are updated and tombstones are added.
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These take up precious disk space.
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To fix these issues, there is a periodic merging and compaction process running in the background
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to merge SSTables and discard outdated or deleted values.
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This reclaims this space and caps the number of SSTables a read has to look through.
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Since the tables are sorted, this merging and compaction process is simple and efficient.
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The approach is similar to the one used in the merge phase of the merge sort algorithm.
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This, in a nutshell, is how an LSM tree provides fast writes.
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Let's recap.
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An LSM tree buffers incoming writes in memory.
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When the buffer fills up, we sort and flush it to disk as an immutable SS table.
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The number of SS tables increases as more buffers get flushed to disk.
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This creates a problem for reads, as each read has to search through these SS tables to perform a lookup.
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To cap the number of SS tables it has to search through for each read, the LSM tree merges the SSTables and compacts them in the background.
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Let's look at the compaction process more closely.
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When the SSTables are merged, they are organized into levels.
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This is where the tree part of the LSM tree comes into the picture.
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There are different strategies to determine when and how the SSTables are merged and compacted.
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There are two broad strategies, size-tiered compaction and leveled compaction.
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Size tier compaction is optimized for write throughput.
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Level compaction is more read optimized.
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We won't go into too much detail on how these compaction strategies work.
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There are a few key points to remember though.
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Compaction keeps the number of SS tables manageable.
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The SS tables are organized into levels, with each level gets exponentially larger as SS tables from the level above emerged into it.
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Compaction consumes a lot of I.O.
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A mistuned compaction could starve the system and slow down both read and write.
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Finally, let's visit some common optimizations for the LSM trees in production systems.
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There are many optimization strategies that try to provide read performance closer to that of the B tree.
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What we explain below are just the common ones, and they are by no means exhaustive.
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To look up a key, it performs searches on the SS tables at every level.
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Even though search is fast on sorted data, going through all the on-disk SS tables consumes a lot of I.O.
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Many systems keep a summary table in memory that contains the min-max range of each disk block of every level.
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It allows the system to skip searches on those disk blocks where the key does not fall within the range.
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This saves a lot of random I.O.
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Another issue that could be potentially expensive is to look up a key that does not exist.
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This would require looking through all the eligible blocks at all the levels.
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Most systems keep a bloom filter at each level.
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A bloom filter is a space-efficient data structure that returns a firm no if a key does not exist, or a probably yes if a key might exist.
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This allows the system to skip a level entirely if the key does not exist there, which dramatically reduces the number of random I.O required.
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In conclusion, NoSQL databases backed by an LSM tree could be tuned to support a very high write rate.
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As with any database, proper tuning is the key.
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For an LSM tree, compaction tuning is the most critical.
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If you would like to learn more about system design, check out our books and weekly newsletter.
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Please subscribe if you learn something new.
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Thank you so much and we'll see you next time.
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Vocabolario e note di pronuncia per questa lezione
Questa lezione di conversazione di livello C1 si basa sul video “The Secret Sauce Behind NoSQL: LSM Tree”. Le parole che ritornano più spesso: table, tree, key, Lsm, compaction. Questo video contiene 77 frasi e 1089 parole da ripetere con lo shadowing. Il parlato dura 7:26. Chi parla mantiene un ritmo costante di circa 147 parole al minuto, comodo per lo shadowing. Solo il 72% delle parole rientra nelle 3.000 più comuni dell’inglese, quindi il lessico è impegnativo.
Vocaboli chiave di questo video
Le 15 parole più avanzate del video, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| merge verbo | /mɝd͡ʒ/ | fondersi, unirsi |
| disk sostantivo | /dɪsk/ | disco |
| immutable aggettivo | immutabile | |
| optimize verbo | /ˈɑptɪmaɪz/ | ottimizzare |
| tombstone sostantivo | /ˈtuːmstoʊn/ | lapide, pietra tombale |
| buffer sostantivo | /ˈbʌfəɹ/ | soluzione tampone |
| flush verbo | /ˈflʌʃ/ | irrorare |
| ingest verbo | /ɪnˈd͡ʒɛst/ | ingerire |
| outdated aggettivo | /aʊtˈdeɪtɪd/ | obsoleto, antiquato |
| optimization sostantivo | /ˌɑptəmaɪˈzeɪʃən/ | ottimizzazione |
| consume verbo | /kənˈsum/ | consumare |
| incoming aggettivo | /ˈɪnˌkʌmɪŋ/ | prossimo, in arrivo |
| bloom sostantivo | /blum/ | fioritura |
| organize verbo | /ˈɔɹɡənaɪz/ | organizzare |
| compact aggettivo | /kəmˈpækt/ | compatto |
I phrasal verb che sentirai
| Parola | Significato |
|---|---|
| take up verbo | sollevare, prendere |
| check out verbo | investigare |
| fill up verbo | riempire |
| go through verbo | attraversare |
| slow down verbo | rallentare, decelerare |
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | is optimized · is typically stored · is commonly backed |
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | have exploded · has been deleted |
Pronuncia a cui fare attenzione
- I suoni “th”: throughput /ˈθɹuː.pʊt/, algorithm /ˈælɡəɹɪðm̩/
- I suoni “sh” e “zh”: compaction /kəmˈpækʃən/, flush /ˈflʌʃ/, optimization /ˌɑptəmaɪˈzeɪʃən/, exponentially /ɛkspə(ʊ)ˈnɛnʃəli/, insatiable /ɪnˈseɪʃ(j)əbəl/
- Parole lunghe — attenzione all’accento: optimization /ˌɑptəmaɪˈzeɪʃən/, accumulate /əˈkjuːmjʊˌleɪt/, chronological /ˌkɹɑnəˈlɑd͡ʒɪkəl/, exponentially /ɛkspə(ʊ)ˈnɛnʃəli/, insatiable /ɪnˈseɪʃ(j)əbəl/
I suoni difficili per chi parla italiano:
- Consonante finale — senza aggiungere una vocale dopo: merge /mɝd͡ʒ/, disk /dɪsk/, optimize /ˈɑptɪmaɪz/, flush /ˈflʌʃ/, ingest /ɪnˈd͡ʒɛst/
- /æ/ — più aperta della “e”: compaction /kəmˈpækʃən/, compact /kəmˈpækt/, manageable /ˈmænəd͡ʒəb(ə)l/, bash /bæʃ/, algorithm /ˈælɡəɹɪðm̩/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Fai shadowing frase per frase a velocità normale, ripetendo ognuna finché il tuo ritmo coincide con quello di chi parla.
- Registrati e confronta con l’originale, facendo attenzione a parole come merge, disk, immutable.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.